Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA

UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works

Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum

Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613 · see citing works

Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738 · see citing works

Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update

Kramer, C. M., Barkhausen, J., Bucciarelli‐Ducci, C., Flamm, S. D., Kim, R. J., & Nagel, E. C. (2020) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-020-00607-1 · Cited by 1,264 · see citing works

Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: a report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging

Plana, J. C., Galderisi, M., Barac, A., Ewer, M. S., Ky, B., Scherrer‐Crosbie, M., Gáname, J., Sebag, I. A., Agler, D. A., Badano, L. P., et al. (2014) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeu192 · Cited by 1,062 · see citing works

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

Ali, F., El–Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A., Imran, M. A., & Kwak, K.-S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.008 · Cited by 840 · see citing works

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., Fries, J. A., Wornow, M., Swaminathan, A., Lehmann, L. S., et al. (2024) · doi:10.1101/2024.04.15.24305869 · Cited by 738 · see citing works

Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016

Smith‐Bindman, R., Kwan, M. L., Marlow, E. C., Theis, M. K., Bolch, W. E., Cheng, S. Y., Bowles, E. J. A., Duncan, J. R., Greenlee, R. T., Kushi, L. H., et al. (2019) · JAMA · doi:10.1001/jama.2019.11456 · Cited by 711 · see citing works

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

Motwani, M., Dey, D., Berman, D. S., Germano, G., Achenbach, S., Al-Mallah, M. H., Andreini, D., Budoff, M. J., Cademartiri, F., Callister, T. Q., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw188 · Cited by 692 · see citing works

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00063-2 · Cited by 611 · see citing works

Aligning artificial intelligence with climate change mitigation

Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01377-7 · Cited by 570 · see citing works

The carbon impact of artificial intelligence

Dhar, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0219-9 · Cited by 509 · see citing works

Health system-scale language models are all-purpose prediction engines

Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06160-y · Cited by 460 · see citing works

External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review

Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 455 · see citing works

1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease

Douglas, P. S., Bruyne, B. D., Pontone, G., Patel, M. R., Nørgaard, B. L., Byrne, R. A., Curzen, N. P., Purcell, I. F., Gutberlet, M., Rioufol, G., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.05.057 · Cited by 446 · see citing works

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

Koetzier, L. R., Mastrodicasa, D., Szczykutowicz, T. P., van der Werf, N. R., Wang, A., Sandfort, V., van der Molen, A. J., Fleischmann, D., & Willemink, M. J. (2023) · Radiology · doi:10.1148/radiol.221257 · Cited by 433 · see citing works

Almanac — Retrieval-Augmented Language Models for Clinical Medicine

Zakka, C., Shad, R., Chaurasia, A., Dalal, A. R., Kim, J. L., Moor, M., Fong, R., Phillips, C., Alexander, K., Ashley, E., et al. (2024) · NEJM AI · doi:10.1056/aioa2300068 · Cited by 424 · see citing works

Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

Coenen, A., Kim, Y., Kruk, M. B. P., Tesche, C., Geer, J. D., Kurata, A., Lubbers, M. M., Daemen, J., Lucian Mihai, I. T. U., Rapaka, S., et al. (2018) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.117.007217 · Cited by 404 · see citing works

Large Language Models in Medicine: The Potentials and Pitfalls

Omiye, J. A., Gui, H., Rezaei, S. J., Zou, J., & Daneshjou, R. (2024) · Annals of Internal Medicine · doi:10.7326/m23-2772 · Cited by 394 · see citing works

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Borondy Kitts, A., Birch, J., Shields, W. F., et al. (2019) · Radiology · doi:10.1148/radiol.2019191586 · Cited by 388 · see citing works

Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications

Bhayana, R. (2024) · Radiology · doi:10.1148/radiol.232756 · Cited by 340 · see citing works

Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019) · Radiology · doi:10.1148/radiol.2018180921 · Cited by 303 · see citing works

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

Lin, D. J., Johnson, P. M., Knöll, F., & Lui, Y. W. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27078 · Cited by 295 · see citing works

Prospective study design and data analysis in UK Biobank

Allen, N. E., Lacey, B., Lawlor, D. A., Pell, J. P., Gallacher, J., Smeeth, L. L., Elliott, P., Matthews, P. M., Lyons, R. A., Whetton, A. D., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adf4428 · Cited by 289 · see citing works

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Leiner, T., Rueckert, D., Suinesiaputra, A., Baeßler, B., Nezafat, R., Išgum, I., & Young, A. A. (2019) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-019-0575-y · Cited by 284 · see citing works

Technology readiness levels for machine learning systems

Lavin, A., Lee, C. M., Visnjic, A., Ganju, S., Newman, D. J., Ganguly, S., Lange, D., Baydin, A. G., Sharma, A., Gibson, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33128-9 · Cited by 247 · see citing works

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols

van Velzen, S. G. M., Lessmann, N., Velthuis, B. K., Bank, I. E. M., van den Bongard, D. H. J. G., Leiner, T., de Jong, P. A., Veldhuis, W. B., Correa, A., Terry, J. G., et al. (2020) · Radiology · doi:10.1148/radiol.2020191621 · Cited by 244 · see citing works

Noninvasive FFR Derived From Coronary CT Angiography

Lu, M. T., Ferencik, M., Roberts, R., Lee, K. L., Ivanov, A. V., Adami, E. C., Mark, D. B., Jaffer, F. A., Leipsic, J. A., Douglas, P. S., et al. (2017) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2016.11.024 · Cited by 241 · see citing works

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions

Küstner, T., Fuin, N., Hammernik, K., Bustin, A., Qi, H., Hajhosseiny, R., Masci, P. G., Neji, R., Rueckert, D., Botnar, R. M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-70551-8 · Cited by 236 · see citing works

Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification

Boogers, M. J., Broersen, A., van Velzen, J. E., de Graaf, F. R., El-Naggar, H. M., Kitslaar, P. H., Dijkstra, J., Delgado, V., Boersma, E. H., Roos, A., et al. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehr465 · Cited by 233 · see citing works

Automated Three-dimensional Quantification of Noncalcified Coronary Plaque from Coronary CT Angiography: Comparison with Intravascular US

Dey, D., Schepis, T., Marwan, M., Slomka, P. J., Berman, D. S., & Achenbach, S. S. (2010) · Radiology · doi:10.1148/radiol.10100681 · Cited by 225 · see citing works

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

Zhao, J., Feng, Q., Wu, P., Lupu, R. A., Wilke, R. A., Wells, Q. S., Denny, J. C., & Wei, W. (2019) · Scientific Reports · doi:10.1038/s41598-018-36745-x · Cited by 208 · see citing works

Automated coronary calcium scoring using deep learning with multicenter external validation

Eng, D. K.-M., Chute, C., Khandwala, N., Rajpurkar, P., Long, J., Shleifer, S., Khalaf, M. H., Sandhu, A. T., Rodríguez, F., Maron, D. J., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00460-1 · Cited by 185 · see citing works

Fully Automated, Quality-Controlled Cardiac Analysis From CMR

Ruijsink, B., Anton, E. P., Öksüz, İ., Sinclair, M. D., Bai, W., Schnabel, J. A., Razavi, R., & King, A. P. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.05.030 · Cited by 185 · see citing works

Association of Statin Treatment With Progression of Coronary Atherosclerotic Plaque Composition.

van Rosendael, A. V., van den Hoogen, I. J., Gianni, U., Ma, X.-Y., Tantawy, S., Bax, A., Lu, Y., Andreini, D., Al-Mallah, M., Budoff, M., et al. (2021) · JAMA cardiology · doi:10.1001/jamacardio.2021.3055 · Cited by 175 · see citing works

Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project)

Sandhu, A. T., Rodriguez, F., Ngo, S., Patel, B. N., Mastrodicasa, D., Eng, D., Khandwala, N., Balla, S., Sousa, D., & Maron, D. J. (2023) · Circulation · doi:10.1161/circulationaha.122.062746 · Cited by 174 · see citing works

Pericoronary Adipose Tissue Attenuation, Low-Attenuation Plaque Burden, and 5-Year Risk of Myocardial Infarction

Tzolos, E., Williams, M. C., McElhinney, P. A., Lin, A., Grodecki, K., Tomasino, G. F., Cadet, S. J., Kwieciński, J., Doris, M. K., Adamson, P. D., et al. (2022) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2022.02.004 · Cited by 172 · see citing works

Interventions to reduce wait times for primary care appointments: a systematic review

Ansell, D. R., Crispo, J. A. G., Simard, B., & Bjerre, L. M. (2017) · BMC Health Services Research · doi:10.1186/s12913-017-2219-y · Cited by 169 · see citing works

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy

Benz, D. C., Benetos, G., Rampidis, G. P., von Felten, E., Bakula, A., Šustar, A., Kudura, K., Messerli, M., Fuchs, T. A., Gebhard, C., et al. (2020) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2020.01.002 · Cited by 169 · see citing works

Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction

Sandino, C. M., Lai, P., Vasanawala, S. S., & Cheng, J. Y. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28420 · Cited by 159 · see citing works

On Hallucinations in Tomographic Image Reconstruction

Bhadra, S., Kelkar, V. A., Brooks, F. J., & Anastasio, M. A. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3077857 · Cited by 158 · see citing works

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD

Nurmohamed, N. S., Bom, M. J., Jukema, R., de Groot, R. J., Driessen, R. S., van Diemen, P. A., de Winter, R. W., Gaillard, E. L., Sprengers, R. W., Stroes, E. S. G., et al. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.05.020 · Cited by 158 · see citing works

CAD-RADS™ 2.0 – 2022 Coronary Artery Disease – Reporting and Data System An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI)

Cury, R. C., Leipsic, J. A., Abbara, S., Achenbach, S. S., Berman, D. S., Bittencourt, M. S., Budoff, M. J., Chinnaiyan, K. M., Choi, A., Ghoshhajra, B. B., et al. (2022) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.220183 · Cited by 153 · see citing works

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

Chamberlin, J., Kocher, M., Waltz, J. T., Snoddy, M., Stringer, N., Stephenson, J., Sahbaee, P., Sharma, P. K., Rapaka, S., Schoepf, U. J., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01928-3 · Cited by 150 · see citing works

The Extent of Left Ventricular Scar Quantified by Late Gadolinium Enhancement MRI Is Associated With Spontaneous Ventricular Arrhythmias in Patients With Coronary Artery Disease and Implantable Cardioverter-Defibrillators

Scott, P. A., Morgan, J. M., Carroll, N., Murday, D. C., Roberts, P. R., Peebles, C., Harden, S. P., & Curzen, N. P. (2011) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.110.959544 · Cited by 145 · see citing works

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Zhang, Q., Burrage, M. K., Lukaschuk, E., Shanmuganathan, M., Popescu, I. A., Nikolaidou, C., Mills, R., Werys, K., Hann, E., Barutcu, A., et al. (2021) · Circulation · doi:10.1161/circulationaha.121.054432 · Cited by 136 · see citing works

Opportunistic Screening: Radiology Scientific Expert Panel

Pickhardt, P. J., Summers, R. M., Garrett, J. W., Krishnaraj, A., Agarwal, S., Dreyer, K. J., & Nicola, G. N. (2023) · Radiology · doi:10.1148/radiol.222044 · Cited by 134 · see citing works

“Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation

Banerjee, I., Bhattacharjee, K., Burns, J. L., Trivedi, H., Purkayastha, S., Seyyed-Kalantari, L., Patel, B. N., Shiradkar, R., & Gichoya, J. W. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.06.025 · Cited by 131 · see citing works

Innovative Imaging Methods in Heart Failure: A Shifting Paradigm in Cardiac Assessment. Position Statement on Behalf of the Heart Failure Association of the European Society of Cardiology

Čelutkienė, J., Plymen, C. M., Flachskampf, F. A., de Boer, R. A., Grapsa, J., Manka, R., Anderson, L., Garbi, M., Barberis, V. I., Filardi, P. P., et al. (2018) · European Journal of Heart Failure · doi:10.1002/ejhf.1330 · Cited by 127 · see citing works

Environmental Sustainability and AI in Radiology: A Double-Edged Sword

Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 124 · see citing works

Environmental Sustainability and MRI : Challenges, Opportunities, and a Call for Action

Chaban, Y. V., Vosshenrich, J., McKee, H., Gunasekaran, S., Brown, M. J., Atalay, M. K., Heye, T. J., Markl, M., Woolen, S. A., Simonetti, O. P., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28994 · Cited by 122 · see citing works

Myocardial Fibrosis as a Predictor of Sudden Death in Patients With Coronary Artery Disease

Zegard, A., Okafor, O., de Bono, J. P., Kalla, M., Lencioni, M., Marshall, H. J., Hudsmith, L., Qiu, T., Steeds, R. P., Stegemann, B., et al. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.10.046 · Cited by 116 · see citing works

Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods

Li, P., Hu, Y., & Liu, Z.-P. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102474 · Cited by 116 · see citing works

Incremental prognostic value of quantitative plaque assessment in coronary CT angiography during 5 years of follow up

Nadjiri, J., Hausleiter, J., Jähnichen, C., Will, A., Hendrich, E., Martinoff, S. A., & Hadamitzky, M. (2016) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2016.01.007 · Cited by 115 · see citing works

Climate Change and Radiology: Impetus for Change and a Toolkit for Action

Brown, M. J., Schoen, J. H., Gross, J., Omary, R. A., & Hanneman, K. A. (2023) · Radiology · doi:10.1148/radiol.230229 · Cited by 114 · see citing works

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning–Based Virtual Native Enhancement

Zhang, Q., Burrage, M. K., Shanmuganathan, M., Gonzales, R. A., Lukaschuk, E. I., Thomas, K. E. H., Mills, R., Pelado, J. L., Nikolaidou, C., Popescu, I. A., et al. (2022) · Circulation · doi:10.1161/circulationaha.122.060137 · Cited by 112 · see citing works

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record

Harvey, H. B., Catherine, L., Ai, J., Jaworsky, C., Guerrier, C. E., Flores, E. J., & Pianykh, O. S. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.05.007 · Cited by 111 · see citing works

Time to wait: a systematic review of strategies that affect out-patient waiting times

Naiker, U., FitzGerald, G., Dulhunty, J. M., & Rosemann, M. (2017) · Australian Health Review · doi:10.1071/ah16275 · Cited by 109 · see citing works

Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

Muscogiuri, G., Chiesa, M., Trotta, M., Gatti, M., Palmisano, V., Dell’Aversana, S., Baessato, F., Cavaliere, A., Cicala, G., Loffreno, A., et al. (2019) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.12.001 · Cited by 105 · see citing works

Optimization of Radiology Workflow with Artificial Intelligence

Ranschaert, E. R., Topff, L., & Pianykh, O. S. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.06.006 · Cited by 103 · see citing works

Characterizing the clinical adoption of medical AI devices through U.S. insurance claims

Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103 · see citing works

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries

Föllmer, B., Williams, M. C., Dey, D., Arbab‐Zadeh, A., Maurovich‐Horvat, P., Volleberg, R. H. J. A., Rueckert, D., Schnabel, J. A., Newby, D. E., Dweck, M. R., et al. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-023-00900-3 · Cited by 102 · see citing works

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Bizzo, B. C., de Almeida, R. R., Michalski, M. H., & Alkasab, T. K. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.010 · Cited by 100 · see citing works

Quantitative assessment of atherosclerotic plaque, recent progress and current limitations

Williams, M. C., Earls, J. P., & Hecht, H. S. (2021) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2021.07.001 · Cited by 99 · see citing works

Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource

Raisi‐Estabragh, Z., Harvey, N. C., Neubauer, S., & Petersen, S. E. (2020) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaa297 · Cited by 98 · see citing works

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

Davies, R. H., Augusto, J. B., Bhuva, A., Xue, H., Treibel, T. A., Ye, Y., Hughes, R. K., Bai, W., Lau, C. W. Z., Shiwani, H., et al. (2022) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-022-00846-4 · Cited by 97 · see citing works

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study

Fahmy, A. S., Neisius, U., Chan, R. H. M., Rowin, E. J., Manning, W. J., Maron, M. S., & Nezafat, R. (2019) · Radiology · doi:10.1148/radiol.2019190737 · Cited by 96 · see citing works

Cardiac Imaging Trends from 2010 to 2019 in the Medicare Population

Reeves, R. A., Halpern, E. J., & Rao, V. M. (2021) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2021210156 · Cited by 96 · see citing works

Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics

Faghani, S., Khosravi, B., Zhang, K., Moassefi, M., Jagtap, J. M. M., Nugen, F., Vahdati, S., Kuanar, S. P., Rassoulinejad-Mousavi, S. M., Singh, Y., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220061 · Cited by 95 · see citing works

Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement

Kalra, A., Chakraborty, A. K., Fine, B., & Reicher, J. J. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.03.012 · Cited by 94 · see citing works

Socioeconomic and Demographic Predictors of Missed Opportunities to Provide Advanced Imaging Services

Glover, M., Daye, D., Khalilzadeh, O., Pianykh, O. S., Rosenthal, D. I., Brink, J. A., & Flores, E. J. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.05.015 · Cited by 93 · see citing works

Precise and Automatic Patient Positioning in Computed Tomography

Saltybaeva, N., Schmidt, B. T., Wimmer, A., Flohr, T. G., & Alkadhi, H. (2018) · Investigative Radiology · doi:10.1097/rli.0000000000000482 · Cited by 92 · see citing works

Atherosclerotic Plaque Tissue: Noninvasive Quantitative Assessment of Characteristics with Software-aided Measurements from Conventional CT Angiography

Sheahan, M. G., Ma, X., Paik, D. S., Obuchowski, N. A., Pierre, S. S., Newman, W. P., Rae, G., Perlman, E. S., Rosol, M. S., Keith, J. C., et al. (2017) · Radiology · doi:10.1148/radiol.2017170127 · Cited by 92 · see citing works

Machine Learning for Predicting Patient Wait Times and Appointment Delays

Curtis, C., Liu, C., Bollerman, T. J., & Pianykh, O. S. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.021 · Cited by 90 · see citing works

Age- and Sex-Specific Nomographic CT Quantitative Plaque Data From a Large International Cohort

Τζίμας, Γ., Gulsin, G. S., Everett, R. J., Akodad, M., Meier, D. J., Sewnarain, K., Ally, Z., Alnamasy, R., Ng, N., Mullen, S., et al. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.05.011 · Cited by 90 · see citing works

Improving healthcare operations management with machine learning

Pianykh, O. S., Guitron, S., Parke, D., Zhang, C., Pandharipande, P., Brink, J., & Rosenthal, D. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0176-3 · Cited by 87 · see citing works

Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review

Suri, J. S., Bhagawati, M., Paul, S., Protogeron, A., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A., Saxena, S., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105204 · Cited by 86 · see citing works

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation

Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.859310 · Cited by 85 · see citing works

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Guillon, L. L., Camirand, D., Guilbert, F., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.008 · Cited by 84 · see citing works

Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT

Topff, L., Ranschaert, E. R., Rutten, A., Negoita, A. N., de Menezes, R. X., Beets‐Tan, R. G. H., & Visser, J. J. (2023) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.220163 · Cited by 84 · see citing works

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230513 · Cited by 80 · see citing works

ESR paper on structured reporting in radiology—update 2023

European Society of Radiology (ESR), dos Santos, D. P., Kotter, E., Mildenberger, P., & Martí‐Bonmatí, L. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01560-0 · Cited by 77 · see citing works

Planetary Health and Radiology: Why We Should Care and What We Can Do

McKee, H., Brown, M. J., Kim, H. H. R., Doo, F. X., Panet, H., Rockall, A., Omary, R. A., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.240219 · Cited by 77 · see citing works

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study

van Assen, M., Martin, S. S., Varga‐Szemes, Á., Rapaka, S., Çimen, S., Sharma, P., Sahbaee, P., Cecco, C. N. D., Vliegenthart, R., Leonard, T. J., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109428 · Cited by 75 · see citing works

Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults

Zucker, E. J., Sandino, C. M., Kino, A., Lai, P., & Vasanawala, S. S. (2021) · Radiology · doi:10.1148/radiol.2021202624 · Cited by 74 · see citing works

Body Composition, Coronary Microvascular Dysfunction, and Future Risk of Cardiovascular Events Including Heart Failure

de Souza, A. C. D. A. H., Rosenthal, M. H., Moura, F. A., Divakaran, S., Osborne, M. T., Hainer, J. M., Dorbala, S., Blankstein, R., Carli, M. F. D., & Taqueti, V. R. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.07.014 · Cited by 72 · see citing works

AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry

Ghanbari, F., Joyce, T., Lorenzoni, V., Guaricci, A. I., Pavon, A. G., Fusini, L., Andreini, D., Rabbat, M., Aquaro, G. D., Abete, R., et al. (2023) · Radiology · doi:10.1148/radiol.222239 · Cited by 72 · see citing works

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman, K., Playford, D., Dey, D., van Assen, M., Mastrodicasa, D., Cook, T. S., Gichoya, J. W., Williamson, E. E., Rubin, G. D., & on behalf of the American Heart Association Council on Cardiovascular Radiology and Intervention; and Council on Lifelong Congenital Heart Disease and Heart Health in the Young (2024) · Circulation · doi:10.1161/cir.0000000000001202 · Cited by 72 · see citing works

Coronary CT Angiography: Variability of CT Scanners and Readers in Measurement of Plaque Volume

Symons, R., Morris, J. Z., Wu, C. O., Pourmorteza, A., Ahlman, M. A., Lima, J. A. C., Chen, M. Y., Mallek, M., Sandfort, V., & Bluemke, D. A. (2016) · Radiology · doi:10.1148/radiol.2016161670 · Cited by 71 · see citing works

Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging with Cardiovascular Outcomes

Peng, A. W., Dudum, R., Jain, S. S., Maron, D. J., Patel, B. N., Khandwala, N., Eng, D., Chaudhari, A. S., Sandhu, A. T., & Rodriguez, F. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.06.040 · Cited by 67 · see citing works

Cardiac Magnetic Resonance Fingerprinting

Liu, Y., Hamilton, J., Rajagopalan, S., & Seiberlich, N. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.08.028 · Cited by 66 · see citing works

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

Gang, Y., Chen, X., Li, H., Wang, H., Li, J., Guo, Y., Zeng, J., Hu, Q., Hu, J., & Xu, H. (2021) · European Radiology · doi:10.1007/s00330-020-07629-4 · Cited by 66 · see citing works

Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping

Hamilton, J., Currey, D., Rajagopalan, S., & Seiberlich, N. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28568 · Cited by 58 · see citing works

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA

Alaref, S. J., Singh, G., Choi, J. W., Xu, Z., Maliakal, G., van Rosendael, A. R., Lee, B. C., Fatima, Z., Andreini, D., Bax, J. J., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.03.025 · Cited by 57 · see citing works

Advances in Coronary Computed Tomographic Angiographic Imaging of Atherosclerosis for Risk Stratification and Preventive Care

Bienstock, S., Lin, F. Y.-H., Blankstein, R., Leipsic, J. A., Cardoso, R. N., Ahmadi, A. A., Gelijns, A. C., Patel, K., Baldassarre, L. A., Hadley, M. B., et al. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.02.002 · Cited by 56 · see citing works

Reproducibility of quantitative plaque measurement in advanced coronary artery disease

Meah, M. N., Singh, T., Williams, M. C., Dweck, M. R., Newby, D. E., Slomka, P. J., Adamson, P. D., Moss, A. J., & Dey, D. (2020) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2020.12.008 · Cited by 55 · see citing works

Methodology for ESR iGuide content

European Society of Radiology (ESR) (2019) · Insights into Imaging · doi:10.1186/s13244-019-0720-z · Cited by 53 · see citing works

A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography

Fuin, N., Bustin, A., Küstner, T., Öksüz, İ., Clough, J. R., King, A. P., Schnabel, J. A., Botnar, R. M., & Prieto, C. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.04.007 · Cited by 53 · see citing works

Infarct Characterization and Quantification by Delayed Enhancement Cardiac Magnetic Resonance Imaging Is a Powerful Independent and Incremental Predictor of Mortality in Patients With Advanced Ischemic Cardiomyopathy

Kwon, D. H., Asamoto, L., Popović, Z., Kusunose, K., Robinson, M., Desai, M. Y., Marwick, T. H., & Flamm, S. D. (2014) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.114.002077 · Cited by 52 · see citing works

A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting

Hamilton, J. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.928546 · Cited by 51 · see citing works

Cardiovascular Imaging, Climate Change, and Environmental Sustainability

Gunasekaran, S., Szava-Kovats, A., Battey, T., Gross, J., Picano, E., Raman, S. V., Lee, E., Bissell, M. M., Alasnag, M. A., Campbell‐Washburn, A. E., et al. (2024) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.240135 · Cited by 51 · see citing works

Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease

Qiao, H. Y., Tang, C. X., Schoepf, U. J., Tesche, C., Bayer, R. R., Giovagnoli, D. A., Hudson, H. T., Zhou, C. S., Yan, J., Lu, M. J., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06964-w · Cited by 50 · see citing works

Vertical off‐centering affects organ dose in chest CT : Evidence from Monte Carlo simulations in anthropomorphic phantoms

Saltybaeva, N., & Alkadhi, H. (2017) · Medical Physics · doi:10.1002/mp.12519 · Cited by 49 · see citing works

Patient Satisfaction in Radiology: Qualitative Analysis of Written Complaints Generated Over a 10-Year Period in an Academic Medical Center

Salazar, G., Quencer, K. B., Aran, S., & Abujudeh, H. H. (2013) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2013.03.013 · Cited by 47 · see citing works

Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: applicability to clinical routine settings

Backhaus, S. J., Staab, W., Steinmetz, M., Ritter, C. O., Lotz, J., Hasenfuss, G. P., Schuster, A., & Kowallick, J. T. (2019) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-019-0532-9 · Cited by 46 · see citing works

Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study

Jonas, R. A., Weerakoon, S., Fisher, R., Griffin, W. F., Kumar, V., Rahban, H., Marques, H. P., Karlsberg, R. P., Jennings, R. S., Crabtree, T. R., et al. (2022) · Clinical Imaging · doi:10.1016/j.clinimag.2022.08.005 · Cited by 46 · see citing works

Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography

Takafuji, M., Kitagawa, K., Mizutani, S., Hamaguchi, A., Kisou, R., Iio, K., Ichikawa, K., Izumi, D., & Sakuma, H. (2023) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.230085 · Cited by 41 · see citing works

Energy and Greenhouse Gas Emission Savings Associated with Implementation of an Abbreviated Cardiac MRI Protocol

Ibrahim, F., Cadour, F., Campbell‐Washburn, A. E., Allen, B. D., Vosshenrich, J., Brown, M. J., Thavendiranathan, P., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.240588 · Cited by 40 · see citing works

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data

Fang, C., Dziedzic, A., Zhang, L., Oliva, L., Verma, A. A., Razak, F. A., Papernot, N., & Wang, B. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105006 · Cited by 39 · see citing works

Publication bias in diagnostic imaging: conference abstracts with positive conclusions are more likely to be published

Treanor, L., Frank, R. A., Cherpak, L. A., Dehmoobad Sharifabadi, A., Salameh, J.-P., Hallgrimson, Z., Fabiano, N., McGrath, T. A., Kraaijpoel, N., Yao, J., et al. (2020) · European Radiology · doi:10.1007/s00330-019-06568-z · Cited by 38 · see citing works

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging

Dey, D., Arnaout, R., Antani, S., Badano, A. G., Jacques, L., Li, H., Leiner, T., Margerrison, E., Samala, R. K., Sengupta, P. P., et al. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.05.012 · Cited by 34 · see citing works

Motion artifact removal in coronary CT angiography based on generative adversarial networks

Zhang, L., Jiang, B., Chen, Q., Wang, L., Zhao, K., Zhang, Y., Vliegenthart, R., & Xie, X. (2022) · European Radiology · doi:10.1007/s00330-022-08971-5 · Cited by 33 · see citing works

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment

Ross, J., Hammouche, S., Chen, Y., Rockall, A., Alabed, S., Chen, M., Dwivedi, K., Fascia, D., Greenhalgh, R., Hall, M., et al. (2024) · Clinical Radiology · doi:10.1016/j.crad.2024.01.026 · Cited by 31 · see citing works

Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection

Zhang, H., Wang, X., Liu, C., Liu, Y., Li, P., Yao, L., Li, H., Wang, J., & Jiao, Y. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abc323 · Cited by 31 · see citing works

Fully automated accurate patient positioning in computed tomography using anterior–posterior localizer images and a deep neural network: a dual-center study

Salimi, Y., Shiri, I., Akavanallaf, A., Mansouri, Z., Arabi, H., & Zaidi, H. (2023) · European Radiology · doi:10.1007/s00330-023-09424-3 · Cited by 28 · see citing works

Motion artefact reduction in coronary CT angiography images with a deep learning method

Ren, P., He, Y., Zhu, Y. C., Zhang, T., Cao, J., Wang, Z., & Yang, Z. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00914-2 · Cited by 27 · see citing works

Deep Learning–Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT

de Vos, B. D., Leßmann, N., de Jong, P. A., & Išgum, I. (2021) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2021190219 · Cited by 26 · see citing works

Cardiac radiology in Europe: status and vision by the European Society of Cardiovascular Radiology (ESCR) and the European Society of Radiology (ESR)

Natale, L., Vliegenthart, R., Salgado, R. A., Bremerich, J., Budde, R. P. J., Dacher, J.-N., Francone, M., Kreitner, K., Loewe, C., Nikolaou, K., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09533-z · Cited by 26 · see citing works

A novel method for non-invasive plaque morphology analysis by coronary computed tomography angiography

Fujimoto, S., Kondo, T., Kodama, T., Fujisawa, Y., Groarke, J. D., Kumamaru, K. K., Takamura, K., Matsunaga, E., Miyauchi, K., Daida, H., et al. (2014) · International journal of cardiac imaging · doi:10.1007/s10554-014-0461-5 · Cited by 26 · see citing works

The Financial Burden of Missed Appointments: Uncaptured Revenue Due to Outpatient No-Shows in Radiology

Mieloszyk, R. J., Rosenbaum, J. I., Hall, C. S., Raghavan, U. N., & Bhargava, P. (2018) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2018.06.001 · Cited by 25 · see citing works

Risk predicting for acute coronary syndrome based on machine learning model with kinetic plaque features from serial coronary computed tomography angiography

Wang, Y., Chen, H., Sun, T., Li, A., Wang, S., Zhang, J., Li, S., Zhang, Z., Zhu, D., Wang, X., et al. (2021) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeab101 · Cited by 25 · see citing works

Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction Using Mesh Priors

van Herten, R. L. M., Hampe, N., Takx, R. A. P., Franssen, K. J., Wang, Y., Suchá, D., Henriques, J. P. S., Leiner, T., Planken, R. N., & Išgum, I. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3326243 · Cited by 23 · see citing works

Fusion Modeling; Combining Clinical and Imaging Data to Advance Cardiac Care

van Assen, M., Tariq, A., Razavi, A. C., Yang, C., Banerjee, I., & Cecco, C. D. D. (2023) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.122.014533 · Cited by 21 · see citing works

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology

Yao, T., Clair, N. S., Miller, G. F., Dorfman, A. L., Fogel, M. A., Ghelani, S. J., Krishnamurthy, R., Lam, C. Z., Quail, M. A., Robinson, J. D., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.230132 · Cited by 21 · see citing works

Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease

Gröschel, J., Kuhnt, J., Viezzer, D., Hadler, T., Hormes, S., Barckow, P., Schulz‐Menger, J., & Błaszczyk, E. (2023) · European Radiology · doi:10.1007/s00330-023-10127-y · Cited by 20 · see citing works

The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records

Baxter, R., Nind, T., Sutherland, J., McAllister, G., Hardy, D. R., Hume, A. C., MacLeod, R., Caldwell, J., Krueger, S., Tramma, L., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220266 · Cited by 18 · see citing works

Impact of Upstream Medical Image Processing on Downstream Performance of a Head CT Triage Neural Network

Hooper, S., Dunnmon, J., Lungren, M. P., Mastrodicasa, D., Rubin, D. L., Ré, C., Wang, A., & Patel, B. N. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200229 · Cited by 18 · see citing works

An Orchestration Platform that Puts Radiologists in the Driver’s Seat of AI Innovation: a Methodological Approach

Cohen, R. Y., & Sodickson, A. D. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00649-0 · Cited by 17 · see citing works

Publication Bias: Association of Diagnostic Accuracy in Radiology Conference Abstracts with Full-Text Publication

Cherpak, L. A., Korevaar, D. A., McGrath, T. A., Dang, W., Walker, D., Salameh, J., Sharifabadi, A. D., & McInnes, M. D. F. (2019) · Radiology · doi:10.1148/radiol.2019182206 · Cited by 16 · see citing works

Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis

Åkesson, J., Ostenfeld, E., Carlsson, M., Arheden, H., & Heiberg, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-28348-y · Cited by 16 · see citing works

A machine learning framework for auto classification of imaging system exams in hospital setting for utilization optimization

Patil, M. A., Patil, R. B., Krishnamoorthy, P. M., & John, J. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591219 · Cited by 15 · see citing works

New CMS Clinical Decision Support Regulations: A Potential Opportunity with Major Challenges

Hentel, K., Menard, A., & Khorasani, R. (2017) · Radiology · doi:10.1148/radiol.2017161560 · Cited by 15 · see citing works

Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting

Hatipoğlu, S., Mohiaddin, R. H., Gatehouse, P. D., Alpendurada, F., Baksi, A. J., İzgi, C., Prasad, S. K., Pennell, D. J., & Krupičková, S. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-022-02649-1 · Cited by 14 · see citing works

Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System

Chung, R., Demers, J. P., Tiberio, R., Savage, C. A., McNulty, F., Stout, M. B., Kambadakone, A. R., Gilman, M. D., Sharma, A., & Alkasab, T. K. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.23.29806 · Cited by 13 · see citing works

Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Rumac, S., Pavon, A. G., Hamilton, J., Rodrigues, D., Seiberlich, N., Schwitter, J., & van Heeswijk, R. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-23573-3 · Cited by 12 · see citing works

Automated Inline Myocardial Segmentation of Joint T1 and T2 Mapping Using Deep Learning

Howard, J. P., Chow, K., Chacko, L., Fontana, M., Cole, G. D., Kellman, P., & Xue, H. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220050 · Cited by 12 · see citing works

Redesigning patient flow in orthopedics and radiology clinics via a three-phase ‘Kaizen’ improvement approach and interrupted time series analysis

Sullivan, K. J., Topper, L., & Rajwani, A. (2022) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzac061 · Cited by 10 · see citing works

Denoising Multiphase Functional Cardiac CT Angiography Using Deep Learning and Synthetic Data

Sandfort, V., Willemink, M. J., Codari, M., Mastrodicasa, D., & Fleischmann, D. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230153 · Cited by 9 · see citing works

Clinical language search algorithm from free-text: facilitating appropriate imaging

Chaudhari, G., Chillakuru, Y. R., Chen, T. L., Pedoia, V., Vu, T.-K. H., Hess, C. P., Seo, Y., & Sohn, J. H. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00740-6 · Cited by 5 · see citing works

Using machine learning for no show prediction in the scheduling of clinical exams

Scrivani, H., & Soeiro, F. J. D. C. P. (2020) · International Journal of Radiology & Radiation Therapy · doi:10.15406/ijrrt.2020.07.00260 · Cited by 4 · see citing works

Modeling workflows: Identifying the most predictive features in healthcare operational processes

Crowley, C., Guitron, S., Son, J., & Pianykh, O. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233810 · Cited by 3 · see citing works

A predictive signal model for dynamic cardiac magnetic resonance imaging

Curtis, A. D., Mertens, A. J., & Cheng, H. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-37475-5 · Cited by 3 · see citing works

Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging

Pallumeera, M., Giang, J. C., Singh, R., Pracha, N. S., & Makary, M. S. (2025) · Cancers · doi:10.3390/cancers17091510 · Cited by 29 · see citing works

Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes

Momin, E., Cook, T. S., Gershon, G., Barr, J., Cecco, C. N. D., & van Assen, M. (2025) · European Radiology · doi:10.1007/s00330-025-11674-2 · Cited by 17 · see citing works

Myocardial infarction, stroke and arterial stenosis: time to reassess a major misunderstanding

Saba, L., & Libby, P. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01186-3 · Cited by 19 · see citing works

Advanced Computer Simulation Based on Cardiac Imaging in Planning of Structural Heart Disease Interventions

Agarwal, A., Ranard, L. S., Vahl, T. P., & Khalique, O. K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196885 · Cited by 10 · see citing works

AI-Driven Multimodality Fusion in Cardiac Imaging: Integrating CT, MRI, and Echocardiography for Precision

Tran, H. H.-V., Thu, A., Twayana, A. R., Fuertes, A., Gonzalez, M., Basta, M., James, M., Mehta, K. A., Eliáš, D., Figaro, Y. M., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001052 · Cited by 9 · see citing works

Applications of Artificial Intelligence in Acute Thoracic Imaging

Briody, H., Hanneman, K. A., & Patlas, M. N. (2025) · Canadian Association of Radiologists Journal · doi:10.1177/08465371251322705 · Cited by 6 · see citing works

Reliability of Gemini 2.5 Pro, ChatGPT 4.1, DeepSeek V3, and Claude Opus 4 in generating standardized CMR protocols

Licu, R.-A., Muscogiuri, G., Casartelli, D., Bacarea, A., Pop, M., Licu, A.-M., Sferratore, D., Caruso, A., Mirchuk, M., Tarkowski, P., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-025-00671-1 · Cited by 6 · see citing works

Applying artificial intelligence to cardiac MRI to diagnose congenital heart disease in low-resource settings such as Sub-Saharan Africa

Negussie, M. A., Sanchez, N., Sidiq, S. A., Trvalik, A., Baettig, E., Özkök, S., & Umair, M. (2025) · Communications Medicine · doi:10.1038/s43856-025-01179-w · Cited by 5 · see citing works

Zero-Shot PI-RADS Version 2.1 Scoring with ChatGPT-4 Turbo and Llama 3: Diagnostic Performance and Agreement with Abdominal Radiologists

Firoozeh, N., Mastrodicasa, D., Behr, S. C., Muglia, V. F., & Westphalen, A. C. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.250119 · Cited by 5 · see citing works

Beyond volumes and ejection fraction: practical insights into cine-CMR interpretation and applications

Vaz, A., Serra, V. C., & Ramos, D. D. O. S. (2025) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-025-03530-7 · Cited by 5 · see citing works

Artificial intelligence-enabled electrocardiography to triage echocardiography for structural heart disease diagnosis in a low-resource setting

Pedroso, A. F., Nascimento, B. R., Dhingra, L. S., Shankar, S. V., Vinhal, W., Reges, R. B. E., Cardoso, C. S., Sable, C., Ribeiro, A. L. P., & Khera, R. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101539 · Cited by 4 · see citing works

Artificial Intelligence in Coronary Computed Tomography

Giarletta, L., Zhou, B., Marano, R., Cecco, C. N. D., & van Assen, M. (2026) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000873 · Cited by 3 · see citing works

The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging

Sejo, C. S., Randazzo, M., Lang, R. M., & Slivnick, J. A. (2026) · Current Cardiology Reports · doi:10.1007/s11886-025-02344-2 · Cited by 3 · see citing works

Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial

Petch, J., Bortesi, J. P. T., Sheth, T., Natarajan, M. K., Pinilla‐Echeverri, N., Di, S., Bangdiwala, S. I., Mosleh, K., Ibrahim, O., Bainey, K. R., et al. (2025) · JMIR Research Protocols · doi:10.2196/71726 · Cited by 3 · see citing works

Quantitative evaluation of an artificial intelligence-assisted platform in CT acquisition workflow

Caballo, M., McLennan, L., Benbow, M., Condron, M., Foden, A., Thomas, S., & Bull, R. K. (2025) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2025.102133 · Cited by 2 · see citing works

The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging

Özkök, S. (2025) · Pediatric Radiology · doi:10.1007/s00247-025-06487-w · Cited by 2 · see citing works

Artificial intelligence in nuclear cardiology: Technical perspectives, strategic directions, and recommendations from an IAEA expert working group

Wiefels, C., Juárez‐Orozco, L. E., Craviolatti, P. S., Diahiliev, O., Eskander, A. M., Giubbini, R., Milan, E., Zhou, W., Karthikeyan, G., Jiménez‐Heffernan, A., et al. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2025.11.011 · Cited by 2 · see citing works

Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography

Bennett, S., Wild, C. B., Paton, M. F., Victor, K., Khan, S., Kirkham, J., Gaye, G., Peacock, C., Robinson, S., Manisty, C., et al. (2026) · Echo Research and Practice · doi:10.1186/s44156-026-00124-4 · Cited by 2 · see citing works

Cardiothoracic Imaging AI for Cardiac Diseases

Tripathi, S., O’Quinn, R. P., & Cook, T. S. (2025) · Seminars in Roentgenology · doi:10.1053/j.ro.2025.06.004 · Cited by 2 · see citing works

Cardiovascular Magnetic Resonance imaging analysis using neural networks

Wang, L. M., Wang, L. M., Wu, P., & Ding, L. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101874 · Cited by 3 · see citing works

SCMR Survey of Centers Performing CMR in Pediatric/Congenital Heart Disease: 2024 Update

Buddhe, S., Soriano, B. D., Wilson, H. C., (Dorfman), A. M., Rutz, T., Chudgar, P. D., Greil, G. F., Guerrero‐Chalela, C., Husain, N., Cheung, M. M. H., et al. (2025) · Journal of Cardiovascular Magnetic Resonance · doi:10.1016/j.jocmr.2024.101159 · Cited by 1 · see citing works

In Vivo Dynamic Coronary Arteries Blood Flow Imaging Based on Multi‐Cycle Phase Clustering Ultrafast Ultrasound

Yu, H., Zhang, J., Feng, F., Yang, J., Xia, Y., Zhao, Y.-L., & Zhang, J. (2025) · Advanced Science · doi:10.1002/advs.202505485 · Cited by 1 · see citing works

A Scalable Big Data-Driven Distributed Deep Learning Framework for Breast Cancer Diagnosis Using Big Data Analytics

Babar, M., Kaleem, S., ELAffendi, M. A., & Khan, Z. A. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.12485 · Cited by 1 · see citing works

Artificial intelligence in MRI clinical practice: From historical innovation to emerging trends

Aldahery, S. T. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102158 · Cited by 1 · see citing works

Tips and Tricks for Interpreting Coronary CT Angiography – Part 2: Accurate Assessment of Stenosis Severity

Canan, A., Wiessman, M. P., Aslan, Z. S., & Rajiah, P. S. (2026) · Canadian Association of Radiologists Journal · doi:10.1177/08465371251413195 · Cited by 1 · see citing works

From Artificial Intelligence to Robotics: How to Navigate Technological Innovation in Radiology With the Gartner Hype Cycle

Dogra, S., Rifai, S., Hadi, M., Hanna, T. N., Rozenshtein, A., Retrouvey, M., Huang, H., & Doo, F. X. (2026) · Academic Radiology · doi:10.1016/j.acra.2025.11.018 · Cited by 1 · see citing works

Clinical usability of generative artificial intelligence for MR safety advice

Rose, H., Thorpe, J. C., Panek, R., Goncalves, E., & Morgan, P. S. (2026) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2026.100083 · Cited by 1 · see citing works

Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?

Singhal, A., Bowyer-Howell, T., Sabharwal, N. K., Lewis, A. C. W., Mitchell, A. R. J., Rider, O. J., & Henry, J. A. (2026) · Life · doi:10.3390/life16030507 · Cited by 1 · see citing works

Highlights from the first interdisciplinary summit of the European Association of Cardiovascular Imaging and the European Society for Cardiovascular Radiology

Vliegenthart, R., Baritussio, A., Budde, R. P. J., Pontone, G., van Assen, M., Dacher, J., Danad, I., Delgado, V., Dweck, M. R., van der Harst, P., et al. (2026) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeag031 · Cited by 1 · see citing works

Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review

Rai, M. C. E., Far, A. B., Darweesh, M., Dhou, S., Aburaed, N., El, S., Elkhazendar, M., & Ellahham, S. (2026) · Diagnostics · doi:10.3390/diagnostics16101485 · Cited by 1 · see citing works

Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction

Porta, M., Agresti, G., Laganà, M. M., Orofino, S., Pangaro, S., Carapella, N., Bonaffini, P. A., Bernasconi, P., Genovese, E. A., Sironi, S., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00743-w · Cited by 1 · see citing works

Calculation of Ejection Fraction Using Cardiac Computed Tomography: Clinical Evolution, Reliability, and Technological Challenges—A Narrative Review

Steffani, S., Piscione, M., Gaudio, D., Meghnagi, G., Crignola, G. G., Asmundo, L., Tagliati, C., Laudazi, M., & Chiocchi, M. (2026) · Medicina · doi:10.3390/medicina62061084 · Cited by 1 · see citing works

Reference framework for implementation of cardiovascular imaging in clinical trials: a scientific statement of the European Association of Cardiovascular Imaging of the ESC

Surkova, E., Gimelli, A., Giannopoulos, A. A., Marsan, N. A., Baggiano, A., Čikeš, M., Baritussio, A., Ramkisoensing, A. A., Dweck, M. R., González‐Del‐Hoyo, M., et al. (2026) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeag171 · Cited by 1 · see citing works

From promise to practice: Implementing artificial intelligence in radiology

Hanneman, K. A., & Patlas, M. N. (2026) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2026.01.010 · Cited by 1 · see citing works

Real-World Implementation of Artificial Intelligence: Fully Automated Coronary Artery Calcium Scoring

Kang, S. R., & Rho, J. Y. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2025.1978 · Cited by 1 · see citing works

Highlights from the first interdisciplinary summit of the European Association of Cardiovascular Imaging and the European Society for Cardiovascular Radiology

Vliegenthart, R., Baritussio, A., Budde, R. P. J., Pontone, G., van Assen, M., Dacher, J., Danad, I., Delgado, V., Dweck, M. R., van der Harst, P., et al. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02256-x · Cited by 1 · see citing works

Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review

McDonald, V. J. F., Parsa, S., Mastrodicasa, D., Rodriguez, F., Langlotz, C., Maron, D. J., Sandhu, A. T., Dudum, R., & Jain, S. S. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000053

Survey of Advanced Image Fusion Techniques for Enhanced Visualization in Cardiovascular Diagnosis and Treatment

Trivedi Gargi, J. (2025) · Journal of Clinical Medical and Experimental Images · doi:10.29328/journal.jcmei.1001034

Editor’s Note 2024: The Year in Review for Radiology

Moy, L. P. (2025) · Radiology · doi:10.1148/radiol.250376

Artificial Intelligence to Boost Vascular Enhancement and Minimize the Environmental Impact of CT

Suchá, D., Huisman, M., & Hanneman, K. A. (2025) · Canadian Association of Radiologists Journal · doi:10.1177/08465371251327137

Top Publications in Radiology , 2025

Fowler, K. J., & Goh, V. (2025) · Radiology · doi:10.1148/radiol.253887

U-TraCE: a conformal prediction approach to uncertainty quantification in black-box models

Marchi, E., & Liebl, M. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae35ce · Cited by 1 · see citing works

Optimization and technological advances in cardiovascular magnetic resonance to improve access and screening

Li, X., Sprinkart, A. M., Isaak, A., & Luetkens, J. A. (2026) · British Journal of Radiology · doi:10.1093/bjr/tqag016

The Efficiency of Artificial Intelligence-Enhanced CT Scans to Combat COVID-19 for Diagnostic Accuracy: A Narrative Review

Akaf, A., Darvish, L., & Haghparast, M. (2025) · Disease and Diagnosis · doi:10.34172/ddj.1654

What should we do with all the AI-generated information?

Arbab‐Zadeh, A. (2026) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2026.01.014

Climate Change and Globally Sustainable Cardiovascular Magnetic Resonance

Bodnariuc, N., Markl, M., Cook, T., Law, M., & Hanneman, K. A. (2026) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2026.01.006

Strain Imaging in Heart Failure

Hayes, D., Bayshtok, G., Stojanovska, J., & Bloom, M. W. (2026) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2026.01.007

Feasibility of two-dimensional multi-segmented late gadolinium enhancement combined with artificial intelligence reconstruction deep-learning noise reduction in patients with non-ischemic cardiomyopathy

Orii, M., Fujiwara, J., Obara, J., Sugawara, T., & Yoshioka, K. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110642

Cardiovascular imaging for the detection of asymptomatic coronary artery disease: an updated systematic review of clinical practice guidelines

Kumar, S., Shyam-Sundar, V., Archbold, A., Ferket, B. S., Ricci, F., & Khanji, M. Y. (2026) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyag034

Coronary CT Angiography Pearls and Pitfalls

D’Errico, L., & Hanneman, K. A. (2026) · Canadian Association of Radiologists Journal · doi:10.1177/08465371261429370

Applications of coronary computed tomography angiography: a clinical cardiologist’s point of view

Bolognese, L., Chioccioli, M., & Reccia, M. R. (2026) · European Heart Journal Supplements · doi:10.1093/eurheartjsupp/suag030

SAS-SemiUNet++: A Stochastic Consistency Regularized Framework with Scale-Aware Semantic Recalibration for Cardiac MRI Segmentation

Rao, J., Ma, X., & Li, X. (2026) · Applied Sciences · doi:10.3390/app16073507

Editor’s Pick: Universal Non-invasive Cardiac Imaging: Which Modality Best Combines Anatomy and Physiology?

Saulat, F., Patel, R., King, D., Khasawneh, T., Blaker, N. J., Fu, S., Eskander, C., Lunny, C., Chalamgari, A., Name, J. V., et al. (2026) · EMJ Radiology · doi:10.33590/emjradiol/v6908tnz

Do We Trust the Algorithm? Machine Learning for Coronary Plaque Quantification

Williams, M. C. (2026) · Radiology · doi:10.1148/radiol.260858

From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging

Kang, E. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2025.1964

Navigating the intelligence revolution: transforming cardiovascular care through AI-driven innovation

Tang, Y., Zhao, K., & Zhao, S. (2026) · Open Heart · doi:10.1136/openhrt-2026-004160

Optimizing Cardiac Imaging Protocols: Best Practices for Climate Sustainability

Kharawala, A., Pargaonkar, S., Satyavolu, B. S., Hatimi, A., Hovseth, C., & Tiwari, N. (2026) · Current Cardiovascular Imaging Reports · doi:10.1007/s12410-026-09615-8

Radiology: Cardiothoracic Imaging Highlights 2025

Sanchez, D. A. P., de Farias, L. D. P. G., Onnis, C., Suchá, D., Escalon, J. G., Hanneman, K. A., Abbara, S., Litt, H. I., Gulsin, G. S., & Mastrodicasa, D. (2026) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.260065

Advances in artificial intelligence for the evaluation of mitral regurgitation

Sanchez, A. A., Sadeghpour, A., & Asch, F. M. (2026) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001317

Mind the Gap: Cardiac MRI and the Future of Heart Failure Risk Prediction

Hanneman, K. A., & Moayedi, Y. (2026) · Radiology · doi:10.1148/radiol.261363

Digitale Patientenreise

Aziz-Safaie, T., Bienias, T., Böhner, A. M. C., Beissel, L. D., Isaak, A., & Luetkens, J. A. (2026) · Die Radiologie · doi:10.1007/s00117-026-01633-3

Artificial Intelligence in Cardiovascular Imaging: Integration with Clinical and Nonimaging Data

Moreo, A. M., Bombace, S., Sforna, S., Frontoni, E., Panunzi, M., Bonelli, A., Ricci, F., Marsan, N. A., & Fortuni, F. (2026) · Journal of Cardiovascular Echography · doi:10.4103/jcecho.jcecho_45_26

Integrating AI Into Emergency Radiology: Promises, Pitfalls, and Practical Approaches

Miller, Z. D., King, W. E., Cross, N. M., Singh, S., Linnau, K. F., Medverd, J. R., Sahani, D., & Mastrodicasa, D. (2026) · Seminars in Roentgenology · doi:10.1016/j.ro.2026.151009

Advancements in Cardiac Magnetic Resonance Imaging: Innovations, Challenges, and Future Directions

Dugal, J. K., Woolley, R. K., Liszewski, A., Razmi, R., Nameh, S., Malhi, J. S., Malhi, A. S., & Singh, A. (2026) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm48251

Artificial intelligence in interventional cardiology: from procedural planning to intelligent cath lab ecosystems

Liang, Y., Li, Z., & Ge, J. (2026) · Vessel Plus · doi:10.20517/2574-1209.2026.40

Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review

Mora, R., Irannejad, K., Abbas, N., Mogga, P., Iskander, B., Hubbard, L., Roy, S. K., Lakshmanan, S., Budoff, M. J., & Krishnan, S. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1850945

The evolving role of cardiac CT: Emerging technologies and clinical impact

Oda, S., Iima, M., Yanagawa, M., Saida, T., Ide, S., Takumi, K., Ito, R., Kurokawa, R., Honda, M., Nishioka, K., et al. (2026) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2026.10.001

NOTIFY-DISCUSS: Patient and clinician perspectives on opportunistic screening for coronary artery calcification using artificial intelligence

Keamy-Minor, E., Dudum, R., Bragdon, C. L., Ngo, S., Xu, S., Asch, S. M., Maron, D. J., Brown-Johnson, C., Sandhu, A. T., & Rodriguez, F. (2026) · American Heart Journal Plus Cardiology Research and Practice · doi:10.1016/j.ahjo.2026.100989

RF-based cardiac activity sensing in daily life: Region-focused enhancement and dynamic harmonic constraints for precise heartbeat extraction

Cai, Y., Li, X., 施凱耀, Li, J., Zhang, Y., & Cui, W. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111703

Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging

Mastrodicasa, D., & van Assen, M. (2024) · BJR|Open · doi:10.1093/bjro/tzaf015 · Cited by 2 · see citing works

[Artificial intelligence in cardiac imaging: status and future developments].

Ryffel, C. P., Shiri, I., & Gräni, C. (2026) · PubMed · doi:10.23785/tu.2026.02.004

Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?

Singhal, A., Bowyer-Howell, T., Sabharwal, N. K., Lewis, A. C. W., Mitchell, A. R. J., Rider, O. J., & Henry, J. A. (2026) · Life · doi:10.3390/life16030507 · Cited by 1 · see citing works

Application of artificial intelligence in cardiac CT: From basics to clinical practice

van den Oever, L. B., Vonder, M., van Assen, M., van Ooijen, P. M. A., de Bock, G. H., Xie, X., & Vliegenthart, R. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.108969 · Cited by 44 · see citing works

The Role of Artificial Intelligence in Cardiac Imaging

Onnis, C., van Assen, M., Muscogiuri, E., Muscogiuri, G., Gershon, G., Saba, L., & Cecco, C. N. D. (2024) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2024.01.002 · Cited by 14 · see citing works