Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856

Statistical Methods for Rates and Proportions

Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

Measuring nominal scale agreement among many raters.

Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733

Guidelines for the Early Management of Patients With Acute Ischemic Stroke

Jauch, E. C., Saver, J. L., Adams, H. P., Bruno, A., Connors, J. J. “., Demaerschalk, B. M., Khatri, P., McMullan, P. W., Qureshi, A. I., Rosenfield, K., et al. (2013) · Stroke · doi:10.1161/str.0b013e318284056a · Cited by 7,698

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697

Untitled work

(2005) · PubMed · Cited by 7,011

Generation of synthetic training data for SEEG electrodes segmentation

Pantovic, A., Ren, X., Wemmert, C., Ollivier, I., & Essert, C. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02585-4 · Cited by 4

Use of Deep Learning in Biomedical Imaging

Shanu, N., & Ganesh, R. S. (2022) · Springer eBooks · doi:10.1007/978-3-030-96569-3_1 · Cited by 4

Brief Introduction to Artificial Intelligence and Machine Learning

Motamedian, S. R., Hassanzadeh-Samani, S., Nadimi, M., Shobeiri, P., Motie, P., Rohban, M. H., Mahmoudinia, E., & Mohammad‐Rahimi, H. (2023) · Springer eBooks · doi:10.1007/978-981-19-8602-4_14 · Cited by 4

Human Factors of Using Artificial Intelligence in Healthcare: Challenges That Stretch Across Industries

Furniss, D. J., Habli, I., Sujan, M. A., & Hawkins, R. (2020) · Safety-Critical Systems Symposium (SSS) · doi:10.65391/r1186 · Cited by 4

Artificial Intelligence Surgery

(2021) · Artificial Intelligence Surgery · doi:10.20517/ais · Cited by 4

Advancement in Diagnostic and Therapeutic Techniques for Ischemic Stroke

Jain, M., Patil, D., Gupta, S., & Mahajan, S. (2024) · Wiley eBooks · doi:10.1002/9781394230952.ch1 · Cited by 4

The Effect of AI on the Radiologist Workforce: A Task-Based AnalysisPreprint

Langlotz, C. P. (2025) · medRxiv · doi:10.64898/2025.12.20.25342714 · Cited by 4

Transforming Healthcare: The Role of Generative AI in Personalized Treatment Recommendations

Gupta, V., Singh, A., Singh, H., & Chaudhary, E. (2024) · Advances in Healthcare Information Systems and Administration · doi:10.4018/979-8-3693-7452-8.ch011 · Cited by 5

Neurofind: using deep learning to make individualised inferences in brain-based disorders

Vieira, S., Baecker, L., Pinaya, W. H. L., Garcia‐Dias, R., Scarpazza, C., Calhoun, V. D., & Mechelli, A. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03290-x · Cited by 4

MEPNet: Medical Entity-Balanced Prompting Network for Brain CT Report Generation

Zhang, X., Shi, Y., Ji, J., Zheng, C., & Qu, L. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i24.34788 · Cited by 3

Deep Learning Model for Osteoporosis Screening From Chest Radiographs: A Multicenter Analysis of External Robustness and Model Calibration

Imoto, Y., Inui, T., Matsui, K., Watanabe, Y., Igari, T., Takeuchi, S., Kimura, M., Yagi, S., & Kawano, H. (2025) · Cureus · doi:10.7759/cureus.89446 · Cited by 3

Deep Learning Model for Extensive Diagnosis of Corneal Deposits

Taki, Y., Ueno, Y., Oda, M., Kitaguchi, Y., Maehara, H., & Yamaguchi, T. (2025) · Cornea · doi:10.1097/ico.0000000000004018 · Cited by 3

Bridging artificial intelligence and ethics in medical education: a comprehensive perspective from students and faculty

Hawal, M. V., Simon, M., Husain, N. H. A., Al-Harrasi, S. K. R., & Al-Hinai, L. N. S. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.3.14 · Cited by 3

TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network

Zanier, O., Ryu, S. Y., Mutten, R. D., Theiler, S., Carretta, A., Palandri, G., Mazzatenta, D., Regli, L., Serra, C., & Staartjes, V. E. (2026) · European Radiology · doi:10.1007/s00330-025-12253-1 · Cited by 3

An artificial intelligence approach for segmenting and classifying brain lesions caused by stroke

Mena, R., Peláez, E., Loayza, F. R., Macas, A., & Franco-Maldonado, H. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2264410 · Cited by 3

14P Classification of molecular subtypes of breast cancer in whole-slide histopathological images using a novel deep learning algorithm

Kim, H. S., Min, K.-W., & Kim, J. S. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.146 · Cited by 3

Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis

Zhang, F., Turhon, M., Huang, J., Li, M., Liu, J., Zhang, Y., & Zhang, Y. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-793 · Cited by 3

AI in Acute Cerebrovascular Disorders: What can the Radiologist Contribute?

Zhang, Y., Joshi, J. K., & Hadi, M. (2024) · Seminars in Roentgenology · doi:10.1053/j.ro.2024.01.001 · Cited by 3

Specific-CT brain template construction and retrospective dosimetric comparison study in brain for nasopharyngeal carcinoma patients treated with IMRT or VMAT

Du, F. (2024) · American Journal of Cancer Research · doi:10.62347/hacn9549 · Cited by 3

A critical review on the relevance, essentiality, and analytical techniques of trace elements in human cancer

Liu, R. R., Zhang, Q., Yan, X., & Lv, Y. (2025) · Metallomics · doi:10.1093/mtomcs/mfaf005 · Cited by 3

A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage

Yoo, Y., Georgescu, B., Zhang, Y., Grbić, S., Liu, H., Aldea, G.-D., Re, T. J., Das, J., Ullaskrishnan, P., Eibenberger, E., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04965-0_1 · Cited by 3

Strukturierte Befundung und künstliche Intelligenz

Hempel, J.-M., & dos Santos, D. P. (2021) · Der Radiologe · doi:10.1007/s00117-021-00920-5 · Cited by 3

Decision tree distinguish affective disorder diagnosis from psychotic disorder diagnosis with clinical and lab factors

Liu, X., Wang, X., Wen, C., & Li, W. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11514 · Cited by 3

Artificial intelligence-based identification of brain CT medical images

Chen, R., Cai, Y., Wu, J., Liu, H., Zheng, P., Xie, Y., Xu, C., & Xiao, P. (2023) · Applied Optics and Photonics China: Biomedical Optics (AOPC) · doi:10.1117/12.2652045 · Cited by 3

Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis

Salave, S., Rana, D., Benival, D. M., & Jain, A. (2023) · Current Indian Science · doi:10.2174/2210299x01666230223104508 · Cited by 3

Basilar tubercles and eminences of the clivus: Novel anatomical entities

Tangrodchanapong, T., Yurasakpong, L., Suwannakhan, A., Chaiyamoon, A., Iwanaga, J., & Tubbs, R. S. (2023) · Annals of Anatomy - Anatomischer Anzeiger · doi:10.1016/j.aanat.2023.152133 · Cited by 3

Machine Learning for Cerebrovascular Disorders

Yu, Y., & Chen, D. Y. T. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_29 · Cited by 3

A fast and accurate brain extraction method for CT head images

Hu, D., Liang, H., Qu, S., Han, C., & Jiang, Y. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01097-0 · Cited by 3

Using ScrutinAI for visual inspection of DNN performance in a medical use case

Görge, R., Haedecke, E., & Möck, M. (2023) · AI and Ethics · doi:10.1007/s43681-023-00399-x · Cited by 3

Intracranial Hemorrhage Segmentation using Neural Network and Riesz Fractional Order Derivative-Based Texture Enhancement

Manokhin, D., & Sokolovskyy, Y. (2024) · Computer Design Systems Theory and Practice · doi:10.23939/cds2024.01.001 · Cited by 3

Deep Learning Approach to Predict Intracerebral Hemorrhage and Grad-Cam Visualization on CT Images

Rao, B. N., Sabut, S., & Mishra, R. (2024) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci61380.2024.10547930 · Cited by 3

Application of Artificial Intelligence Technologies in Neurology: A Literature Review

Чичановская, Л. В., Сороковикова, Т. В., Морозов, А. М., & Ковальчук, Ю. И. (2024) · Неврология и нейрохирургия Восточная Европа · doi:10.34883/pi.2024.14.1.049 · Cited by 3

Zukünftige Bildanalyse mit Hilfe automatisierter Algorithmen

Weichert, J., Rody, A., & Gembicki, M. (2020) · Der Gynäkologe · doi:10.1007/s00129-020-04563-w · Cited by 3

Künstliche Intelligenz in der Neurointensivmedizin

Schweingruber, N., & Gerloff, C. P. (2021) · Der Nervenarzt · doi:10.1007/s00115-020-01050-4 · Cited by 3

Intracranial hemorrhage detection and classification using deep learning

Ojha, N., & Banerji, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90037-9.00009-6 · Cited by 3

A Pipeline for Segmenting and Classifying Brain Lesions Caused by Stroke: A Machine Learning Approach

Mena, R., Macas, A., Peláez, E., Loayza, F. R., & Franco-Maldonado, H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-04829-6_37 · Cited by 3

Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks

Angermann, C., Haltmeier, M., & Siyal, A. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25085-9_4 · Cited by 3

A Comparative Study on Subdural Brain Hemorrhage Segmentation

Gençtürk, T. H., Kaya, İ., & Gülağız, F. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27099-4_24 · Cited by 3

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

Ngo, D. T., Nguyen, T. T. B., Nguyen, H. T., Nguyen, D. B., Nguyen, H. Q., & Pham, H. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10207932 · Cited by 4

The COVID-19 opportunity for Qure.ai

Roy, R. (2021) · The International Journal of Entrepreneurship and Innovation · doi:10.1177/14657503211063913 · Cited by 3

Assuring safe artificial intelligence in critical ambulance service response: study protocol

Sujan, M. A., Thimbleby, H. W., Habli, I., Cleve, A., Maaløe, L., & Rees, N. (2022) · British Paramedic Journal · doi:10.29045/14784726.2022.06.7.1.36 · Cited by 3

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology

Simović, A., Lutovac, M., Lekić, S., & Kuleto, V. (2022) · Diagnostics · doi:10.3390/diagnostics12123208 · Cited by 3

Computed tomography and artificial intelligence

Dey, D., Lin, A., Han, D., & Slomka, P. J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00009-9 · Cited by 3

Deep learning and medical diagnosis – Authors' reply

Chilamkurthy, S., & Tanamala, S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32614-5 · Cited by 3

Re-Isotropic Segmentation for Subcellular Ultrastructure in Anisotropic EM Images

Guo, J., Wang, Z., Zhai, H., Zhang, Y., Liu, J., & Han, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3511599 · Cited by 2

Artificial intelligence versus radiologists in predicting lung cancer treatment response: a systematic review and meta-analysis

Rodriguez, N. G., Mercado, N. C., Panjiyar, K., & Kunwor, R. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1634694 · Cited by 2

Development and internal validation of multimodal machine learning models for predicting eligibility for mechanical thrombectomy in suspected stroke patients using routinely collected clinical and imaging data

Agarwal, A., Bharti, N., Ghosh, T., Golla, S., Bains, N. K., Chamadia, R., Robert, D., Putha, P., & Qureshi, A. I. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334242 · Cited by 2

Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study

Chunyu, H., Chen, Y., Xue, S., Zhang, X., Miao, Y., Guo, R., Li, B., & Shi, K. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07618-z · Cited by 2

Patient and clinician perspectives in the use of machine learning and artificial intelligence in the context of acute neurology

Abahuje, E. A. E., Houskamp, E. J., do Nascimento, J. S. P., Agha, E., Thompson, W. K., Michelson, K. N., & Naidech, A. M. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf143 · Cited by 2

Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification

Bhaduri, S., Mondal, R., Sarangi, P., Kurmi, V. K., Goyal, S., Kaushal, L., Sodani, M., & Basu, T. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100243 · Cited by 2

Two-stage AI model for endometrial cancer staging: YOLOv4 localization and deep learning classification

Chen, M.-J., Hsu, Y.-L., & Xu, J.-L. (2025) · Innovation and Emerging Technologies · doi:10.1142/s2737599425500343 · Cited by 2

Artefacts in continuous neuromonitoring

Chen, X., Bögli, S. Y., Olakorede, I., Xu, W., Beqiri, E., Tang, C., Occhipinti, L. G., Gao, S., & Smielewski, P. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00378-3 · Cited by 2

Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset

Rafati, Z., Hoseyni, M., Khoramdel, J., & Nikoofard, A. (2025) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme68496.2025.11392202 · Cited by 2

Beyond the Pan-Scan: Optimizing Imaging and Management in Pediatric Blunt Trauma — a Narrative Review

Aboali, A. A., Elewa, M., Akinbi, O. M., Haddad, J. A., Hadhoud, A. M., Pyala, R., Yoo, P., Bucciarelli, B., & Alsabri, M. (2026) · Current Emergency and Hospital Medicine Reports · doi:10.1007/s40138-026-00336-2 · Cited by 2

Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation

Zhang, L., Zhao, J., Cheng, G., Wu, J., Liu, Y., Lei, G., & Wang, Q. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1753651 · Cited by 2

Awareness and Education of Medical Students toward Artificial Intelligence and Radiology

Sachdev, R., Garg, K., & Srivastava, A. (2021) · Dentistry and Medical Research · doi:10.4103/dmr.dmr_17_21 · Cited by 2

Validierung und Implementierung von künstlicher Intelligenz in der radiologischen Versorgung

Müller, L., Kloeckner, R., Mildenberger, P., & dos Santos, D. P. (2022) · Die Radiologie · doi:10.1007/s00117-022-01097-1 · Cited by 2

Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

Almujally, N. A., Alzahrani, A., Hakeem, A. M., Attiah, A., Umer, M., Alsubai, S., Polsinelli, M., & Ashraf, I. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19286-6 · Cited by 3

Structural positional encoding for knowledge integration in transformer-based medical process monitoring and trace classification

Irwin, C., Dossena, M., Leonardi, G., & Stefania Montani, B. (2024) · Progress in Artificial Intelligence · doi:10.1007/s13748-024-00356-7 · Cited by 3

Distillation network based on spatial-frequency fusion for super-resolution of medical CT images

Chen, X., Zheng, Q., Chen, J., Yu, F., & Fu, Q. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105067 · Cited by 2

Comparative Analysis of Deep Learning Models for Intracerebral Hemorrhage Detection from Computed Tomography

Ahad, T. R., Alam, S. B., Ha-mim, F. Z., Nur, A., Wasi, S., & Rahman, R. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022361 · Cited by 2

Few-Shot Learning For Auromatic Intracranial Hematoma Segmentation

Liu, X., Zhu, S., Yang, W., & Zhou, S. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669426 · Cited by 2

ANALYSIS OF CIRCULATORY SYSTEM PATHOLOGIES IN HEAD CT DATA – HEMORRHAGE LOCALIZATION

Nemček, J., & Jakubíček, R. (2021) · Lékař a technika - Clinician and Technology · doi:10.14311/ctj.2021.1.10 · Cited by 2

Brain hemorrhage detection using computed tomography images and deep learning

Elen, A., Di̇ker, A., & Subaşı, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18450-5.00014-1 · Cited by 2

Intracranial Hemorrhage Grading using Novel ConceptionNet

Nizarudeen, S., & Shunmugavel, G. R. (2022) · IEEE India Conference · doi:10.1109/indicon56171.2022.10040029 · Cited by 2

Artificial Intelligence Applications for Traumatic Brain Injury Research and Clinical Management

Fabrizio, C., & Termine, A. (2023) · Contemporary clinical neuroscience · doi:10.1007/978-3-031-24930-3_18 · Cited by 2

NextGen Neuroradiology AI

Flanders, A. E., & Geis, J. R. (2023) · Radiology · doi:10.1148/radiol.231426 · Cited by 2

Legal particularities of AI technology usage in real-world data formation

Malichenko, V. S., Gadzhieva, A. O., Игоревна, П. Н., & Solovieva-Oposhnyanskaya, A. Y. (2023) · FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology · doi:10.17749/2070-4909/farmakoekonomika.2023.222 · Cited by 2

The Impact of Artificial Intelligence on the Future of Radiographer Career in Saudi Arabia

Alfuraydi, A., Alquraini, H., & Alotaibi, M. (2024) · Saudi Journal of Radiology · doi:10.55038/sjr.v2i2.167 · Cited by 2

Detection and Segmentation of Skull Fractures via CNN and U-Net Hybrid Model using Computed Tomography Images

Kodavati, T., & Kumar, R. P. (2023) · Global Conference on Information Technologies and Communications (GCITC) · doi:10.1109/gcitc60406.2023.10426088 · Cited by 2

Unsupervised model for structure segmentation applied to brain computed tomography

Santos, P. V., Martins, M. S. R., Nogueira, S. A., Gonçalves, C., Loureiro, R. M., & Calixto, W. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304017 · Cited by 2

A Comprehensive Review of Fracture Detection and Identification from Medical Images using Deep Learning Methods

Kumar, T. P., & Suthendran, K. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575018 · Cited by 2

Analisis Hasil Safire pada Imaging CT Scan Kepala

Irfan, I. (2024) · Corona Jurnal Ilmu Kesehatan Umum Psikolog Keperawatan dan Kebidanan · doi:10.61132/corona.v2i3.604 · Cited by 2

Intracranial hematoma segmentation on head CT based on multiscale convolutional neural network and transformer

Li, G., Gao, K., Liu, C., & Li, S. (2024) · IET Image Processing · doi:10.1049/ipr2.13188 · Cited by 2

The role of humans in the future of medicine: Completing the cycle

Kenig, N., & Vives, A. M. (2025) · Metaverse · doi:10.54517/m3129 · Cited by 2

Machine Learning–Aided Diagnosis Enhances Human Detection of Perilunate Dislocations

Luan, A., von Rabenau, L., Serebrakian, A. T., Crowe, C. S., Do, B. H., Eberlin, K. R., Chang, J., & Pridgen, B. C. (2025) · Hand · doi:10.1177/15589447241308603 · Cited by 2

Development of a No-Reference CT Image Quality Assessment Method Using RadImageNet Pre-trained Deep Learning Models

Ohashi, K., Nagatani, Y., Yamazaki, A., Yoshigoe, M., Iwai, K., Uemura, R., Shimomura, M., Tanimura, K., & Ishida, T. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01542-2 · Cited by 2

Anatomically-guided masked autoencoder pre-training for aneurysm detection

Ceballos-Arroyo, A. M., Kim, J., Lin, C.-H., Qin, L., Young, G. S., & Jiang, H. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00552 · Cited by 2

CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network

Deng, W., Yang, B., Liu, W., Song, W., Gao, Y., & Xu, J. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/7259414 · Cited by 2

Radiomics and Radiogenomics

Li, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-83047-2_16 · Cited by 2

Monocular Depth Decomposition of Semi-Transparent Volume Renderings

Engel, D., Hartwig, S., & Ropinski, T. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3245305 · Cited by 2

Segmentation of Intracranial Hemorrhage through an EfficientNetB7-based UNET model

Kothala, L. P., & Guntur, S. R. (2022) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon56628.2022.10083730 · Cited by 2

Automated Detection of SDH and EDH due to TBI from CT-scan images using CNN

Mazhari, A., Allahgholi, A., & Shafieian, M. (2023) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme61513.2023.10488625 · Cited by 2

The Rise of Deep Learning in Radiology: An Overview of Recent Research

Chatterjee, D. (2019) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2019.6397 · Cited by 2

Deep learning: a turning point in acute neurology

Filippi, M., Cividini, C., & Agosta, F. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30106-0 · Cited by 2

Deep Learning Algorithms to Detect and Localize Acute Intracranial Hemorrhages

Manasa, C. S., & Bhavana, V. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-0425-6_27 · Cited by 2

Prospects and Difficulties of Artificial Intelligence (AI) Implementations in Naturopathy

Verma, A., & Naaz, I. (2022) · Springer eBooks · doi:10.1007/978-3-030-96569-3_15 · Cited by 2

Assessing Robustness to Noise: Low-Cost Head CT TriagePreprint

Hooper, S., Dunnmon, J., Lungren, M. P., Gambhir, S. S., Ré, C., Wang, A., & Patel, B. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07977 · Cited by 2

Building Blocks for Integrating Image Analysis Algorithms into a Clinical WorkflowPreprint

Juluru, K., Shih, H.-H., Elnajjar, P., El-Rowmeim, A., Fox, J. J., Siegel, E. L., & Murthy, K. N. K. (2020) · medRxiv · doi:10.1101/2020.03.06.20027367 · Cited by 2

Noisy Student learning for cross-institution brain hemorrhage detection.Preprint

Lin, E., Kuo, W., & Yuh, E. L. (2021) · arXiv (Cornell University) · Cited by 2

Big Data Analysis of Electronic Health Records: Clinically interpretable representations of older adult inpatient trajectories using time-series numerical data and Hidden Markov ModelsPreprint

Herrero-Zazo, M., Fitzgerald, T. W., Taylor, V., Street, H., Chaudhry, A., Bradley, J. R., Birney, E., & Keevil, V. L. (2021) · medRxiv · doi:10.1101/2021.06.18.21258885 · Cited by 2

Reply:

Chang, P. D., & Chow, D. S. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5913 · Cited by 2

Clinical and pathological diagnosis using AI and data-driven science

Hiraoka, S.-I. (2020) · Journal of Japanese Society of Oral Oncology · doi:10.5843/jsot.32.159 · Cited by 1

CNS Machine Learning

Tong, E., Grøvik, E., Emblem, K. E., Chen, K. T., Fan, A. P., Yu, Y., Zhu, G., Zhao, M. Y., Niri, S. G., & Zaharchuk, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_58 · Cited by 1

Investigate on Skull Defect Reconstruction Technologies

Nguyen, L.-H., Le, T. H., Hoang, H. H., Nguyen, H.-T., & Nguyen, T. K. C. (2024) · IEEE International Conference on Computational Cybernetics (ICCC) · doi:10.1109/iccc62278.2024.10582973 · Cited by 1

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele

Curry, S. D., Boochoon, K. S., Casazza, G. C., Surdell, D. L., & Cramer, J. A. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03259-z · Cited by 1

GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information

Viriyasaranon, T., Ma, S., & Choi, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0901-7_13 · Cited by 1

Advanced Computational Techniques In Stroke Prediction: Analyzing Cardiac And Vascular Bio-Signals

Kathiravan, M., Raju, B. V., & Rao, K. T. (2024) · International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST) · doi:10.1109/icrisst59181.2024.10921861 · Cited by 1

Unsupervised motion artifacts reduction for cone-beam CT via enhanced landmark detection

Viriyasaranon, T., Ma, S., Thies, M., Maier, A., & Choi, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127258 · Cited by 1

A Low-Cost Articulated Arm Navigation System for External Ventricular Drain Placement

Smith, A. D., Naik, A., Pappu, S., Arnold, P. M., & Hauser, K. K. (2025) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2025.3573390 · Cited by 1

INTRACRANIAL HEMORRHAGE DETECTION IN CT SCAN USING DEEP LEARNING

Tharek, A., Muda, A. S., Hudi, A. B., & Huddin, A. B. (2022) · Asian Journal Of Medical Technology · doi:10.32896/ajmedtech.v2n1.1-18 · Cited by 42

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography

Wang, H.-C., Wang, S. H., Xiao, F., Ho, U., Lee, C. C., Yan, J., Chen, Y., & Ko, L.-W. (2025) · Neurologia medico-chirurgica · doi:10.2176/jns-nmc.2024-0163 · Cited by 5

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning

Kuo, W., Häne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908021116 · Cited by 281

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal, D. K., Poonamallee, L., & Joshi, S. (2023) · Indian Journal of Neurotrauma · doi:10.1055/s-0043-1770770 · Cited by 6

Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance

Kang, D.-W., Kim, M., Park, G.-H., Kim, Y., Han, M., Lee, M., Kim, D., Ryu, W., & Jeong, H. (2025) · Neuroradiology · doi:10.1007/s00234-025-03560-x · Cited by 18