Artificial Intelligence in Cardiology

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Dermatologist-level classification of skin cancer with deep neural networks

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The perceptron: A probabilistic model for information storage and organization in the brain.

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Mastering the game of Go without human knowledge

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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

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

Serkan Kiranyaz, B., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,997

Predicting good probabilities with supervised learning

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Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710

Potential of artificial intelligence in cardiac surgery: a systematic review

Shatskiy, A. S., Masyutina, S. E., & Mamalyga, M. L. (2026) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2026-6878 · Cited by 1

A hybrid ensemble stacking and explainable machine learning framework enhanced with bald eagle search optimization for coronary artery disease risk prediction

Napa, K. K., Murugan, S., Murugan, J. S., Sathya, S., Balamurugan, A. G., & Nageswari, D. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01354-5 · Cited by 1

Digital Technologies in Cardiology: From Continuous Monitoring to Predictive Medicine

Tudor, S. S., Fertu, I. D., Dumitru, C. N., Vîlceleanu, B.-V., Popescu, A. M., Dumitrescu, S. I., & Munteanu, A. E. (2026) · Romanian Journal of Military Medicine · doi:10.55453/rjmm.2026.129.3.7 · Cited by 1

Cluster analysis: an overview of traditional and novel approaches for health researchers

Dou, B., Vandenberk, B., & Moons, P. (2026) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvag100 · Cited by 1

Lower-Leg muscle index Identifies High-Risk Peripheral artery disease Patients: Mortality prediction via Machine learning

Zhao, W., Cao, Y., Guo, Y., Zhang, X., Zhang, B., Ren, H., 陈作观, 刁永鹏, & Li, Y. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112991 · Cited by 1

Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology

Tupu, A. E., Tudor, S. S., Dumitru, C. N., Ștefan, C. S., & Ferţu, D. I. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166398 · Cited by 1

Modeling Life-Course Socioeconomic Changes and Blood Pressure in Adults: Time for Transdisciplinary Approach

Bharti, S., & Bharti, B. (2019) · American Journal of Hypertension · doi:10.1093/ajh/hpz156 · Cited by 1

The Impact of Non-coding RNA Networks on Disease Comorbidity: Cardiometabolic Diseases, Inflammatory Diseases, and Cancer

Holvoet, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-68844-8_10 · Cited by 1

Patient Selection for Biventricular Cardiac Resynchronization Therapy, His Bundle Pacing, and Left Bundle Branch Pacing

Sanders, D. J., & Krishnan, K. (2021) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-021-00684-6 · Cited by 1

Unlocking the Potential of Natural Language Processing and Healthchatbots in Healthcare Management

Sivarethinamohan, R., & Sujatha, S. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-6332-1_40 · Cited by 1

Cardiac Ultrasound Imaging: The Role of Artificial Intelligence

Seetharam, K., & Sengupta, P. P. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_38 · Cited by 1

Prehospital STEMI Referral Systems and Sex-Related Bias in Canada: A National Survey

Boivin‐Proulx, L., Pacheco, C., Matteau, A., Mansour, S., & Potter, B. J. (2022) · CJC Open · doi:10.1016/j.cjco.2022.05.006 · Cited by 1

Towards Network Medicine: Implementation of Panomics and Artificial Intelligence for Precision Medicine

Barber, R. D., & Kroeger, K. (2022) · Healthcare delivery in the information age · doi:10.1007/978-3-030-95675-2_3 · Cited by 1

Post-cardiac arrest PCI is underutilized among cancer patients: Machine learning augmented nationally representative case-control study of 30 million hospitalizations

Kim, J. W., Monlezun, D. J., Park, J. K., Chauhan, S., Balanescu, D. V., Koutroumpakis, E., Palaskas, N. L., Kim, P. Y., Hassan, S. A., Botz, G. H., et al. (2022) · Resuscitation · doi:10.1016/j.resuscitation.2022.07.032 · Cited by 1

Fibonacci Numbers as Hyperparameters for Image Dimension of a Convolu-tional Neural Network Image Prognosis Classification Model of COVID X-ray Images

Torralba, E. M. (2022) · International Journal of Multidisciplinary Applied Business and Education Research · doi:10.11594/ijmaber.03.09.11 · Cited by 1

Artificial intelligence in coronary physiology: where do we stand?

Mody, R., Dash, D., & Mody, D. (2022) · Journal of Transcatheter Interventions · doi:10.31160/jotci202230a20220009 · Cited by 1

Unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care

Anand, R., Reddy, S. R. N., & Kumar, D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5868-7_35 · Cited by 1

IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR CARDIAC ARREST PREDICTION

Ekuma, I. C., Ndubuka, G. I., Azeez, T. O., Nosiri, O. C., Okafor, S. A., Ekedigwe, M. C., Otuu, C. A., & Onwukamuche, C. K. (2023) · Engineering and Technology Journal · doi:10.47191/etj/v8i2.01 · Cited by 1

Novel Visual Effects in Computer Vision of Animation Film Based on Artificial Intelligence

Reddy, V. S., Kathiravan, M., & Reddy, V. L. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-5166-6_67 · Cited by 1

Prediction of Coronary Artery Disease using Machine Learning – A Comparative study of Algorithms

Ramanathan, G., & Jagadeesha, S. N. (2023) · International Journal of Health Sciences and Pharmacy · doi:10.47992/ijhsp.2581.6411.0116 · Cited by 1

Empowering Healthcare Systems Using Machine Learning: Working, Classification and Application

Thakur, K. R., & Bhushan, B. (2023) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis60361.2023.10425214 · Cited by 1

Огляд методів аналізу даних та методів машинного навчання при прогнозуванні ішемічної хвороби серця

Zemlianyi, O. D., & Baybuz, O. H. (2023) · Actual problems of automation and information technology · doi:10.15421/432311 · Cited by 1

Performance Evaluation of Machine Learning Models for Multiple Chronic Disease Diagnosis Using Symptom Data

Singh, K., Dhawan, S., & Mehla, D. (2024) · Automatic Control and Computer Sciences · doi:10.3103/s0146411624700093 · Cited by 1

Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes

Cordero, A., Bertomeu‐González, V., Segura, J., Morales, J., Álvarez, B. Á., Escribano, D., Rodríguez‐Mañero, M., Cid-Álvarez, B. A., García‐Acuña, J. M., González‐Juanatey, J. R., et al. (2024) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2024.01.038 · Cited by 1

Internet of Medical Things: A revolution in cardiovascular disease Prevalence and Diagnosis

Mehta, S., Reddy, N. A. S., Sharma, A., Mahajan, A., & Sharma, A. (2023) · International Conference on Intelligent Computing, Communication & Convergence (ICI3C) · doi:10.1109/ici3c60830.2023.00046 · Cited by 1

Artificial intelligence-assisted data fusion

Ding, X., Zhang, Y., Haick, H., & Zhang, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15684-7.00042-7 · Cited by 1

Clinical Application of AI-ECG

Xue, Z.-K., Chen, K., Li, X.-M., Liu, T., Xie, J., Guo, S., Song, W.-H., Chu, H., Fu, G., Zhang, N., et al. (2024) · Springer eBooks · doi:10.1007/978-981-97-8359-5_3 · Cited by 1

Development of a surrogate model for predicting atherosclerotic plaque progression based on agent based modeling data

Spahić, L., & Filipović, N. (2025) · Technology and Health Care · doi:10.1177/09287329241309771 · Cited by 1

Enhancing Personalized Medicine Using Data Analytics and Artificial Intelligence

Tejpal, K., & Singh, S. P. (2024) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i61595.2024.10828668 · Cited by 1

Predicting Cardiac Magnetic Resonance-Derived Ejection Fraction from Echocardiogram Via Deep Learning Approach in Tetralogy of Fallot

Adhikari, A., Wesley, G. V., Nguyen, M. B., Doan, T. T., Rao, M. Y., Parthiban, A., Patterson, L. W., Adhikari, K., Ouyang, D., Heinle, J. S., et al. (2025) · Pediatric Cardiology · doi:10.1007/s00246-025-03802-y · Cited by 1

Risk Scores and Neural Networks in Heart Failure Patients

Juárez‐Orozco, L. E. (2021) · Revista Argentina de Cardiología · doi:10.7775/rac.es.v89.i5.20449 · Cited by 1

Machine Learning Application in Different Imaging Modalities for Detection of Obstructive Coronary Artery Disease and Outcome Prediction: A Systematic Review and Meta-Analysis

McGranaghan, P., Schöppenthau, D., Popp, A., Saxena, A., Kothakapu, S., Rubens, M., Jiménez, G., Gordillo, P., Veledar, E., Al, A. A. E., et al. (2025) · Hearts · doi:10.3390/hearts6030021 · Cited by 1

Machine Learning in Early Skin Cancer Diagnosis: A Crucial Step Toward Achieving Universal Health Coverage (SDG 3)

Duggal, S., & Duggal, S. (2025) · International Conference on Computing Technologies (ICOCT) · doi:10.1109/icoct64433.2025.11118410 · Cited by 1

Big Data in Cardiovascular Disease

Lima, F. V., Russell, R. R., & Druz, R. S. (2019) · Current Epidemiology Reports · doi:10.1007/s40471-019-00209-1 · Cited by 1

Artificial Intelligence in Subspecialties

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00008-1 · Cited by 1

Medical Ethics in the Era of Artificial Intelligence Based on Medical Big Data

Na, H.-R., & Kim, H. (2020) · Journal of Korean Diabetes · doi:10.4093/jkd.2020.21.3.126 · Cited by 1

Construction of Budget Evaluation Index System for Application-Oriented Undergraduate Universities Based on Artificial Intelligence

Wang, D., & Song, G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4572-0_80 · Cited by 1

Design and Implementation of Personalized Portal Web site Based on Artificial Intelligence

Zhang, H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0115-6_202 · Cited by 1

Machine Learning in Cardiovascular Disorders

Ghosh, S., & Dasgupta, R. (2022) · Springer eBooks · doi:10.1007/978-981-16-8881-2_13 · Cited by 1

Leveraging “Big Data” for the Design and Execution of Clinical Trials

Greene, S. J., Samsky, M. D., & Hernandez, A. F. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_161 · Cited by 1

A Review on Predictive Models Designed From Artificial Intelligence Techniques in the Wastewater Treatment Process

Mohan, V. G., Ameedeen, M. A., Vijayan, B. L., & Ali, A.-F. M. (2022) · Advances in mechatronics and mechanical engineering (AMME) book series · doi:10.4018/978-1-6684-5887-7.ch013 · Cited by 1

HRCT chest analysis for detection of pulmonary arterial hypertension in COVID-19 patients using convolutional neural networks

Nazir, A., Naaz, R., Qureshi, S., & Nazir, N. (2022) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat55367.2022.9972033 · Cited by 1

A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients

Polz, M., Bergmoser, K. M., Horn, M., Schörghuber, M., Šajić, J. L., Rienmüller, T. M., & Baumgärtner, C. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1101966 · Cited by 1

Reading the Digital Tea Leaves

Bunte, M. C., & Baron, S. J. (2020) · Circulation Cardiovascular Interventions · doi:10.1161/circinterventions.120.010114 · Cited by 1

Environmental Regulation of the Heart: The Role of Non-Coding RNA and Epigenetics in Influencing Mitochondrial and Cellular Health

Hathaway, Q. A. (2019) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.4052 · Cited by 1

A Smart Web Application for Symptom‐Based Disease Detection and Prediction Using State‐of‐the‐Art ML and ANN Models

Saleh, P. R., & Saikia, E. (2021) · Wiley eBooks · doi:10.1002/9781119818717.ch4 · Cited by 1

Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low‐Concentration CVD Biomarkers

Laad, M. S., Sajna, M. S., Sadasivuni, K. K., & Waseem, S. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch10 · Cited by 1

Journal on Artificial intelligencePreprint

Asemota, K. U., & Ifekanandu, C. C. (2024) · doi:10.31124/advance.23247530.v1 · Cited by 1

Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang HospitalPreprint

Seesuwan, N., Phetkub, N., Rojanasumapong, A., & Chuenjai, N. (2024) · Preprints.org · doi:10.20944/preprints202408.0428.v1 · Cited by 1

Data-driven discovery of core sleep biomarkers for predicting early cardiometabolic risk in a healthy population using machine learningPreprint

Yu, Z. (2025) · medRxiv · doi:10.1101/2025.06.15.25329640 · Cited by 1

Selection of abstracts from the scientific sessions of the Society of Nuclear Medicine and Molecular Imaging, Philadelphia PA June 23–26, 2018

Kwieciński, J., Lee, Y. J., & Slomka, P. J. (2018) · Journal of Nuclear Cardiology · doi:10.1007/s12350-018-1405-6

Learning to think like Machines

Khalil, M. J., Piccirilli, M., & Sengupta, P. P. (2018) · Indian Heart Journal · doi:10.1016/j.ihj.2018.08.003

Clinical Research and Evidence-Based Medicine

Cokkinos, D. V. (2019) · Springer eBooks · doi:10.1007/978-3-319-98186-4_3

TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applicationsPreprint

Heber, F., Trstanova, Z., & Leimkuhler, B. (2019) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.48550/arxiv.1903.08640 · Cited by 1

High-Risk Percutaneous Coronary Intervention in Patients With Stable Ischemic Heart Disease

Weintraub, W. S. (2019) · Cardiovascular revascularization medicine · doi:10.1016/j.carrev.2019.02.009

Application of Artificial Intelligence Approach for Optimizing Management of Road Traffic

Bridge-Nduwimana, C. B., Malaoui, A., & Antari, J. (2019) · Studies in systems, decision and control · doi:10.1007/978-3-030-22773-9_5

Machine Learning Algorithms in Cardiology Domain: A Systematic Review (Preprint)

Kopanitsa, G., Dudchenko, A., & Ganzinger, M. (2019) · JMIR Medical Informatics · doi:10.2196/14784

Intelligently Advancing Cardiovascular Health Care

Puri, R., & Kapadia, S. R. (2019) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2019.07.046

Sparsely Activated Networks: A new method for decomposing and compressing dataPreprint

Bizopoulos, P. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00400

Optimization of the allocation of academic schedules through artificial intelligence techniques

Velásquez, A. M. P., Pérez, T. V., & Puentes-Velásquez, A. M. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1403/1/012019

Data Capture, Data Management, and Quality Control; Single Versus Multicenter Trials

Knust, K., Yesko, L., Case, A., & Bickett, K. (2020) · Springer eBooks · doi:10.1007/978-3-319-52677-5_40-1

A Questionnaire Study on Artificial Intelligence and Its Effects on Individual Health and Wearable Device

Bu, T., & Li, F. (2020) · Springer eBooks · doi:10.1007/978-981-15-3824-7_15

Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Yamini, G., & Ganapathy, G. (2020) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-7998-3591-2.ch011

The Influence of Artificial Intelligence Technology on the Development of Competitive Sports in China

Su, C., & Yu, W. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-53980-1_49

Hamilton County Courthouse, detail

Belden, D. L. (2006)

Data mining of myths, legends and folk tales in the context of artificial intelligence

Zhang, Z., & Yang, J. (2020) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc47916.2020.9171134

Low-Shot Machine Learning for Medical Image Classification

Yip, C. M., Sun, Y., Wang, L., & Wang, Y. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-91683-0_3

Leveraging “Big Data” for the Design and Execution of Clinical Trials

Greene, S. J., Samsky, M. D., & Hernandez, A. F. (2021) · Springer eBooks · doi:10.1007/978-3-319-52677-5_161-1

Are the current colonoscopy recommendations for interval surveillance in patients with polyps enough? Machine learning-augmented propensity score cohort analysis of 1840 patients

Baker, S. E., Monlezun, D. J., Wieghard, N., Whitlow, C. B., & Margolin, D. A. (2021) · Surgical Endoscopy · doi:10.1007/s00464-021-08403-3

Improving Out-of-hospital Cardiac Arrest Outcomes and Automated External Defibrillator Placement Guidelines through Optimization and Predictive Modeling

Sun, C. L. F. (2019) · TSpace (University of Toronto)

Peer Review #3 of "Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.560v0.1/reviews/3

Peer Review #3 of "Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.560v0.2/reviews/3

PERSPECTIVES FOR THE USE OF ARTIFICIAL NEURAL NETWORKS IN FORENSIC MEDICINE (LITERATURE REVIEW)

Gunas, I. V., Bachynskyі, V., Popadynets, O. H., Кіндратів, Е. О., & Kozovyi, R. (2020) · Sudovo-medična ekspertiza · doi:10.24061/2707-8728.1.2020.2

Diagnosing heart problem using machine learning

Juberkhan, P. (2020) · eSource (Dublin Business School)

Impact of Artificial Intelligence in Cardiovascular Disease

Khan, M. A., Ahmed, S., Kumar, P., & Bhatti, D. M. S. (2021) · Wiley eBooks · doi:10.1002/9781119761655.ch14

A Systems-Based Morbidity and Mortality Conference Was Associated With a Transient Reduction in ECG Completion Times

Levy, A. E., Whittington, M. D., Anstett, T. J., Simon, S. T., Wentworth, A., Carter, J. E., & Ho, P. M. (2021) · Quality Management in Health Care · doi:10.1097/qmh.0000000000000319

Classification of Coronary Artery Lesions Based on XGBoost

Chen, R., Wang, J., Pan, J., & Yao, Y. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-7207-1_18

Impact of Statistical Intelligent Analysis on Corona Virus Disease

Saxena, K., & Banodha, U. (2021) · Advances in human services and public health (AHSPH) book series · doi:10.4018/978-1-7998-7176-7.ch005

Internet of Things (IoT) in Monitoring Physiological Parameters

Fuior, R., Băeșu, A. C., & Corciovă, C. (2022) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-92328-0_33

Catching-up on Research Related to AI-based Tools Implementation in Clinical Settings : Viewpoint (Preprint)Preprint

Poba‐Nzaou, P., Kume, N., Kobayashi, S., & Galani, M. (2021) · doi:10.2196/preprints.29401

Looking forward: the future of cardiovascular care

Thompson, D. R., Neubeck, L., & Gallagher, R. D. (2021) · Oxford University Press eBooks · doi:10.1093/med/9780198849315.003.0015

Pericardial Effusion Detection on Post-Mortem Computed Tomography (PMCT) Images using Convolutional Neural Network-based Models: Algorithm Development and Validation (Preprint)Preprint

Kong, H., Rong, J., Bain, C., Bassed, R., Lee, V. C. S., & Zhang, X. (2021) · doi:10.2196/preprints.32615

Automated algorithms in diastology: how to move forward?

Strachinaru, M., & Bosch, J. G. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-021-02505-8

Peer Review #1 of "Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.560v0.1/reviews/1

Peer Review #2 of "Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.560v0.1/reviews/2

Artificial intelligence in echocardiography is here and more to come

Shimoni, O., & Shimoni, S. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-022-02559-2

A Constructive Deep Learning Applied Agent Mining for Supported Categorization Model to Forecast the Hypokinetic Rigid Syndrome (HRS) Illness

Priyadharshini, N., Rozario, J., & Mary, S. D. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9012-9_16

Artificial Intelligence Techniques in Cardiology

and, I. H., & Xu, B. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch6

Key references

(2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00025-1

Artificial Intelligence in Electrocardiology for Arrhythmia Diagnosis

Mukai, Y., Tohyama, T., & Sakamoto, K. (2022) · Circulation Journal · doi:10.1253/circj.cj-22-0229

Artificial intelligence–based neural network for the diagnosis of diabetes and COVID

Roland, G., Kumar, N., Gururaj, B., Richa, R., Bobade, S. D., & Lourens, M. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns1.8606

Advancement of Technology and Innovation for Future Cardiovascular Care

Young, J. B. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-816861-5.00019-8

Design of Nanchang VI Visual Image Recognition System based on Artificial Intelligence

Yang, S. Q., & Tong, C. (2022) · IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) · doi:10.1109/icetci55101.2022.9832383

Definition of a Sensitivity Profile for Drug Treatment and Identification on Clinical Biomarkers in Atrial Fibrillation

Nava, A. (2022) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/184778

A Study of Intelligent Paper Grouping Model for Adult Higher Education Based on Random Matrix

Wang, Y. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/2610545

Medicine of the future: a look through the keyhole

Tendera, M. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac523 · Cited by 1

Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Yamini, G., & Ganapathy, G. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7544-7.ch006

Machine learning in modern cardiology

Alekseev, I., & Алексеев, И. А. (2020) · Electronic scientific archive of UrFU (Ural Federal University)

Using of machine learning methods in cardiology

Фирюлина, М. А., & Каширина, И. Л. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1902/1/012135 · Cited by 3

Машинное обучение в современной кардиологии

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