McConnell, Michael V.

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 1,865

Routine assessment and promotion of physical activity in healthcare settings: A scientific statement from the American Heart Association

Lobelo, F., Rohm Young, D., Sallis, R., Garber, M. D., Billinger, S. A., Duperly, J., Hutber, A., Pate, R. R., Thomas, R. J., Widlansky, M. E., et al. (2018) · Circulation

Physical Activity and HealthHealth Promotion and Cardiovascular Prevention

Cited by 402

Deep learning predicts hip fracture using confounding patient and healthcare variables

Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., McConnell, M. V., Percha, B., Snyder, T. M., & Dudley, J. T. (2019) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationHip and Femur Fractures

Cited by 290

Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study

McConnell, M. V., Shcherbina, A., Pavlovic, A., Homburger, J. R., Goldfeder, R. L., Waggot, D., Cho, M. K., Rosenberger, M. E., Haskell, W. L., Myers, J., et al. (2017) · JAMA Cardiology

Mobile Health and mHealth ApplicationsPhysical Activity and Health

Cited by 239

The use of smartphones for health research

Dorsey, E. R., “Yvonne” Chan, Y.-F., McConnell, M. V., Shaw, S. Y., Trister, A. D., & Friend, S. H. (2017) · Academic Medicine

Mobile Health and mHealth ApplicationsMobile Crowdsensing and Crowdsourcing

Cited by 154

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: proceedings of the European Society of Cardiology cardiovascular round table

Leclercq, C., Witt, H., Hindricks, G., Katra, R. P., Albert, D., Belliger, A., Cowie, M. R., Deneke, T., Friedman, P., Haschemi, M., et al. (2022) · EP Europace

ECG Monitoring and AnalysisMobile Health and mHealth Applications

Cited by 109

The effect of digital physical activity interventions on daily step count: A randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study

Shcherbina, A., Hershman, S. G., Lazzeroni, L., King, A. C., O'Sullivan, J. W., Hekler, E., Moayedi, Y., Pavlovic, A., Waggott, D., Sharma, A., et al. (2019) · The Lancet Digital Health

Mobile Health and mHealth ApplicationsPhysical Activity and Health

Cited by 90

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Naghavi, M., Reeves, A. P., Atlas, K., Zhang, C., Atlas, T., Henschke, C. I., Yankelevitz, D. F., Budoff, M. J., Li, D., Roy, S. K., et al. (2024) · npj Digital Medicine

Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and Education

Cited by 45

Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores – Part I

Naghavi, M., Azimi, A., Atlas, K., Reeves, A. P., Zhang, C., Mirjalili, S. R., Mozafarybazargany, M., Jolfayi, A. G., Hashemi, A., Atlas, T., et al. (2026) · American Journal of Preventive Cardiology

Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and Education

Cited by 2