Raman, Rajiv

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

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 7,697

Artificial intelligence and deep learning in ophthalmology

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 1,554

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

A deep learning system for predicting time to progression of diabetic retinopathy

Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 339

Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

Gulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 303

Integrated image-based deep learning and language models for primary diabetes care

Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 250

Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program

Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 233

Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban–rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study

Raman, R., Vasconcelos, J. C., Rajalakshmi, R., Prevost, A. T., Ramasamy, K., Mohan, V., Mohan, D., Rani, P. K., Conroy, D., Das, T., et al. (2022) · The Lancet Global Health

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 106

Analysis of a comprehensive diabetic retinopathy screening model for rural and urban diabetics in developing countries

Rani, P. K., Raman, R., Sharma, V., Mahuli, S. V., Tarigopala, A., Sudhir, R. R., Kumaramanickavel, G., & Sharma, T. (2007) · British Journal of Ophthalmology

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 51

The Tele-Screening Model for Diabetic Retinopathy: Evaluating the Influence of Mydriasis on the Gradability of a Single-Field 45° Digital Fundus Image

Raman, R., Rani, P. K., Mahajan, S., Paul, P., Gnanamoorthy, P., Krishna, M. S., & Sharma, T. (2007) · Telemedicine and e-Health

Retinal Imaging and AnalysisOphthalmology and Visual Health Research

Cited by 22