Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25165, first published .
Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Journals

  1. Betzler B, Rim T, Sabanayagam C, Cheng C. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Frontiers in Digital Health 2022;4 View
  2. Peng Q, Tseng R, Tham Y, Cheng C, Rim T. Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review. Asia-Pacific Journal of Ophthalmology 2022;11(2):126 View
  3. Tseng R, Rim T, Shantsila E, Yi J, Park S, Kim S, Lee C, Thakur S, Nusinovici S, Peng Q, Kim H, Lee G, Yu M, Tham Y, Bakhai A, Leeson P, Lip G, Wong T, Cheng C. Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank. BMC Medicine 2023;21(1) View
  4. Iqbal S, Khan T, Naveed K, Naqvi S, Nawaz S. Recent trends and advances in fundus image analysis: A review. Computers in Biology and Medicine 2022;151:106277 View
  5. Iao W, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics 2023;13(5):900 View
  6. Yi J, Rim T, Park S, Kim S, Kim H, Lee C, Kim H, Lee G, Lim J, Tan Y, Yu M, Tham Y, Bakhai A, Shantsila E, Leeson P, Lip G, Chin C, Cheng C. Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores. European Heart Journal - Digital Health 2023;4(3):236 View
  7. Khan R, Surya J, Roy M, Swathi Priya M, Mohan S, Raman S, Raman A, Vyas A, Raman R. Use of artificial intelligence algorithms to predict systemic diseases from retinal images. WIREs Data Mining and Knowledge Discovery 2023;13(5) View
  8. Hassan M, Zhang H, Fateh A, Ma S, Liang W, Shang D, Deng J, Zhang Z, Lam T, Xu M, Huang Q, Yu D, Zhang C, You Z, Pang W, Yang C, Qin P. Retinal disease projection conditioning by biological traits. Complex & Intelligent Systems 2023 View
  9. Lee C, Rim T, Kang H, Yi J, Lee G, Yu M, Park S, Hwang J, Tham Y, Wong T, Cheng C, Kim D, Kim S, Park S. Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI. Journal of the American Medical Informatics Association 2023;31(1):130 View
  10. Chikumba S, Hu Y, Luo J. Deep learning-based fundus image analysis for cardiovascular disease: a review. Therapeutic Advances in Chronic Disease 2023;14 View
  11. Famiglini L, Campagner A, Barandas M, La Maida G, Gallazzi E, Cabitza F. Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems. Computers in Biology and Medicine 2024;170:108042 View
  12. Carrillo-Larco R. Recognition of Patient Gender: A Machine Learning Preliminary Analysis Using Heart Sounds from Children and Adolescents. Pediatric Cardiology 2024 View
  13. Yang Y, Chen X, Lin H. Privacy preserving technology in ophthalmology. Current Opinion in Ophthalmology 2024;35(6):431 View
  14. Ghenciu L, Dima M, Stoicescu E, Iacob R, Boru C, Hațegan O. Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases. Biomedicines 2024;12(9):2150 View

Books/Policy Documents

  1. Singh A, Garza E, Chopra A, Vepakomma P, Sharma V, Raskar R. Computer Vision – ECCV 2022. View