Accessibility settings

This paper is in the following e-collection/theme issue:

Artificial Intelligence (4709) Clinical Information and Decision Making (3667) New Methods (1781) Decision Support for Health Professionals (2207) Machine Learning (3132) Computer-Aided Diagnosis (416)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48142, first published .
Futuristic eye scan with digital interface overlay

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy

Authors of this article:

Jeewoo Yoon1, 2 Author Orcid Image ;   Jinyoung Han1, 3 Author Orcid Image ;   Junseo Ko1, 2 Author Orcid Image ;   Seong Choi1, 2 Author Orcid Image ;   Ji In Park4 Author Orcid Image ;   Joon Seo Hwang5 Author Orcid Image ;   Jeong Mo Han6 Author Orcid Image ;   Daniel Duck-Jin Hwang7, 8 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Lin A, Peng Y, Lin T, Dai J, Li J, Shi T, Ke X, Liao X, Fang D, Chen M, Liang H, Chen S, Xia H, Wang J, Jiang Z, Li T, Liang D, Yu S, Luo J, Gao L, Sun D, Tham Y, Chen X, Chen H. Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography. Advanced Intelligent Systems 2025;7(4) View
  2. Bilal H, Keles A, Bendechache M. Advances in disease detection through retinal imaging: A systematic review. Computers in Biology and Medicine 2025;194:110412 View
  3. Zhang P, Zhang Q, Hu X, Chi W, Yang W. Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review. Ophthalmology and Therapy 2025;14(9):2083 View
  4. Nouri H, Hasan N, Abtahi S, Ahmadieh H, Chhablani J. Deep learning in central serous chorioretinopathy. Survey of Ophthalmology 2026;71(2):718 View
  5. Shojaeinia M, Hosseini A, Naderi M, Baloutch B, Yekta M, Akbarpour L, Moghaddasi H. A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis. BMC Ophthalmology 2025;25(1) View
  6. Zhu Y, Xu Y, Yang W. Review: Algorithmic advances in central serous chorioretinopathy OCT: From classification to segmentation. Biomedical Signal Processing and Control 2026;113:108876 View
  7. Yoon J, Kim T, Han J, Hwang J, Han J, Park J, Song H, Hwang D. Expertise Matters in AI Adoption: A Comparative Study of Retina Specialists and General Ophthalmologists in AI-CAD Adoption. International Journal of Human–Computer Interaction 2026;42(16):13377 View
  8. Jackson N, Brown K, Miller R, Murrow M, Cauley M, Collins B, Novak L, Benda N, Ancker J. Factors influencing the effectiveness of artificial intelligence-assisted decision-making in medicine: a scoping review. Journal of the American Medical Informatics Association 2026;33(5):1054 View
  9. 夏 玉. Research Advances of Artificial Intelligence in Central Serous Chorioretinopathy. Advances in Clinical Medicine 2026;16(03):507 View
  10. Fernández-Vigo J, Valverde-Megías A, Burgos-Blasco B, de Moura Ramos J, Ly-Yang F. Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review. International Journal of Retina and Vitreous 2026;12(1) View
  11. Hagiwara Y, Fitch K, Trapp M. Safety design guidelines for clinician–AI interaction in computer-aided diagnosis systems using system-theoretic framework with explainability validation. Scientific Reports 2026;16(1) View

Conference Proceedings

  1. Shojaeinia M, Moghaddasi H. 2026 International Interdisciplinary Conference on Artificial Intelligence: Engineering, Health, Finance and Humanities (IICAI). AI-CADx in Retinal Disease: A Systematic Review of Explainability, Privacy, and Scalability for Next-Generation Telemedicine View
  2. Avvar P, Nandan D. 2026 IEEE Global Symposium on Emerging and Communication Technologies (GSEACT). Cross-Architecture Fusion for Enhanced Diagnostic Accuracy in Medical Imaging - A Review View