Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48142, first published .
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

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

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 2025 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