Mackay DD, Garza PS, Bruce BB, Newman NJ, Biousse V. The demise of direct ophthalmoscopy: a modern clinical challenge. Neurol Clin Pract. 2015;5(2):150–7. https://doi.org/10.1212/CPJ.0000000000000115.
Article PubMed PubMed Central Google Scholar
Bruce BB, Newman NJ, Perez MA, Biousse V. Non-mydriatic ocular fundus photography and telemedicine: Past, present and future. Neuroophthalmology. 2013;37(2):51–7. https://doi.org/10.3109/01658107.2013.773451.
Lin DY, Blumenkranz MS, Brothers RJ, Grosvenor DM. The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am J Ophthalmol. 2002;134:204–13. https://doi.org/10.1016/s0002-9394(02)01522-2.
Lin MY, Ray HJ, Pendley AM, Benard-Seguin E, Okrent Smolar AL, Rodriguez Duran M, et al. Emergency department (ED) non-mydriatic fundus photography expedites care for patients referred for papilledema. Ophthalmology. 2025;132(7):823–9. https://doi.org/10.1016/j.ophtha.2025.01.024.
Rahmani AM, Yousefpoor E, Yousefpoor MS, Mehmood Z, Haider A, Hosseinzadeh M, et al. Machine learning (ML) in medicine: review, applications, and challenges. Mathematics. 2021;9(22):2970. https://doi.org/10.3390/math9222970.
Chiang CYN, Milea D, Girard MJA. Empowering optical coherence tomography with AI: A new era for neuro-ophthalmic and neurological disorders. In: Grzybowski A, Barboni P, editors. OCT and imaging in central nervous system diseases: the eye as a window to the brain. Springer Nature Switzerland; 2025. p. 699-736.
Li T, Bo W, Hu C, Kang H, Liu H, Wang K, et al. Applications of deep learning in fundus images: a review. Med Image Anal. 2021;69:101971. https://doi.org/10.1016/j.media.2021.101971.
Devalla SK, Renukanand PK, Sreedhar BK, Subramanian G, Zhang L, Perera S, et al. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed Opt Express. 2018;9(7):3244–65. https://doi.org/10.1364/BOE.9.003244.
Article PubMed PubMed Central Google Scholar
Devalla SK, Pham TH, Panda SK, Zhang L, Subramanian G, Swaminathan A, et al. Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning. Biomed Opt Express. 2020;11(11):6356–78. https://doi.org/10.1364/BOE.395934.
Article PubMed PubMed Central Google Scholar
Zhang C, Wu H, Ling S, Dong Z, Dong L, Zhang R, et al. Artificial intelligence-based retinal vascular fractal dimension quantification and related factors: a retrospective study from the Beijing Eye Study. Intell Med Published online Dec. 2025;12. https://doi.org/10.1016/j.imed.2025.12.002.
Xu Y, Liu H, Sun R, Wang H, Huo Y, Wang N, et al. Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma. Heliyon. 2024;10(13):e33813. https://doi.org/10.1016/j.heliyon.2024.e33813.
Article PubMed PubMed Central Google Scholar
Zhou HP, Toyama T, Mihara G, Nakajima K, Fujino R, Nagahara M, et al. A novel method of quantifying retinal vascular tortuosity in retinopathy of prematurity. Sci Rep. 2025;15(1):45005. https://doi.org/10.1038/s41598-025-29065-4.
Article CAS PubMed PubMed Central Google Scholar
Russakoff DB, Mannil SS, Oakley JD, Ran AR, Cheung C, Dasari S, et al. A 3D deep learning system for detecting referable glaucoma using full OCT macular cube scans. Transl Vis Sci Technol. 2020;9(2):12. https://doi.org/10.1167/tvst.9.2.12.
Article PubMed PubMed Central Google Scholar
Chiang CYN, Braeu FA, Chuangsuwanich T, Tan RKY, Chua J, Schmetterer, et al. Are macula or optic nerve head structures better at diagnosing glaucoma? An answer using artificial intelligence and wide-field optical coherence tomography. Transl Vis Sci Technol. 2024;13(1):5. https://doi.org/10.1167/tvst.13.1.5.
Article PubMed PubMed Central Google Scholar
Chen Z, Shemuelian E, Wollstein G, Wang Y, Ishikawa H, Schuman JS. Segmentation-free OCT-volume-based deep learning model improves pointwise visual field sensitivity estimation. Transl Vis Sci Technol. 2023;12(6):28. https://doi.org/10.1167/tvst.12.6.28.
Article PubMed PubMed Central Google Scholar
Rasel RK, Wu F, Chiariglione M, Choi SS, Doble N, Gao XR. Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection. Sci Rep. 2024;14(1):11758. https://doi.org/10.1038/s41598-024-62411-6.
Article CAS PubMed PubMed Central Google Scholar
Chiang CYN, Wang X, Gardiner SK, Buist M, Girard MJA. Impact of optic nerve tortuosity, globe proptosis, and size on retinal ganglion cell thickness across general, glaucoma, and myopic Populations. Invest Ophthalmol Vis Sci. 2025;66(6):4. https://doi.org/10.1167/iovs.66.6.4.
Article PubMed PubMed Central Google Scholar
Paschali M, Conjeti S, Navarro F, Navab N. Generalizability vs. robustness: investigating medical imaging networks using adversarial examples. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention – MICCAI 2018. Springer International Publishing; 2018. p. 493-501.
Tran AT, Zeevi T, Payabvash S. Strategies to improve the robustness and generalizability of deep learning segmentation and classification in neuroimaging. BioMedInformatics. 2025;5(2):20. https://doi.org/10.3390/biomedinformatics5020020.
Article PubMed PubMed Central Google Scholar
Wang S, Shen W, Gao Z, Jiang X, Wang Y, Li Y, et al. Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms. Front Med. 2024;11:1418048. https://doi.org/10.3389/fmed.2024.1418048.
Cheong H, Krishna Devalla S, Chuangsuwanich T, Tun TA, Wang X, et al. OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head. Biomed Opt Express. 2021;12(3):1482–98. https://doi.org/10.1364/BOE.412156.
Article PubMed PubMed Central Google Scholar
Cheong H, Devalla SK, Pham TH, Zhang L, Tun TA, Wang X, et al. DeshadowGAN: A deep learning approach to remove shadows from optical coherence tomography images. Transl Vis Sci Technol. 2020;9(2):23. https://doi.org/10.1167/tvst.9.2.23.
Article PubMed PubMed Central Google Scholar
Tajmirriahi M, Kafieh R, Amini Z, Rabbani H. A lightweight mimic convolutional auto-encoder for denoising retinal optical coherence tomography images. IEEE Trans Instrum Meas. 2021;70:1–8. https://doi.org/10.1109/TIM.2021.3072109.
Müller D, Soto-Rey I, Kramer F. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res Notes. 2022;15:210. https://doi.org/10.1186/s13104-022-06096-y.
Article PubMed PubMed Central Google Scholar
Kocak B, Klontzas ME, Stanzione A, Meddeb A, Demircioğlu A, Bluethgen C, et al. Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations. Eur J Radiol Artif Intell. 2025;3:100030. https://doi.org/10.1016/j.ejrai.2025.100030.
Yu Z, Chen R, Gui P, Wang W, Razzak I, Alinejad-Rokny H, et al. A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning. npj Digit Med. 2025;8(1):344. https://doi.org/10.1038/s41746-025-01751-7.
Article PubMed PubMed Central Google Scholar
Thompson AC, Jammal AA, Berchuck SI, Mariottoni EB, Medeiros FA. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans. JAMA Ophthalmol. 2020;138(4):333–9. https://doi.org/10.1001/jamaophthalmol.2019.5983.
Article PubMed PubMed Central Google Scholar
Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. A feature agnostic approach for glaucoma detection in OCT volumes. PLoS ONE. 2019;14(7):e0219126. https://doi.org/10.1371/journal.pone.0219126.
Article CAS PubMed PubMed Central Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.
Article CAS PubMed Google Scholar
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing; 2015. p. 234-241.
Xiao Y, Lin Q, Xiang Z, Ding X, He Z, Zhang Z. Multi-scale attention Unet: an innovative approach for segmentation of optic disc and optic cup in early detection of retinopathy. Ophthalmol Sci. 2026;101118. https://doi.org/10.1016/j.xops.2026.101118.
Girard MJA, Panda S, Tun TA, Wibroe EA, Najjar RP, Aung T, et al. Discriminating between papilledema and optic disc drusen using 3D structural analysis of the optic nerve head. Neurology. 2023;100(2):e192–202. https://doi.org/10.1212/WNL.0000000000201350.
Comments (0)