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

Decision Support for Health Professionals (1186) Artificial Intelligence (1373) Clinical Information and Decision Making (1408) Imaging Informatics (194) Machine Learning (1466) Ophthalmology (44)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28868, first published .
A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

Authors of this article:

Eugene Yu-Chuan Kang1, 2 Author Orcid Image ;   Ling Yeung2, 3 Author Orcid Image ;   Yi-Lun Lee4 Author Orcid Image ;   Cheng-Hsiu Wu2, 3 Author Orcid Image ;   Shu-Yen Peng2, 3 Author Orcid Image ;   Yueh-Peng Chen4 Author Orcid Image ;   Quan-Ze Gao4 Author Orcid Image ;   Chihung Lin4 Author Orcid Image ;   Chang-Fu Kuo2, 4 Author Orcid Image ;   Chi-Chun Lai2, 3 Author Orcid Image
Article Authors Cited by (22) Tweetations (3) Metrics

Journals

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  6. Lo J, Kang E, Chen Y, Hsieh Y, Wang N, Chen T, Chen K, Wu W, Hwang Y, Lo F, Lai C, Li T. Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy. Journal of Diabetes Research 2021;2021:1 View
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  8. Lim W, Ho H, Ho H, Chen Y, Lee C, Chen P, Lai F, Jang J, Ko M. Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia. BMC Medical Imaging 2022;22(1) View
  9. Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Frontiers in Cell and Developmental Biology 2023;11 View
  10. He J, Song J, Han Z, Cui M, Li B, Gong Q, Huang W. Multi-spectral transformer with attention fusion for diabetic macular edema classification in multicolor image. Soft Computing 2024;28(7-8):6117 View
  11. Campos A, Lima E, Jacobsen P, Arnould L, Lottenberg S, Maia R, Conci L, Minelli T, Morato A, Dantas-Jr R, Nomura C, Rissoli P, Pimentel S, Serrano Junior C. Association between obstructive coronary disease and diabetic retinopathy: Cross-sectional study of coronary angiotomography and multimodal retinal imaging. Journal of Diabetes and its Complications 2024;38(4):108721 View
  12. Parmar U, Surico P, Singh R, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. Medicina 2024;60(4):527 View
  13. El-Ateif S, Idri A. Multimodality Fusion Strategies in Eye Disease Diagnosis. Journal of Imaging Informatics in Medicine 2024;37(5):2524 View
  14. Martin E, Cook A, Frost S, Turner A, Chen F, McAllister I, Nolde J, Schlaich M. Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs. Eye 2024;38(13):2581 View
  15. Yang M, Yang L, Zhang Q, Xu L, Yang B, Li Y, Cheng X, Zhang F, Liu M, Yu N. Deep learning‐based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk. Medical Physics 2024;51(8):5270 View
  16. Wassan J, Zheng H, Wang H. Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review. Cells 2021;10(11):2924 View
  17. Li Y, Chiu P, Tam V, Lee A, Lam E. Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases. IEEE Transactions on Biomedical Circuits and Systems 2024;18(4):783 View
  18. Lim G, Elangovan K, Jin L. Vision language models in ophthalmology. Current Opinion in Ophthalmology 2024;35(6):487 View
  19. Sendecki A, Ledwoń D, Tuszy A, Nycz J, Wąsowska A, Boguszewska-Chachulska A, Mitas A, Wylęgała E, Teper S. Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score. Biomedicines 2024;12(9):2092 View
  20. Kang C, Lo J, Zhang H, Ng S, Lin J, Scott I, Kalpathy-Cramer J, Liu S, Greenberg P. Artificial intelligence for diagnosing exudative age-related macular degeneration. Cochrane Database of Systematic Reviews 2024;2024(10) View

Books/Policy Documents

  1. Cai L, Jin A, Hinkle J, Xu D, Kuriyan A. Diabetic Macular Edema. View