ArticleFrontiers in medicine2023
Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- OCT-PRO: A Multimodal Model Integrating OCT and Clinical Traits to Predict Postoperative Outcomes in Cataract Patients.Ophthalmology science · 2026Article
- Incremental Predictive Value of Preoperative Macular Optical Coherence Tomography for Postoperative Visual Acuity After Phacoemulsification Cataract Surgery: A Retrospective Cohort Study.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Predicting visual acuity using optical coherence tomography in patients with neovascular age-related macular degeneration.Scientific reports · 2025Article
- Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models.Frontiers in cell and developmental biology · 2025Article
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Authors and funding
12 authors.
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Abstract
Background: To predict postoperative visual acuity (VA) in patients with age-related cataracts using macular optical coherence tomography-based deep learning method. Methods: A total of 2,051 eyes from 2,051 patients with age-related cataracts were included. Preoperative optical coherence tomography (OCT) images and best-corrected visual acuity (BCVA) were collected. Five novel models (I, II, III, IV, and V) were proposed to predict postoperative BCVA. The dataset was randomly divided into a training ( Results: Model V containing preoperative OCT images with horizontal and vertical B-scans, macular morphological feature indices, and preoperative BCVA had a better performance in predicting postoperative VA, with the lowest MAE (0.1250 and 0.1194LogMAR) and RMSE (0.2284 and 0.2362LogMAR), and the highest precision (90.7% and 91.7%), sensitivity (93.4% and 93.8%), accuracy (88% and 89%), F1 (92% and 92.7%) and AUCs (0.856 and 0.854) in the validation and test datasets, respectively. Conclusion: The model had a good performance in predicting postoperative VA, when the input information contained preoperative OCT scans, macular morphological feature indices, and preoperative BCVA. The preoperative BCVA and macular OCT indices were of great significance in predicting postoperative VA in patients with age-related cataracts.
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