ArticleFrontiers in medicine2022
Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Effect of postoperative nutrient management on macular edema and visual function following phacoemulsification for age-related cataract.American journal of translational research · 2026Article
- Cornea Oculomics: A Clinical Blueprint for Extending Corneal Diagnostics and Artificial Intelligence in Systemic Health Insights.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting Behaviour Patterns in Online and PDF Magazines with AI Eye-Tracking.Behavioral sciences (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: To investigate an artificial intelligence (AI) model performance using multi-source anterior segment optical coherence tomographic (OCT) images in estimating the preoperative best-corrected visual acuity (BCVA) in patients with senile cataract. Design: Retrospective, cross-instrument validation study. Subjects: A total of 2,332 anterior segment images obtained using swept-source OCT, optical biometry for intraocular lens calculation, and a femtosecond laser platform in patients with senile cataract and postoperative BCVA ≥ 0.0 logMAR were included in the training/validation dataset. A total of 1,002 images obtained using optical biometry and another femtosecond laser platform in patients who underwent cataract surgery in 2021 were used for the test dataset. Methods: AI modeling was based on an ensemble model of Inception-v4 and ResNet. The BCVA training/validation dataset was used for model training. The model performance was evaluated using the test dataset. Analysis of absolute error (AE) was performed by comparing the difference between true preoperative BCVA and estimated preoperative BCVA, as ≥0.1 logMAR (AE Outcome Measurements: Mean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and correlation coefficient between true preoperative BCVA and estimated preoperative BCVA. Results: The test dataset MAE, RMSE, and MPE were 0.050 ± 0.130 logMAR, 0.140 ± 0.134 logMAR, and 1.3 ± 13.9%, respectively. The correlation coefficient was 0.969 ( Conclusions: The AI model achieved high-level visual acuity estimation in patients with senile cataract. This quantification method encompassed both visual acuity and cataract severity of OCT image, which are the main indications for cataract surgery, showing the potential to objectively evaluate cataract severity.
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