Using a deep learning model to predict postoperative visual outcomes of idiopathic epiretinal membrane surgery

Hsin-Le Lin; Po-Chen Tseng; Min-Huei Hsu; Syu-Jyun Peng

doi:10.1016/j.ajo.2025.01.003

Using a deep learning model to predict postoperative visual outcomes of idiopathic epiretinal membrane surgery

Am J Ophthalmol. 2025 Jan 13:S0002-9394(25)00022-4. doi: 10.1016/j.ajo.2025.01.003. Online ahead of print.

Authors

Hsin-Le Lin¹, Po-Chen Tseng², Min-Huei Hsu³, Syu-Jyun Peng⁴

Affiliations

¹ Department of Ophthalmology, Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taiwan.
² Department of Ophthalmology, Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; Department of Special Education, University of Taipei, Taipei, Taiwan; Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
³ Graduate Institute of Data Science, College of Management, Taipei Medical University, Taiwan; Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan.
⁴ Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address: sjpeng2019@tmu.edu.tw.

PMID: 39814096
DOI: 10.1016/j.ajo.2025.01.003

Abstract

Purpose: This study assessed the performance of various deep learning models in predicting the postoperative outcomes of idiopathic epiretinal membrane (ERM) surgery based on preoperative optical coherence tomography (OCT) images.

Design: Validation of algorithms to predict the outcomes of ERM surgery based on OCT data.

Methods: Internal training and validation were performed using 1,392 OCT images from 696 eyes. External testing was performed using 152 OCT images from 76 eyes. This study assessed three deep learning models, including Inception-v3, ResNet-101, and VGG-19. Grad-CAM was employed for hotspot analysis. The dataset was split into a training set (80%) and a validation set (20%). Subjects presenting an improvement of ≥2 lines on the Snellen chart at 1-year post-surgery were classified as pronounced visual improvement, whereas those presenting an improvement of <2 lines were classified as limited visual improvement. Using an external test dataset, we compared assessments by seven ophthalmologists with the prediction of deep learning model. The main outcome measures were recall, specificity, precision, F1 score, accuracy, and area under the receiver operating characteristic curve (AUROC).

Results: ResNet-101 achieved the best overall performance, as evidenced by the following metrics: recall (0.90), specificity (0.90), precision (0.91), F1-score (0.90), accuracy (0.90), and AUROC (0.97). In Grad-CAM heatmap analysis, the logic of ResNet-101 closely resembled that of clinical physicians. Overall, the performance of this deep learning model was significantly better than that of general ophthalmologists and non-retina specialists and was slightly superior to that of retina specialists.

Conclusions: Deep learning based on preoperative OCT images proved highly effective in predicting the outcomes of ERM surgery and elucidating the structural mechanisms underlying the phenomena observed in OCT images.

Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Epiretinal membrane; Optical coherence tomography.