PREDICTING VISUAL OUTCOME AFTER SURGERY IN PATIENTS WITH IDIOPATHIC EPIRETINAL MEMBRANE USING A NOVEL CONVOLUTIONAL NEURAL NETWORK

Author:

Yeh Tsai-Chu12ORCID,Chen Shih-Jen12,Chou Yu-Bai12,Luo An-Chun3,Deng Yu-Shan3,Lee Yu-Hsien3,Chang Po-Han3,Lin Chun-Ju3,Tai Ming-Chi34,Chen Ying-Chi5,Ko Yu-Chieh12ORCID

Affiliation:

1. Department of Ophthalmology, Taipei Veterans General Hospital, Taipei City, Taiwan;

2. Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan;

3. Industrial Technology Research Institute, Taipei City, Taiwan;

4. Department of Materials Science and Engineering, National Tsing-Hua University, Taipei City, Taiwan; and

5. Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan.

Abstract

Purpose: To develop a deep convolutional neural network that enables the prediction of postoperative visual outcomes after epiretinal membrane surgery based on preoperative optical coherence tomography images and clinical parameters to refine surgical decision making. Methods: A total of 529 patients with idiopathic epiretinal membrane who underwent standard vitrectomy with epiretinal membrane peeling surgery by two surgeons between January 1, 2014, and June 1, 2020, were enrolled. The newly developed Heterogeneous Data Fusion Net was introduced to predict postoperative visual acuity outcomes (improvement ≥2 lines in Snellen chart) 12 months after surgery based on preoperative cross-sectional optical coherence tomography images and clinical factors, including age, sex, and preoperative visual acuity. The predictive accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network model were evaluated. Results: The developed model demonstrated an overall accuracy for visual outcome prediction of 88.68% (95% CI, 79.0%–95.7%) with an area under the receiver operating characteristic curve of 97.8% (95% CI, 86.8%–98.0%), sensitivity of 87.0% (95% CI, 67.9%–95.5%), specificity of 92.9% (95% CI, 77.4%–98.0%), precision of 0.909, recall of 0.870, and F1 score of 0.889. The heatmaps identified the critical area for prediction as the ellipsoid zone of photoreceptors and the superficial retina, which was subjected to tangential traction of the proliferative membrane. Conclusion: The novel Heterogeneous Data Fusion Net demonstrated high accuracy in the automated prediction of visual outcomes after weighing and leveraging multiple clinical parameters, including optical coherence tomography images. This approach may be helpful in establishing personalized therapeutic strategies for epiretinal membrane management.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Ophthalmology,General Medicine

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