Ye-Ting Lin,1 Xu Xiong,1 Ying-Ping Zheng,2 Qiong Zhou1 

1Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 2Department of Product Design, Jiangxi Normal University, Nanchang, Jiangxi, People’s Republic of China

Correspondence: Qiong Zhou, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zheng Street, Donghu District, Nanchang, Jiangxi, People’s Republic of China, Email qiongzd06@163.com

Background: Idiopathic macular hole is an ophthalmic disease that seriously affects vision, and its early diagnosis and treatment have important clinical significance to reduce the occurrence of blindness. At present, OCT is the gold standard for diagnosing this disease, but its application is limited due to the need for professional ophthalmologist to diagnose it. The introduction of artificial intelligence will break this situation and make its diagnosis efficient, and how to build an effective predictive model is the key to the problem, and more clinical trials are still needed to verify it.
Objective: This study aims to evaluate the role of deep learning systems in Idiopathic Macular Hole diagnosis, grading, and prediction.
Methods: A single-center, retrospective study used binocular OCT images from IMH patients at the First Affiliated Hospital of Nanchang University (November 2019 - January 2023). A deep learning algorithm, including traditional omics, Resnet101, and fusion models incorporating multi-feature fusion and transfer learning, was developed. Model performance was evaluated using accuracy and AUC. Logistic regression analyzed clinical factors, and a nomogram predicted surgical risk. Analysis was conducted with SPSS 22.0 and R 3.6.3. P < 0.05 was statistically significant.
Results: Among 229 OCT images, the traditional omics, Resnet101, and fusion models achieved accuracies of 93%, 94%, and 95%, respectively, in the training set. In the test set, the fusion model and Resnet101 correctly identified 39 images, while the traditional omics model identified 35. The nomogram had a C-index of 0.996, with macular hole diameter most strongly associated with surgical risk.
Conclusion: The deep learning system with transfer learning and multi-feature fusion effectively diagnoses and grades IMH from OCT images.

Keywords: transfer learning, deep learning, optical coherence tomography, idiopathic, macular hole, multi-feature, grading