DOI: 10.57760/sciencedb.dmh.00616
title: Development and Validation of a Deep Learning Model for Macular Hole Staging from Optical Coherence Tomography
title: 基于深度学习的OCT图像黄斑裂孔分期诊断模型构建与验证

description: Objective: To explore the use of artificial intelligence (AI) deep learning algorithms to construct an automated identification model for macular hole (MH) staging and intelligent assisted diagnosis and treatment system based on fundus optical coherence tomography (OCT) images. Method: Based on the image data of macular hole diagnosed by OCT examination at Sichuan Eye Hospital from July 2019 to October 2024, combined with manual and procedural screening methods, images with annotation errors and high repetition were removed. Finally, a total of 6243 representative and effective samples were retained and included in the study. By constructing models such as ResNet series and Swin Transformer, and comparing the performance differences of different fine-tuning depths and training strategies under a unified preprocessing and five fold cross validation framework; The main evaluation indicators are accuracy, precision, recall, F1 score, specificity, and AUC value, supplemented by confusion matrix and ROC curve for multidimensional validation. The experimental results showed that the training set validation accuracy of the Swin Transformer Tiny (Swin-T) model built on the ViT architecture was 99%, but the highest accuracy on the test set did not exceed 90%. Therefore, the ResNet-18 model based on CNN architecture was ultimately evaluated and tested under five fold cross validation. The overall accuracy of each fold was 97.84%, the average accuracy was 97.11%, the average recall or sensitivity was 96.08%, the average F1 score was 96.54%, the average specificity was 99.29%, and the average AUC value was 99.78%. Conclusion: The macular hole staging method based on ResNet-18 model proposed in this article achieves accurate and stable judgment of macular hole staging in fundus OCT images while ensuring lightweight model structure and training efficiency, and has strong potential for clinical auxiliary diagnostic applications. 
description: Objective: To explore the use of artificial intelligence (AI) deep learning algorithms to construct an automated identification model for macular hole (MH) staging and intelligent assisted diagnosis and treatment system based on fundus optical coherence tomography (OCT) images. Method: Based on the image data of macular hole diagnosed by OCT examination at Sichuan Eye Hospital from July 2019 to October 2024, combined with manual and procedural screening methods, images with annotation errors and high repetition were removed. Finally, a total of 6243 representative and effective samples were retained and included in the study. By constructing models such as ResNet series and Swin Transformer, and comparing the performance differences of different fine-tuning depths and training strategies under a unified preprocessing and five fold cross validation framework; The main evaluation indicators are accuracy, precision, recall, F1 score, specificity, and AUC value, supplemented by confusion matrix and ROC curve for multidimensional validation. The experimental results showed that the training set validation accuracy of the Swin Transformer Tiny (Swin-T) model built on the ViT architecture was 99%, but the highest accuracy on the test set did not exceed 90%. Therefore, the ResNet-18 model based on CNN architecture was ultimately evaluated and tested under five fold cross validation. The overall accuracy of each fold was 97.84%, the average accuracy was 97.11%, the average recall or sensitivity was 96.08%, the average F1 score was 96.54%, the average specificity was 99.29%, and the average AUC value was 99.78%. Conclusion: The macular hole staging method based on ResNet-18 model proposed in this article achieves accurate and stable judgment of macular hole staging in fundus OCT images while ensuring lightweight model structure and training efficiency, and has strong potential for clinical auxiliary diagnostic applications.