Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi.

Abstract

Objective: The purpose of this study is to summarize the results from 3 experimental studies into the use of artificial intelligence to classify and segment colour fundus images with choroidal nevi.

Study design: This study is based on a secondary analysis of colour fundus images taken of patients receiving usual clinical care from the Alberta Ocular Brachytherapy Program.

Methods: High-resolution colour fundus images were labeled by experienced ocular oncologists. In experimental study 1, four pre-trained models (ResNet 50, VGG-19, VGG-16, and AlexNet) were evaluated for their ability to classify images based on the presence of choroidal nevi. In experimental study 2, the performance of 3 patch-based models to classify images based on the presence of choroidal nevi were compared. In experimental study 3, four convolutional neural network models were developed to segment the images. In experimental studies 1 and 2, performance was measured using accuracy, precision, recall, F1 score, and AUC. In experimental study 3, IoU and Dice measures were used to evaluate performance.

Results: A total of 591 labelled colour fundus images were used for analysis. In experimental study 1, VGG-16 showed the best accuracy, AUC, and recall, but lower precision in classifying images. In experimental study 2, the patched approached enhanced with artifact and contrast outperformed the others in classifying images. In experimental study 3, a voting-based Ensemble model excelled in segmenting the part of images with nevi.

Conclusions: It is feasible to train AI models to identify choroidal nevi in colour fundus images.