Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images - Li, Dong-Jin; Huang, Bing-Lin; Peng, Yuan (Frontiers in Neuroscience, 2023)
Abstract Purpose: This study combines automatic segmentation and manual fine-tuning with an early fusion method to provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis. Methods: First, 423 high-quality anterior segment images of keratitis were collected in the Department of Ophthalmology of the Jiangxi Provincial People's Hospital (China). The images were divided into fungal keratitis and non-fungal keratitis by a senior ophthalmologist and all images were divided randomly into training and testing sets at the ratio 8:2. Then, two deep-learning models were constructed for diagnosing fungal keratitis. Model 1 included a deep-learning model composed of the densenet 121, mobienet_v2, and squeezentet1_0 models, the least absolute shrinkage and selection operator (LASSO) model, and the multi-layer perception (MLP) classifier. Model 2 included an automatic segmentation program and the deep-learning model already described. Finally, the performance of model 1 and model 2 was compared. Results: In the testing set, the accuracy, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic (ROC) curve (AUC) of model 1 reached 77.65%, 86.05%, 76.19%, 81.42%, and 0.839, respectively. For model 2, accuracy improved by 6.87%, sensitivity by 4.43%, specificity by 9.52%, F1-score by 7.38%, and the AUC by 0.086, respectively. Conclusion: The models in our study could provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis.