Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images

Abstract

PurposeThis study combines automatic segmentation and manual fine-tuning with an early fusion method to provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis.MethodsFirst, 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 a ratio of 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.ResultsIn 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 AUC by 0.086, respectively.ConclusionThe models in our study could provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis.

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“…In one study on fungal keratitis, Li et al assessed the impact of the segmentation method (e.g., manual or automated) on diagnostic accuracy and speed [23]. They hypothesized that combining manual and automated segmentation methods may yield greater efficiency.…”

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“…Diseases with psychiatric and neurodegenerative origins often involve changes in cerebral tissue morphology, such as alterations in the volume or configuration of deep gray matter structures, cortical thickness, surface area, and convoluted brain patterns [ 1 ]. Therefore, the morphometric analysis of cerebral tissue serves as a critical biomarker for disease diagnosis and acts as an effective diagnostic tool [ 2 , 3 ]. In addition, brain tissue segmentation in MRI scans is valuable for preoperative evaluation, surgical planning [ 4 ], and the development of radiation therapy plans [ 5 ].…”