Work

Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images

Year: 2025

Type: article

Abstract: PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types of anterior segment images and compare their performances. DESIGN: Retrospective study. METHODS: A ViT was used to classify bacterial and fungal keratitis. We integrated one or more ViTs by adding a vector or by using self-attention to combine different types of anterior segment images (broad-beam, slit-beam, and blue-light). We compared the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of the models. Cross-validation was performed thrice, and there was no overlap between the validation sets. The training/validation set was divided in an 8:2 ratio based on the number of individuals. RESULTS: A total of 283 broad-beam, 610 slit-beam, and 342 blue-light images were obtained from 79 patients. 62 (78 %) patients were assigned for training and 17 (22 %) for validation. The AUROC of ViT with broad-beam images was 0.72. The top AUROC score (0.93) was attained by combining the outputs from two ViT models utilizing self-attention, incorporating both broad-beam and slit-beam images. Similarly, the highest AUPRC score (0.93) was reached by fusing the outputs from three ViTs with self-attention, involving broad-beam, slit-beam, and blue-light images. CONCLUSIONS: Despite the limited dataset, we validated ViT with self-attention to learn different types of images to improve recognition accuracy in diagnosing bacterial and fungal keratitis. ViT with self-attention has a meaningful effect on enhancing the diagnostic performance of bacterial and fungal keratitis by combining two or more types of anterior segment images.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2025.109976

Authors: Yeo Kyoung Won, Choong Han Kim, Jooyoung Jeon, Jiho Cha, Dong Hui Lim

Institutions: Samsung Medical Center, Sungkyunkwan University, Korea Advanced Institute of Science and Technology

Language: English

Topic: Ocular Infections and Treatments

Subfield: Ophthalmology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,080