Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images

Frontiers in Medicine

Medicine

Hyebin Lee¹  Yong Man Ro¹  Youngjun Kim²  Yeo Kyoung Won²  Gyule Han²  Tae-Young Chung²  Dong Hui Lim³ 

[1] Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea;Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea;Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea;Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea;

关键词: anterior segment image;    bacterial keratitis;    convolutional neural network (CNN);    deep learning (DL);    fungal keratitis;    infectious keratitis;    lesion guiding module (LGM);    mask adjusting module (MAM);   

DOI  :  10.3389/fmed.2023.1162124

 received in 2023-02-09, accepted in 2023-04-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionInfectious keratitis is a vision threatening disease. Bacterial and fungal keratitis are often confused in the early stages, so right diagnosis and optimized treatment for causative organisms is crucial. Antibacterial and antifungal medications are completely different, and the prognosis for fungal keratitis is even much worse. Since the identification of microorganisms takes a long time, empirical treatment must be started according to the appearance of the lesion before an accurate diagnosis. Thus, we developed an automated deep learning (DL) based diagnostic system of bacterial and fungal keratitis based on the anterior segment photographs using two proposed modules, Lesion Guiding Module (LGM) and Mask Adjusting Module (MAM).MethodsWe used 684 anterior segment photographs from 107 patients confirmed as bacterial or fungal keratitis by corneal scraping culture. Both broad- and slit-beam images were included in the analysis. We set baseline classifier as ResNet-50. The LGM was designed to learn the location information of lesions annotated by ophthalmologists and the slit-beam MAM was applied to extract the correct feature points from two different images (broad- and slit-beam) during the training phase. Our algorithm was then externally validated using 98 images from Google image search and ophthalmology textbooks.ResultsA total of 594 images from 88 patients were used for training, and 90 images from 19 patients were used for test. Compared to the diagnostic accuracy of baseline network ResNet-50, the proposed method with LGM and MAM showed significantly higher accuracy (81.1 vs. 87.8%). We further observed that the model achieved significant improvement on diagnostic performance using open-source dataset (64.2 vs. 71.4%). LGM and MAM module showed positive effect on an ablation study.DiscussionThis study demonstrated that the potential of a novel DL based diagnostic algorithm for bacterial and fungal keratitis using two types of anterior segment photographs. The proposed network containing LGM and slit-beam MAM is robust in improving the diagnostic accuracy and overcoming the limitations of small training data and multi type of images.

【 授权许可】

Unknown   
Copyright © 2023 Won, Lee, Kim, Han, Chung, Ro and Lim.