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

INTRODUCTION: Infectious 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 prognos...

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Autores principales: Won, Yeo Kyoung, Lee, Hyebin, Kim, Youngjun, Han, Gyule, Chung, Tae-Young, Ro, Yong Man, Lim, Dong Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233039/
https://www.ncbi.nlm.nih.gov/pubmed/37275380
http://dx.doi.org/10.3389/fmed.2023.1162124
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author Won, Yeo Kyoung
Lee, Hyebin
Kim, Youngjun
Han, Gyule
Chung, Tae-Young
Ro, Yong Man
Lim, Dong Hui
author_facet Won, Yeo Kyoung
Lee, Hyebin
Kim, Youngjun
Han, Gyule
Chung, Tae-Young
Ro, Yong Man
Lim, Dong Hui
author_sort Won, Yeo Kyoung
collection PubMed
description INTRODUCTION: Infectious 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). METHODS: We 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. RESULTS: A 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. DISCUSSION: This 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.
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spelling pubmed-102330392023-06-02 Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images Won, Yeo Kyoung Lee, Hyebin Kim, Youngjun Han, Gyule Chung, Tae-Young Ro, Yong Man Lim, Dong Hui Front Med (Lausanne) Medicine INTRODUCTION: Infectious 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). METHODS: We 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. RESULTS: A 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. DISCUSSION: This 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. Frontiers Media S.A. 2023-05-18 /pmc/articles/PMC10233039/ /pubmed/37275380 http://dx.doi.org/10.3389/fmed.2023.1162124 Text en Copyright © 2023 Won, Lee, Kim, Han, Chung, Ro and Lim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Won, Yeo Kyoung
Lee, Hyebin
Kim, Youngjun
Han, Gyule
Chung, Tae-Young
Ro, Yong Man
Lim, Dong Hui
Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title_full Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title_fullStr Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title_full_unstemmed Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title_short Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
title_sort deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10233039/
https://www.ncbi.nlm.nih.gov/pubmed/37275380
http://dx.doi.org/10.3389/fmed.2023.1162124
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