Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785052143132082176 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10233039 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wonyeokyoung deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT leehyebin deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT kimyoungjun deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT hangyule deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT chungtaeyoung deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT royongman deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages AT limdonghui deeplearningbasedclassificationsystemofbacterialkeratitisandfungalkeratitisusinganteriorsegmentimages |