Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images

Abstract Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of...

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Autores principales:	Ayumi Koyama, Dai Miyazaki, Yuji Nakagawa, Yuji Ayatsuka, Hitomi Miyake, Fumie Ehara, Shin-ichi Sasaki, Yumiko Shimizu, Yoshitsugu Inoue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dadcb78619664107995fb06572c70b59
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spelling	oai:doaj.org-article:dadcb78619664107995fb06572c70b592021-11-28T12:16:19ZDetermination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images10.1038/s41598-021-02138-w2045-2322https://doaj.org/article/dadcb78619664107995fb06572c70b592021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02138-whttps://doaj.org/toc/2045-2322Abstract Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.Ayumi KoyamaDai MiyazakiYuji NakagawaYuji AyatsukaHitomi MiyakeFumie EharaShin-ichi SasakiYumiko ShimizuYoshitsugu InoueNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ayumi Koyama Dai Miyazaki Yuji Nakagawa Yuji Ayatsuka Hitomi Miyake Fumie Ehara Shin-ichi Sasaki Yumiko Shimizu Yoshitsugu Inoue Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
description	Abstract Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.
format	article
author	Ayumi Koyama Dai Miyazaki Yuji Nakagawa Yuji Ayatsuka Hitomi Miyake Fumie Ehara Shin-ichi Sasaki Yumiko Shimizu Yoshitsugu Inoue
author_facet	Ayumi Koyama Dai Miyazaki Yuji Nakagawa Yuji Ayatsuka Hitomi Miyake Fumie Ehara Shin-ichi Sasaki Yumiko Shimizu Yoshitsugu Inoue
author_sort	Ayumi Koyama
title	Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
title_short	Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
title_full	Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
title_fullStr	Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
title_full_unstemmed	Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
title_sort	determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dadcb78619664107995fb06572c70b59
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Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images

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