Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

Abstract This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer l...

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Autores principales:	Ce Zheng, Xiaolin Xie, Zhilei Wang, Wen Li, Jili Chen, Tong Qiao, Zhuyun Qian, Hui Liu, Jianheng Liang, Xu Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cfd6912bb3f04ba8b58082b820d72c02
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spelling	oai:doaj.org-article:cfd6912bb3f04ba8b58082b820d72c022021-12-02T14:01:33ZDevelopment and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography10.1038/s41598-020-79809-72045-2322https://doaj.org/article/cfd6912bb3f04ba8b58082b820d72c022021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79809-7https://doaj.org/toc/2045-2322Abstract This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924–0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%–100%), which is better than an accuracy of 72.3% (95% CI 62.2%–82.4%), 82.8% (95% CI 78.7%–86.9%) and 86.8% (95% CI 82.5%–91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.Ce ZhengXiaolin XieZhilei WangWen LiJili ChenTong QiaoZhuyun QianHui LiuJianheng LiangXu ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ce Zheng Xiaolin Xie Zhilei Wang Wen Li Jili Chen Tong Qiao Zhuyun Qian Hui Liu Jianheng Liang Xu Chen Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
description	Abstract This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924–0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%–100%), which is better than an accuracy of 72.3% (95% CI 62.2%–82.4%), 82.8% (95% CI 78.7%–86.9%) and 86.8% (95% CI 82.5%–91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.
format	article
author	Ce Zheng Xiaolin Xie Zhilei Wang Wen Li Jili Chen Tong Qiao Zhuyun Qian Hui Liu Jianheng Liang Xu Chen
author_facet	Ce Zheng Xiaolin Xie Zhilei Wang Wen Li Jili Chen Tong Qiao Zhuyun Qian Hui Liu Jianheng Liang Xu Chen
author_sort	Ce Zheng
title	Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
title_short	Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
title_full	Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
title_fullStr	Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
title_full_unstemmed	Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
title_sort	development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cfd6912bb3f04ba8b58082b820d72c02
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Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

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