Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning

The aim of this study was to develop and validate a deep learning-based system to detect peripheral neuropathy (DN) from retinal colour images in people with diabetes. Retinal images from 1561 people with diabetes were used to predictDN diagnosed on vibration perception threshold. A total of 189 had...

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Autores principales:	Diego R. Cervera, Luke Smith, Luis Diaz-Santana, Meenakshi Kumar, Rajiv Raman, Sobha Sivaprasad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	diabetes deep learning diabetic neuropathy diabetic retinopathy Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/ef0f54e0eb31403fae121aa4516d6668
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spelling	oai:doaj.org-article:ef0f54e0eb31403fae121aa4516d66682021-11-25T17:20:06ZIdentifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning10.3390/diagnostics111119432075-4418https://doaj.org/article/ef0f54e0eb31403fae121aa4516d66682021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/1943https://doaj.org/toc/2075-4418The aim of this study was to develop and validate a deep learning-based system to detect peripheral neuropathy (DN) from retinal colour images in people with diabetes. Retinal images from 1561 people with diabetes were used to predictDN diagnosed on vibration perception threshold. A total of 189 had diabetic retinopathy (DR), 276 had DN, and 43 had both DR and DN. 90% of the images were used for training and validation and 10% for testing. Deep neural networks, including Squeezenet, Inception, and Densenet were utilized, and the architectures were tested with and without pre-trained weights. Random transform of images was used during training. The algorithm was trained and tested using three sets of data: all retinal images, images without DR and images with DR. Area under the ROC curve (AUC) was used to evaluate performance. The AUC to predict DN on the whole cohort was 0.8013 (±0.0257) on the validation set and 0.7097 (±0.0031) on the test set. The AUC increased to 0.8673 (±0.0088) in the presence of DR. The retinal images can be used to identify individuals with DN and provides an opportunity to educate patients about their DN status when they attend DR screening.Diego R. CerveraLuke SmithLuis Diaz-SantanaMeenakshi KumarRajiv RamanSobha SivaprasadMDPI AGarticlediabetesdeep learningdiabetic neuropathydiabetic retinopathyMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 1943, p 1943 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	diabetes deep learning diabetic neuropathy diabetic retinopathy Medicine (General) R5-920
spellingShingle	diabetes deep learning diabetic neuropathy diabetic retinopathy Medicine (General) R5-920 Diego R. Cervera Luke Smith Luis Diaz-Santana Meenakshi Kumar Rajiv Raman Sobha Sivaprasad Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
description	The aim of this study was to develop and validate a deep learning-based system to detect peripheral neuropathy (DN) from retinal colour images in people with diabetes. Retinal images from 1561 people with diabetes were used to predictDN diagnosed on vibration perception threshold. A total of 189 had diabetic retinopathy (DR), 276 had DN, and 43 had both DR and DN. 90% of the images were used for training and validation and 10% for testing. Deep neural networks, including Squeezenet, Inception, and Densenet were utilized, and the architectures were tested with and without pre-trained weights. Random transform of images was used during training. The algorithm was trained and tested using three sets of data: all retinal images, images without DR and images with DR. Area under the ROC curve (AUC) was used to evaluate performance. The AUC to predict DN on the whole cohort was 0.8013 (±0.0257) on the validation set and 0.7097 (±0.0031) on the test set. The AUC increased to 0.8673 (±0.0088) in the presence of DR. The retinal images can be used to identify individuals with DN and provides an opportunity to educate patients about their DN status when they attend DR screening.
format	article
author	Diego R. Cervera Luke Smith Luis Diaz-Santana Meenakshi Kumar Rajiv Raman Sobha Sivaprasad
author_facet	Diego R. Cervera Luke Smith Luis Diaz-Santana Meenakshi Kumar Rajiv Raman Sobha Sivaprasad
author_sort	Diego R. Cervera
title	Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
title_short	Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
title_full	Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
title_fullStr	Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
title_full_unstemmed	Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
title_sort	identifying peripheral neuropathy in colour fundus photographs based on deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ef0f54e0eb31403fae121aa4516d6668
work_keys_str_mv	AT diegorcervera identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning AT lukesmith identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning AT luisdiazsantana identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning AT meenakshikumar identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning AT rajivraman identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning AT sobhasivaprasad identifyingperipheralneuropathyincolourfundusphotographsbasedondeeplearning
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Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning

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