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Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras

BACKGROUND/AIMS: To evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras. METHODS: Referable pterygium was defined as having extension towards the corn...

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Autores principales: Fang, Xiaoling, Deshmukh, Mihir, Chee, Miao Li, Soh, Zhi-Da, Teo, Zhen Ling, Thakur, Sahil, Goh, Jocelyn Hui Lin, Liu, Yu-Chi, Husain, Rahat, Mehta, Jodhbir, Wong, Tien Yin, Cheng, Ching-Yu, Rim, Tyler Hyungtaek, Tham, Yih-Chung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685734/
https://www.ncbi.nlm.nih.gov/pubmed/34244208
http://dx.doi.org/10.1136/bjophthalmol-2021-318866
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author Fang, Xiaoling
Deshmukh, Mihir
Chee, Miao Li
Soh, Zhi-Da
Teo, Zhen Ling
Thakur, Sahil
Goh, Jocelyn Hui Lin
Liu, Yu-Chi
Husain, Rahat
Mehta, Jodhbir
Wong, Tien Yin
Cheng, Ching-Yu
Rim, Tyler Hyungtaek
Tham, Yih-Chung
author_facet Fang, Xiaoling
Deshmukh, Mihir
Chee, Miao Li
Soh, Zhi-Da
Teo, Zhen Ling
Thakur, Sahil
Goh, Jocelyn Hui Lin
Liu, Yu-Chi
Husain, Rahat
Mehta, Jodhbir
Wong, Tien Yin
Cheng, Ching-Yu
Rim, Tyler Hyungtaek
Tham, Yih-Chung
author_sort Fang, Xiaoling
collection PubMed
description BACKGROUND/AIMS: To evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras. METHODS: Referable pterygium was defined as having extension towards the cornea from the limbus of >2.50 mm or base width at the limbus of >5.00 mm. 2503 images from the Singapore Epidemiology of Eye Diseases (SEED) study were used as the development set. Algorithms were validated on an internal set from the SEED cohort (629 images (55.3% pterygium, 8.4% referable pterygium)), and tested on two external clinic-based sets (set 1 with 2610 images (2.8% pterygium, 0.7% referable pterygium, from slit-lamp ASP); and set 2 with 3701 images, 2.5% pterygium, 0.9% referable pterygium, from hand-held ASP). RESULTS: The algorithm’s area under the receiver operating characteristic curve (AUROC) for detection of any pterygium was 99.5%(sensitivity=98.6%; specificity=99.0%) in internal test set, 99.1% (sensitivity=95.9%, specificity=98.5%) in external test set 1 and 99.7% (sensitivity=100.0%; specificity=88.3%) in external test set 2. For referable pterygium, the algorithm’s AUROC was 98.5% (sensitivity=94.0%; specificity=95.3%) in internal test set, 99.7% (sensitivity=87.2%; specificity=99.4%) in external set 1 and 99.0% (sensitivity=94.3%; specificity=98.0%) in external set 2. CONCLUSION: DL algorithms based on ASPs can detect presence of and referable-level pterygium with optimal sensitivity and specificity. These algorithms, particularly if used with a handheld camera, may potentially be used as a simple screening tool for detection of referable pterygium. Further validation in community setting is warranted. SYNOPSIS/PRECIS: DL algorithms based on ASPs can detect presence of and referable-level pterygium optimally, and may be used as a simple screening tool for the detection of referable pterygium in community screenings.
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spelling pubmed-96857342022-11-25 Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras Fang, Xiaoling Deshmukh, Mihir Chee, Miao Li Soh, Zhi-Da Teo, Zhen Ling Thakur, Sahil Goh, Jocelyn Hui Lin Liu, Yu-Chi Husain, Rahat Mehta, Jodhbir Wong, Tien Yin Cheng, Ching-Yu Rim, Tyler Hyungtaek Tham, Yih-Chung Br J Ophthalmol Clinical Science BACKGROUND/AIMS: To evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras. METHODS: Referable pterygium was defined as having extension towards the cornea from the limbus of >2.50 mm or base width at the limbus of >5.00 mm. 2503 images from the Singapore Epidemiology of Eye Diseases (SEED) study were used as the development set. Algorithms were validated on an internal set from the SEED cohort (629 images (55.3% pterygium, 8.4% referable pterygium)), and tested on two external clinic-based sets (set 1 with 2610 images (2.8% pterygium, 0.7% referable pterygium, from slit-lamp ASP); and set 2 with 3701 images, 2.5% pterygium, 0.9% referable pterygium, from hand-held ASP). RESULTS: The algorithm’s area under the receiver operating characteristic curve (AUROC) for detection of any pterygium was 99.5%(sensitivity=98.6%; specificity=99.0%) in internal test set, 99.1% (sensitivity=95.9%, specificity=98.5%) in external test set 1 and 99.7% (sensitivity=100.0%; specificity=88.3%) in external test set 2. For referable pterygium, the algorithm’s AUROC was 98.5% (sensitivity=94.0%; specificity=95.3%) in internal test set, 99.7% (sensitivity=87.2%; specificity=99.4%) in external set 1 and 99.0% (sensitivity=94.3%; specificity=98.0%) in external set 2. CONCLUSION: DL algorithms based on ASPs can detect presence of and referable-level pterygium with optimal sensitivity and specificity. These algorithms, particularly if used with a handheld camera, may potentially be used as a simple screening tool for detection of referable pterygium. Further validation in community setting is warranted. SYNOPSIS/PRECIS: DL algorithms based on ASPs can detect presence of and referable-level pterygium optimally, and may be used as a simple screening tool for the detection of referable pterygium in community screenings. BMJ Publishing Group 2022-12 2021-07-09 /pmc/articles/PMC9685734/ /pubmed/34244208 http://dx.doi.org/10.1136/bjophthalmol-2021-318866 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Clinical Science
Fang, Xiaoling
Deshmukh, Mihir
Chee, Miao Li
Soh, Zhi-Da
Teo, Zhen Ling
Thakur, Sahil
Goh, Jocelyn Hui Lin
Liu, Yu-Chi
Husain, Rahat
Mehta, Jodhbir
Wong, Tien Yin
Cheng, Ching-Yu
Rim, Tyler Hyungtaek
Tham, Yih-Chung
Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title_full Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title_fullStr Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title_full_unstemmed Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title_short Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
title_sort deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
topic Clinical Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685734/
https://www.ncbi.nlm.nih.gov/pubmed/34244208
http://dx.doi.org/10.1136/bjophthalmol-2021-318866
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