Cargando…
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...
Autores principales: | , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784835577419399168 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9685734 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fangxiaoling deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT deshmukhmihir deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT cheemiaoli deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT sohzhida deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT teozhenling deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT thakursahil deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT gohjocelynhuilin deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT liuyuchi deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT husainrahat deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT mehtajodhbir deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT wongtienyin deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT chengchingyu deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT rimtylerhyungtaek deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras AT thamyihchung deeplearningalgorithmsforautomaticdetectionofpterygiumusinganteriorsegmentphotographsfromslitlampandhandheldcameras |