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

Author:

Fang XiaolingORCID,Deshmukh Mihir,Chee Miao Li,Soh Zhi-DaORCID,Teo Zhen LingORCID,Thakur SahilORCID,Goh Jocelyn Hui Lin,Liu Yu-Chi,Husain Rahat,Mehta Jodhbir,Wong Tien YinORCID,Cheng Ching-YuORCID,Rim Tyler HyungtaekORCID,Tham Yih-ChungORCID

Abstract

Background/aimsTo 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.MethodsReferable 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).ResultsThe 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.ConclusionDL 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/precisDL 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.

Funder

NMRC/MOH

Natural Science Foundation of Shanghai

Singapore Ministry of Health’s National Medical Research Council

Publisher

BMJ

Subject

Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology

Reference34 articles.

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