PT - JOURNAL ARTICLE AU - Xiaoling Fang AU - Mihir Deshmukh AU - Miao Li Chee AU - Zhi-Da Soh AU - Zhen Ling Teo AU - Sahil Thakur AU - Jocelyn Hui Lin Goh AU - Yu-Chi Liu AU - Rahat Husain AU - Jodhbir Mehta AU - Tien Yin Wong AU - Ching-Yu Cheng AU - Tyler Hyungtaek Rim AU - Yih-Chung Tham TI - Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras AID - 10.1136/bjophthalmol-2021-318866 DP - 2021 Jul 08 TA - British Journal of Ophthalmology PG - bjophthalmol-2021-318866 4099 - http://bjo.bmj.com/content/early/2021/07/08/bjophthalmol-2021-318866.short 4100 - http://bjo.bmj.com/content/early/2021/07/08/bjophthalmol-2021-318866.full AB - 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.Data are available upon reasonable request. Data request can be made to corresponding author Dr Yih-Chung Tham.