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使用裂隙灯和手持相机拍摄的眼前段照片进行翼状胬肉自动检测的深度学习算法。

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

机构信息

Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

Department of Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.

出版信息

Br J Ophthalmol. 2022 Dec;106(12):1642-1647. doi: 10.1136/bjophthalmol-2021-318866. Epub 2021 Jul 9.

Abstract

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.

摘要

背景/目的:评估基于裂隙灯和手持相机拍摄的彩色眼前节照片(ASPs)检测翼状胬肉存在和程度的深度学习(DL)算法的性能。

方法

参考性翼状胬肉的定义为从角膜缘延伸>2.50mm 或在角膜缘处的基底宽度>5.00mm。使用新加坡眼病流行病学(SEED)研究的 2503 张图像作为开发集。算法在 SEED 队列的内部集(629 张图像(55.3%翼状胬肉,8.4%参考性翼状胬肉))上进行验证,并在两个基于诊所的外部集上进行测试(集 1 有 2610 张图像(2.8%翼状胬肉,0.7%参考性翼状胬肉,来自裂隙灯 ASP);集 2 有 3701 张图像,2.5%翼状胬肉,0.9%参考性翼状胬肉,来自手持 ASP)。

结果

内部测试集中,算法检测任何翼状胬肉的接收者操作特征曲线(AUROC)下面积为 99.5%(灵敏度=98.6%;特异性=99.0%),外部测试集 1 为 99.1%(灵敏度=95.9%,特异性=98.5%),外部测试集 2 为 99.7%(灵敏度=100.0%,特异性=88.3%)。对于参考性翼状胬肉,算法在内部测试集中的 AUROC 为 98.5%(灵敏度=94.0%,特异性=95.3%),外部集 1 为 99.7%(灵敏度=87.2%,特异性=99.4%),外部集 2 为 99.0%(灵敏度=94.3%,特异性=98.0%)。

结论

基于 ASP 的 DL 算法可以最佳地检测翼状胬肉的存在和参考水平,并且可以用作检测参考性翼状胬肉的简单筛查工具。在社区环境中进一步验证是必要的。

概要/要点:基于 ASP 的 DL 算法可以最佳地检测翼状胬肉的存在和参考水平,并且可以用作社区筛查中检测参考性翼状胬肉的简单筛查工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5132/9685734/59ba857dcbf6/bjophthalmol-2021-318866f01.jpg

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