Development and Testing of Artificial Intelligence-Based Mobile Application to Achieve Cataract Backlog-Free Status in Uttar Pradesh, India

IF 3.7 3区医学 Q1 OPHTHALMOLOGY Asia-Pacific Journal of Ophthalmology Pub Date : 2024-09-01 DOI:10.1016/j.apjo.2024.100094

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Abstract

Background

Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app “Roshni” for cataract screening.

Method

The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm’s suitability for field use.

Results

The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.

Conclusion

This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.

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开发和测试基于人工智能的移动应用程序，以实现印度北方邦无白内障积压状态。

背景介绍北方邦（Uttar Pradesh，UP）是印度人口最多的邦，约有 3600 万人年龄在 50 岁或以上，分布在 10 万多个村庄。其中，估计有 350 万人患有视力障碍，包括因白内障得不到治疗而失明。为了实现无白内障积压状态，UP 需要在社区层面对这部分人群进行筛查，并为白内障患者提供治疗。我们设想了一种利用眼部图像的人工智能初级筛查应用程序，可部署给一线卫生工作者进行社区一级的筛查。本文概述了在开发用于白内障筛查的人工智能移动应用程序 "Roshni "过程中获得的启示：方法：基于人工智能的白内障分类模型是利用 13,633 张眼部图像开发的，经过三个阶段的实验后最终确定，该模型可检测眼球、虹膜和瞳孔图像中的白内障。总体而言，我们使用多种深度学习算法进行了 155 次实验，包括 ResNet50、ResNet101、YOLOv5、EfficientNetV2 和 InceptionV3。我们设定了特异性和灵敏度均达到 90% 的最低阈值，以确保算法适合现场使用：眼球聚焦图像的白内障检测模型达到了 51.9% 的灵敏度和 87.6% 的特异性，而使用好/坏虹膜过滤器的虹膜聚焦图像模型达到了 52.4% 的灵敏度和 93.3% 的特异性。瞳孔分割图像分类模型采用了好/坏瞳孔过滤器、基于 UNet 的语义分割模型和 EfficientNetV2，灵敏度为 96%，特异度为 97%。对 302 名受益人（604 幅图像）进行的现场测试表明，总体灵敏度为 86.6%，特异性为 93.3%，阳性预测值为 58.4%，阴性预测值为 98.5%：本文详细介绍了一款人工智能移动应用程序的开发过程，该应用程序旨在为一线卫生工作者开展社区白内障筛查提供便利。

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来源期刊

Asia-Pacific Journal of Ophthalmology OPHTHALMOLOGY-

CiteScore

8.10

自引率

18.20%

发文量

197

审稿时长

6 weeks

期刊介绍： The Asia-Pacific Journal of Ophthalmology, a bimonthly, peer-reviewed online scientific publication, is an official publication of the Asia-Pacific Academy of Ophthalmology (APAO), a supranational organization which is committed to research, training, learning, publication and knowledge and skill transfers in ophthalmology and visual sciences. The Asia-Pacific Journal of Ophthalmology welcomes review articles on currently hot topics, original, previously unpublished manuscripts describing clinical investigations, clinical observations and clinically relevant laboratory investigations, as well as .perspectives containing personal viewpoints on topics with broad interests. Editorials are published by invitation only. Case reports are generally not considered. The Asia-Pacific Journal of Ophthalmology covers 16 subspecialties and is freely circulated among individual members of the APAO’s member societies, which amounts to a potential readership of over 50,000.