Suppr超能文献

一款用于白内障检测的智能手机应用程序的临床影响评估。

Evaluation of the Clinical Impact of a Smartphone Application for Cataract Detection.

作者信息

Janti Siddharam S, Saluja Rohit, Tiwari Nivedita, Kolavai Raghavendra Rao, Mali Kalpana, Arora Abhishek J, Johar Amita, Sahoo Durgesh Prasad, Sahithi Eereti

机构信息

Ophthalmology, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND.

Biochemistry, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND.

出版信息

Cureus. 2024 Oct 14;16(10):e71467. doi: 10.7759/cureus.71467. eCollection 2024 Oct.

Abstract

Background Approximately 10 million people in India suffer from bilateral blindness, with cataracts accounting for roughly 70% of these cases. However, there is a severe scarcity of ophthalmologists in India (12,000 across the country), which makes routine cataract screening very difficult, particularly in rural areas. To tackle this problem, we investigated the use of an artificial intelligence (AI)-based application for cataract screening at All India Institute of Medical Sciences (AIIMS), Bibinagar, that can be used by nursing officers and other healthcare professionals as a primary screening tool. Ophthalmologists from AIIMS Bibinagar additionally validate the results of this application. Purpose The aim of this study was to assess the clinical performance of a smartphone-based cataract screening application that uses an AI module to identify cataracts in photos taken with the device's camera. The study compared the application's results with diagnoses made by ophthalmologists using a slit lamp. Methods At AIIMS Bibinagar, 495 patients participated in a prospective clinical trial. The AI-based screening solution examined smartphone images that were taken in accordance with a set protocol to identify whether cataracts were present. The results of the application were then compared with the diagnoses made by ophthalmologists based on slit-lamp tests. Results The study included 990 eye images. The AI screening application demonstrated an overall accuracy of 90.01% for cataract detection. Specific metrics include a sensitivity of 89.50%, specificity of 89.73%, precision of 91.43%, and an F1 score of 90.36%. The positive predictive value (PPV) was approximately 91.3%, based on 485 true positives and 46 false positives. The negative predictive value (NPV) was approximately 87.6%, based on 402 true negatives and 57 false negatives. Conclusions The smartphone-based cataract screening application proves to be an effective tool for community-level cataract screening in remote areas where access to expensive equipment and specialized ophthalmic care is limited. Its high accuracy and efficiency make it a valuable option for low-resource settings and suitable for home screening, particularly in the post-COVID era.

摘要

背景

印度约有1000万人患有双侧失明,其中白内障约占这些病例的70%。然而,印度眼科医生严重短缺(全国仅有12000名),这使得常规白内障筛查非常困难,尤其是在农村地区。为解决这一问题,我们在全印医学科学研究所(AIIMS)比宾纳加尔分院研究了一种基于人工智能(AI)的白内障筛查应用程序,护士和其他医疗保健专业人员可将其用作初级筛查工具。AIIMS比宾纳加尔分院的眼科医生会进一步验证该应用程序的结果。

目的

本研究的目的是评估一款基于智能手机的白内障筛查应用程序的临床性能,该应用程序使用人工智能模块通过手机摄像头拍摄的照片来识别白内障。该研究将该应用程序的结果与眼科医生使用裂隙灯做出的诊断进行了比较。

方法

在AIIMS比宾纳加尔分院,495名患者参与了一项前瞻性临床试验。基于人工智能的筛查解决方案检查了按照既定方案拍摄的智能手机图像,以确定是否存在白内障。然后将该应用程序的结果与眼科医生基于裂隙灯检查做出的诊断进行比较。

结果

该研究包括990张眼部图像。人工智能筛查应用程序在白内障检测方面的总体准确率为90.01%。具体指标包括灵敏度为89.50%、特异性为89.73%、精确率为91.43%以及F1分数为90.36%。基于485个真阳性和46个假阳性,阳性预测值(PPV)约为91.3%。基于402个真阴性和57个假阴性,阴性预测值(NPV)约为87.6%。

结论

事实证明,对于偏远地区社区层面的白内障筛查而言,这款基于智能手机的白内障筛查应用程序是一种有效的工具,在这些地区,获取昂贵设备和专业眼科护理的机会有限。其高准确性和效率使其成为资源匮乏地区的一个有价值的选择,适用于家庭筛查,尤其是在新冠疫情后时代。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f0b/11560082/0e2392f1345f/cureus-0016-00000071467-i01.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验