通过手持式视网膜照相机、人工智能和同步专家确认同步诊断糖尿病视网膜病变。

IF 4.4 Q1 OPHTHALMOLOGY

Ophthalmology. Retina Pub Date : 2024-11-01 DOI:10.1016/j.oret.2024.05.009

{"title":"通过手持式视网膜照相机、人工智能和同步专家确认同步诊断糖尿病视网膜病变。","authors":"","doi":"10.1016/j.oret.2024.05.009","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>Diabetic retinopathy<span><span> (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to </span>ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area.</span></div></div><div><h3>Design</h3><div>Retrospective cohort study.</div></div><div><h3>Subjects</h3><div>A total of 1115 individuals with diabetes.</div></div><div><h3>Methods</h3><div>A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment.</div></div><div><h3>Main Outcome Measures</h3><div>Proof-of-concept implementation success.</div></div><div><h3>Results</h3><div>Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus<span>, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63–85.84), specificity 80.79% (95% CI, 79.05–82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12–87.22), specificity was 98.86 (95% CI, 98.39–99.33), and area under the curve was 0.92 (95% CI, 0.91–0.94).</span></div></div><div><h3>Conclusions</h3><div>The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":19501,"journal":{"name":"Ophthalmology. Retina","volume":"8 11","pages":"Pages 1083-1092"},"PeriodicalIF":4.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation\",\"authors\":\"\",\"doi\":\"10.1016/j.oret.2024.05.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><div>Diabetic retinopathy<span><span> (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to </span>ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area.</span></div></div><div><h3>Design</h3><div>Retrospective cohort study.</div></div><div><h3>Subjects</h3><div>A total of 1115 individuals with diabetes.</div></div><div><h3>Methods</h3><div>A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment.</div></div><div><h3>Main Outcome Measures</h3><div>Proof-of-concept implementation success.</div></div><div><h3>Results</h3><div>Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus<span>, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63–85.84), specificity 80.79% (95% CI, 79.05–82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12–87.22), specificity was 98.86 (95% CI, 98.39–99.33), and area under the curve was 0.92 (95% CI, 0.91–0.94).</span></div></div><div><h3>Conclusions</h3><div>The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>\",\"PeriodicalId\":19501,\"journal\":{\"name\":\"Ophthalmology. Retina\",\"volume\":\"8 11\",\"pages\":\"Pages 1083-1092\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ophthalmology. Retina\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468653024002367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ophthalmology. Retina","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468653024002367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

目的：糖尿病视网膜病变（DR）是导致可预防性失明的主要原因，尤其是在医疗服务不足、眼科医疗服务有限的地区。本研究提出了一个概念验证，即利用带有嵌入式人工智能（AI）平台的便携式手持视网膜照相机，辅以视网膜专家的同步远程确认，在服务不足的农村地区进行糖尿病视网膜病变筛查：设计：回顾性队列研究：1,115名糖尿病患者：对巴西东北部四个城市针对糖尿病患者开展的筛查活动进行回顾性分析。便携式手持视网膜照相机拍摄黄斑中心和视盘中心图像，并由人工智能系统进行分析。一旦发现重大异常，系统会立即向视网膜专家发送推送通知，从而实现同步验证和确认，并在几分钟内向患者提供现场反馈。建立了转诊标准，所有转诊患者都接受了完整的眼科检查和后续治疗：主要结果指标：概念验证实施成功：结果：在 2052 名受邀者中，1115 人参加了筛查，平均年龄为 60.93 岁，糖尿病病程为 7.52 年，其中 66.03% 为女性。筛查覆盖了 2,222 只眼睛，发现了各种视网膜疾病。可转诊为 DR 的眼睛占 11.84%，另有 13% 的眼睛患有其他疾病（诊断包括不同阶段的 DR、介质混浊、痣、色素沉着、杯盘比增大、色素变化和其他）。人工智能对可转诊病例（包括 DR 和其他病症）的总体检测结果为：灵敏度- 84.23% (95% CI- 82.63-85.84)，特异性- 80.79% (95% CI- 79.05-82.53)。当我们评估人工智能是否与任何临床诊断（无论是否可转诊）相匹配时，灵敏度为 85.67% (95% CI- 84.12-87.22)，特异度为 98.86 (95% CI- 98.39-99.33)，AUC 为 0.92 (95% CI- 0.91-0.94)：便携式设备、人工智能分析和同步医疗验证的整合有望在预防 DR 致盲方面发挥重要作用，尤其是在社会不平等的情况下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

Purpose

Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area.

Design

Retrospective cohort study.

Subjects

A total of 1115 individuals with diabetes.

Methods

A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment.

Main Outcome Measures

Proof-of-concept implementation success.

Results

Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63–85.84), specificity 80.79% (95% CI, 79.05–82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12–87.22), specificity was 98.86 (95% CI, 98.39–99.33), and area under the curve was 0.92 (95% CI, 0.91–0.94).

Conclusions

The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.