ACCESS 2：用于儿科糖尿病眼科检查的 AI 研究 2 (ACCESS2)
将数字视网膜检查应用于综合性儿科糖尿病护理
The purpose of this study is to determine if use of a nonmydriatic fundus camera using autonomous artificial intelligence software at the point of care increases the proportion of underserved youth with diabetes screened for diabetic retinopathy, and to determine the diagnostic accuracy of the autonomous AI system in detecting diabetic retinopathy from retinal images of youth with diabetes.
研究概览
详细说明
This study will recruit up to 500 individuals ages 8-21 with type 1 or type 2 diabetes. In this study, participants will undergo a point-of-care diabetic eye exam using autonomous AI software on a non-mydriatic fundus camera. Participants will receive the diabetic eye exam results immediately from the autonomous AI system, and if abnormal will be referred to an eye care provider for a dilated eye exam.
In the AI for ChildrenS Diabetic Eye ExamS Study (ACCESS2), 398 participants will be enrolled to determine if point of care autonomous AI increases the proportion of minority and underserved youth screened for diabetic retinopathy. The autonomous AI interpretation will also be compared to consensus grading of retinal specialists to determine if there is agreement and to determine the diagnostic accuracy of the system in youth.
A cohort of youth with known diabetic retinopathy (true positives) will also be enrolled as an enriched population to determine the diagnostic accuracy of autonomous AI compared to the prognostic standard interpretation of a central reading center.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名：Risa M Wolf, MD
- 电话号码：4109556463
- 邮箱：RWolf@jhu.edu
研究联系人备份
- 姓名：Alvin Liu, MD
- 邮箱：tliu25@jhmi.edu
学习地点
-
-
Maryland
-
Baltimore、Maryland、美国、21287
- 招聘中
- Johns Hopkins Pediatric Diabetes Center
-
接触：
- Risa M Wolf, MD
- 电话号码：410-955-6463
- 邮箱：rwolf@jhu.edu
-
-
参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
描述
Inclusion Criteria:
Meets American Diabetes Association (ADA) criteria for diabetic retinopathy screening:
- Diagnosis of Type 1 diabetes for ≥3 years, and age 11 or in puberty
- Diagnosis of Type 2 diabetes
Enriched cohort:
- Patients with Type 1 or Type 2 diabetes,
- 8-21 years of age with known diabetic retinopathy (true positives).
- No time limit on last diabetic eye exam.
Exclusion Criteria:
- Known diabetic eye exam in the last 12 months
学习计划
研究是如何设计的？
设计细节
- 主要用途：放映
- 分配：不适用
- 介入模型：单组作业
- 屏蔽：无（打开标签）
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
|
其他：Diabetic Retinopathy Exam at the point of care
Participants will undergo a point of care diabetic retinopathy eye exam using autonomous AI. Those that test positive will be referred to Eye Care Provider for dilated eye exam. |
Participants will undergo point-of-care diabetic retinopathy screening using autonomous artificial intelligence software to interpret retinal images taken with a non-mydriatic fundus camera and providing an immediate result.
其他名称：
|
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Proportion screened for diabetic retinopathy
大体时间：Day 1
|
Equivalence in proportion screened for diabetic retinopathy of white and non-white youth with autonomous AI |
Day 1
|
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Percentage of agreement in interpretation of retinal images
大体时间：Day 1
|
Agreement in interpretation of retinal images between autonomous AI and consensus grading by ophthalmologists |
Day 1
|
|
Sensitivity of autonomous AI vs. prognostic standard
大体时间：Day 1
|
Sensitivity of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard. This will be analyzed in the ACCESS2 trial cohort alone, and also in the ACCESS2 trial cohort with the enriched cohort of youth with known diabetic retinopathy. |
Day 1
|
|
Specificity of autonomous AI vs. prognostic standard
大体时间：Day 1
|
Specificity of autonomous AI in detecting diabetic retinopathy in youth compared to the prognostic standard. This will be analyzed in the ACCESS2 trial cohort alone, and also in the ACCESS2 trial cohort with the enriched cohort of youth with known diabetic retinopathy. |
Day 1
|
|
Proportion with diabetic retinopathy
大体时间：Day 1
|
Proportion of participants with diabetic retinopathy, including none, mild, moderate or severe DR. |
Day 1
|
合作者和调查者
调查人员
- 首席研究员：Risa M Wolf, MD、Johns Hopkins University
出版物和有用的链接
一般刊物
- Channa R, Wolf R, Abramoff MD. Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application. J Diabetes Sci Technol. 2021 May;15(3):695-698. doi: 10.1177/1932296820909900. Epub 2020 Mar 4.
- Thomas CG, Channa R, Prichett L, Liu TYA, Abramoff MD, Wolf RM. Racial/Ethnic Disparities and Barriers to Diabetic Retinopathy Screening in Youths. JAMA Ophthalmol. 2021 Jul 1;139(7):791-795. doi: 10.1001/jamaophthalmol.2021.1551.
- Wolf RM, Channa R, Abramoff MD, Lehmann HP. Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes. JAMA Ophthalmol. 2020 Oct 1;138(10):1063-1069. doi: 10.1001/jamaophthalmol.2020.3190.
- Wolf RM, Liu TYA, Thomas C, Prichett L, Zimmer-Galler I, Smith K, Abramoff MD, Channa R. The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth. Diabetes Care. 2021 Mar;44(3):781-787. doi: 10.2337/dc20-1671. Epub 2021 Jan 21.
- Porter M, Channa R, Wagner J, Prichett L, Liu TYA, Wolf RM. Prevalence of diabetic retinopathy in children and adolescents at an urban tertiary eye care center. Pediatr Diabetes. 2020 Aug;21(5):856-862. doi: 10.1111/pedi.13037. Epub 2020 May 31.
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
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