A-EYE: A Mixed Quantitative and Qualitative Study to Develop and Evaluate the Application of Artificial Intelligence (AI) Methods Using Retinal Imaging for the Identification of Adverse Retinal Changes Associated With Cancer Therapies. (A-EYE)
This is a data collection study involving the gathering of clinical data and OCT (optical coherence tomography) scans from 350 patients.
The purpose of this study is to gather data to help develop an AI algorithm to detect eye abnormalities specifically those related to certain cancer treatments.
At the end of the study interviews will be held with expert ophthalmologists to assess the acceptability of implementing AI into clinical practice.
研究概览
详细说明
Many cancer patients will access new treatments through clinical trials. These treatments have often never been tested in humans and therefore, are likely to have unknown side effects. Some of these side effects include changes to the eye, such as blindness.
Ahead of patients taking part in these trials there is often little planning done to manage potential side effects on the eye. Additionally, accessing the expertise of eye specialists is not always available and often referral to a specialist is only given when eye symptoms have become advanced. These delays in identifying side effects on the eye also delays treatment and follow-up management. Providing patients access to this expertise would help in the detection and management of treatment side effects, however, due to demands on resources this access is not always readily available.
The aim of this study is to create an artificial intelligence (AI) program that can detect changes to the eye related to disease, which, in the future, can be specifically used in cancer patient care. Additionally, developing an AI program to detect cancer related side effects to the eye will go a significant way in easing the burden on the health care system and improve side effects from new cancer treatments.
This study will involve the collection of eye scans and medical data from participants at the Manchester Royal Eye Hospital. These will then be used to develop AI methods to detect changes in the eye related to those seen by patients on cancer treatment. The AI will then be compared with the assessments of eye specialists to assess if they give similar results.
研究类型
注册 (预期的)
联系人和位置
学习联系方式
- 姓名:Tariq Aslam
- 电话号码:0161 276 1234
- 邮箱:tariq.aslam@manchester.ac.uk
学习地点
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Manchester、英国
- 招聘中
- Manchester Royal Eye Hospital
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接触:
- Tariq Aslam
- 邮箱:tariq.aslam@manchester.ac.uk
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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
取样方法
研究人群
描述
Inclusion Criteria:
Patients are eligible for the study if all inclusion criteria are met:
- Voluntary informed consent.
- Aged at least 18 years.
- Fully registered patient attending the Manchester Royal Eye Hospital
- Patients are having an optical diagnostic imaging as part of their standard of care.
Exclusion Criteria:
Patients are excluded from the study if any of the following criteria apply:
1. Patient who are deemed clinically unable to be scanned by healthcare professional.
学习计划
研究是如何设计的?
设计细节
研究衡量的是什么?
主要结果指标
结果测量 |
大体时间 |
|---|---|
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Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.
大体时间:12 months
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12 months
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次要结果测量
结果测量 |
大体时间 |
|---|---|
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Sensitivity of the AI in identifying clinically relevant lesions as defined by an ophthalmologist. Specificity of the AI in identifying clinically relevant lesions as defined by an ophthalmologist.
大体时间:12 months
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12 months
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其他结果措施
结果测量 |
大体时间 |
|---|---|
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F1 score of the proposed algorithm compared against baseline algorithms.
大体时间:13 months
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13 months
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Recorded questionnaire/ interview with ophthalmologist and cancer specialists.
大体时间:9 months
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9 months
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Number of novel relationships identified
大体时间:12 months
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12 months
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (预期的)
研究完成 (预期的)
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
与本研究相关的术语
其他研究编号
- NHS001768
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)?
药物和器械信息、研究文件
研究美国 FDA 监管的药品
研究美国 FDA 监管的设备产品
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