All Comers
Conditions
Brief summary
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.
Detailed description
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.
Interventions
OTHERNo Intervention
This is an observational study
Sponsors
Institute of Cancer Research, United Kingdom
University of Manchester
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Patients are eligible for the study if all inclusion criteria are met: 1. Voluntary informed consent. 2. Aged at least 18 years. 3. Fully registered patient attending the Manchester Royal Eye Hospital 4. 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment. | 12 months |
Secondary
| Measure | Time frame |
|---|---|
| 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. | 12 months |
Other
| Measure | Time frame |
|---|---|
| F1 score of the proposed algorithm compared against baseline algorithms. | 13 months |
| Recorded questionnaire/ interview with ophthalmologist and cancer specialists. | 9 months |
| Number of novel relationships identified | 12 months |
Countries
United Kingdom
Contacts
Primary ContactTariq Aslam
Outcome results
None listed