All ComersA-EYENCT04901468
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.
An observational study of All Comers, run by University of Manchester at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- All Comers
- How many people
- 350 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 18 Jun 2021to 31 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Participants will be patients at the Manchester Royal Eye Hospital who meet the eligibility criteria.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site, 1 recruiting
- Recruiting
Manchester Royal Eye Hospital
Manchester
Tariq Aslam, tariq.aslam@manchester.ac.uk
About this study
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.
Read the 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.
- Conditions
- All Comers
- Being tested
- No Intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionOther
This is an observational study
What is being measured
The primary outcome is the main question the study is designed to answer.
Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.
Measured: 12 months
Secondary outcomes (1)
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.
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 May 2021
- Study start18 Jun 2021
- Primary completion31 Dec 2022 (estimated)
- Study completion31 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for All ComersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Manchester
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom
- Responsible party
- Principal investigator, Tariq Aslam, University of Manchester
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04901468
- Sponsor's protocol ID
- NHS001768
- Acronym
- A-EYE
- Status last verified
- Nov 2022
- First submitted
- 17 May 2021
- Last updated
- 9 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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.
- ClinicalTrials.gov ID
- NCT04901468
- Acronym / sponsor ID
- A-EYE / NHS001768
- Conditions
- All Comers
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- University of ManchesterUniversity, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom
- Study start
- 18 Jun 2021 (actual)
- Primary completion
- 31 Dec 2022 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 25 May 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be patients at the Manchester Royal Eye Hospital who meet the eligibility criteria.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (2 listed)
- 1. Patient who are deemed clinically unable to be scanned by healthcare professional.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No InterventionThis is an observational study | Other | – |
Primary outcome
Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.
Time frame: 12 months
1 secondary outcome
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.
Time frame: 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2022.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of All Comers, run by University of Manchester at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Tariq Aslam (0161 276 1234, tariq.aslam@manchester.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment. (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Nov 2022.