Ignite Creation Date:
2025-12-24 @ 6:27 PM
Ignite Modification Date:
2026-02-06 @ 5:46 AM
Study NCT ID:
NCT04901468
Status:
UNKNOWN
Last Update Posted:
2022-11-09
First Post:
2021-05-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief 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.
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 350}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-06-18', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-11', 'completionDateStruct': {'date': '2022-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-11-08', 'studyFirstSubmitDate': '2021-05-17', 'studyFirstSubmitQcDate': '2021-05-24', 'lastUpdatePostDateStruct': {'date': '2022-11-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-05-25', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'F1 score of the proposed algorithm compared against baseline algorithms.', 'timeFrame': '13 months'}, {'measure': 'Recorded questionnaire/ interview with ophthalmologist and cancer specialists.', 'timeFrame': '9 months'}, {'measure': 'Number of novel relationships identified', 'timeFrame': '12 months'}], 'primaryOutcomes': [{'measure': 'Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.', 'timeFrame': '12 months'}], 'secondaryOutcomes': [{'measure': '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.', 'timeFrame': '12 months'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['All Comers']}, 'descriptionModule': {'briefSummary': 'This is a data collection study involving the gathering of clinical data and OCT (optical coherence tomography) scans from 350 patients.\n\nThe 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.\n\nAt the end of the study interviews will be held with expert ophthalmologists to assess the acceptability of implementing AI into clinical practice.', 'detailedDescription': '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.\n\nAhead 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.\n\nThe 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.\n\nThis 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Participants will be patients at the Manchester Royal Eye Hospital who meet the eligibility criteria.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nPatients are eligible for the study if all inclusion criteria are met:\n\n1. Voluntary informed consent.\n2. Aged at least 18 years.\n3. Fully registered patient attending the Manchester Royal Eye Hospital\n4. Patients are having an optical diagnostic imaging as part of their standard of care.\n\nExclusion Criteria:\n\nPatients are excluded from the study if any of the following criteria apply:\n\n1\\. Patient who are deemed clinically unable to be scanned by healthcare professional.'}, 'identificationModule': {'nctId': 'NCT04901468', 'acronym': 'A-EYE', 'briefTitle': '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.', 'organization': {'class': 'OTHER', 'fullName': 'University of Manchester'}, 'officialTitle': '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.', 'orgStudyIdInfo': {'id': 'NHS001768'}}, 'armsInterventionsModule': {'interventions': [{'name': 'No Intervention', 'type': 'OTHER', 'description': 'This is an observational study'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Manchester', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'Tariq Aslam', 'role': 'CONTACT', 'email': 'tariq.aslam@manchester.ac.uk'}], 'facility': 'Manchester Royal Eye Hospital', 'geoPoint': {'lat': 53.48095, 'lon': -2.23743}}], 'centralContacts': [{'name': 'Tariq Aslam', 'role': 'CONTACT', 'email': 'tariq.aslam@manchester.ac.uk', 'phone': '0161 276 1234'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Manchester', 'class': 'OTHER'}, 'collaborators': [{'name': 'Institute of Cancer Research, United Kingdom', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor Of Ophthalmology and Interface Technologies and Consultant Ophthalmologist', 'investigatorFullName': 'Tariq Aslam', 'investigatorAffiliation': 'University of Manchester'}}}}