- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05930444
Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases
November 18, 2023 updated by: Eye & ENT Hospital of Fudan University
Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques
With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation.
Furthermore, by incorporating patient selfies, eye examination photos, and other image analysis techniques, the diagnostic capabilities can be further enhanced.
The multi-center study aims to develop an auxiliary diagnostic program for eye diseases using multimodal machine learning techniques and evaluate its diagnostic efficacy in real-world outpatient clinics.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Study Type
Observational
Enrollment (Actual)
1673
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Chen Zhao
- Phone Number: +86 (021) 64377134
- Email: dr_zhaochen@163.com
Study Contact Backup
- Name: Xinjian Chen
- Phone Number: +86 (0512) 65227399
- Email: xjchen@suda.edu.cn
Study Locations
-
-
-
Nanjing, China
- The Affiliated Eye Hospital of Nanjing Medical University
-
Contact:
- Qin Jiang
-
Contact:
- Yue Zhao
-
Shanghai, China
- Fudan Eye & ENT Hospital
-
Suqian, China
- Suqian First People's Hospital
-
Contact:
- Jiang Zhu
-
Contact:
- Bing Qin
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Sampling Method
Non-Probability Sample
Study Population
The "normal participants" refers to individuals with no concerns or issues related to their eyes.
The "participants with eye-related chief complaints" refers to patients from various eye clinics across China. Each participant must undergo comprehensive medical tests and have their medical records reviewed for diagnosis.
Description
Inclusion Criteria:
- Informed consent obtained;
- Participants should be able to have Chinese as their mother tongue, and be sufficiently able to read, write and understand Chinese;
- For normal participants: individuals should have no concerns related to their eyes.
- For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.
Exclusion Criteria:
- Incomplete clinical data to support final diagnosis;
- Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Normal participants
Healthy individuals who have no concerns related to their eyes.
|
|
Patients with Eye-related Chief Complaints
Individuals who have specific concerns or issues related to their eyes, which they consider as the main reason for seeking medical attention or making a complaint.
|
Patients presenting with eye-related chief complaints initially complete a mobile phone application.
This application utilizes patient medical history and relevant images (such as selfies and photos from eye examinations) to provide intelligent diagnosis.
The diagnosis remains undisclosed to the patients.
Subsequently, patients seek medical attention and undergo clinical examination by a skilled clinician.
The clinical diagnosis is subsequently reviewed by a second experienced clinician.
If the diagnoses align, it is considered the gold standard.
In cases of discrepancy, the consensus reached by the two clinicians becomes the gold standard.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Diagnostic accuracy of multimodal machine learning program
Time Frame: from July 2023 to October 2023
|
For each patient, the diagnoses generated by the multimodal machine learning program and the clinical diagnosis provided by skilled clinicians were documented and compared.
Consistency between the two diagnoses indicates the program's precision in clinical practice.
|
from July 2023 to October 2023
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
July 21, 2023
Primary Completion (Actual)
August 31, 2023
Study Completion (Actual)
October 31, 2023
Study Registration Dates
First Submitted
June 25, 2023
First Submitted That Met QC Criteria
June 26, 2023
First Posted (Actual)
July 5, 2023
Study Record Updates
Last Update Posted (Actual)
November 21, 2023
Last Update Submitted That Met QC Criteria
November 18, 2023
Last Verified
November 1, 2023
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- FD-EENT-20230625
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
UNDECIDED
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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