Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

NCT05930444 | Completed DMC

• 1 other identifier: FD-EENT-20230625
• Study Type: observational
• Target: 9,825
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

Eye Diseases

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of multimodal machine learning program

  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 March 2024

Study Arms (2)

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.

Diagnostic Test: Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases

Interventions

Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases DIAGNOSTIC_TEST

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.

Patients with Eye-related Chief Complaints

Eligibility Criteria

• Age: 2 Months+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Eye & ENT Hospital of Fudan University: lead
• The Affiliated Eye Hospital of Nanjing Medical University: collaborator
• Suqian First Hospital: collaborator

Study Sites (3)

The Affiliated Eye Hospital of Nanjing Medical University

Nanjing, China

Location

Fudan Eye & ENT Hospital

Shanghai, China

Location

Suqian First People's Hospital

Suqian, China

Location

Related Publications (2)

• Ma R, Cheng Q, Yao J, Peng Z, Yan M, Lu J, Liao J, Tian L, Shu W, Zhang Y, Wang J, Jiang P, Xia W, Li X, Gan L, Zhao Y, Zhu J, Qin B, Jiang Q, Wang X, Lin X, Chen H, Zhu W, Xiang D, Nie B, Wang J, Guo J, Xue K, Cui H, Cheng J, Zhu X, Hong J, Shi F, Zhang R, Chen X, Zhao C. Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses. NPJ Digit Med. 2025 Jan 27;8(1):64. doi: 10.1038/s41746-025-01461-0.

  PMID: 39870855 DERIVED

• Peng Z, Ma R, Zhang Y, Yan M, Lu J, Cheng Q, Liao J, Zhang Y, Wang J, Zhao Y, Zhu J, Qin B, Jiang Q, Shi F, Qian J, Chen X, Zhao C. Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study. Front Artif Intell. 2023 Dec 8;6:1323924. doi: 10.3389/frai.2023.1323924. eCollection 2023.

  PMID: 38145231 DERIVED

Related Links

• Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study

MeSH Terms

Conditions

Eye Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 25, 2023
• First Posted: July 5, 2023
• Study Start: July 21, 2023
• Primary Completion: March 10, 2024
• Study Completion: March 31, 2024
• Last Updated: November 15, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: The protocol has been published on 08 December 2023.

Locations

CN (3)