Ophthalmic AI-Assisted Medical Decision-Making

Early Phase 1

Recruiting

• Conditions: Ocular Diseases

• Registration Number: NCT06755060
• Lead Sponsor: The Eye Hospital of Wenzhou Medical University

Brief Summary

This is a multi-center, prospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.

Detailed Description

Visual impairments significantly affect an individual's quality of life. Early screening, diagnosis, and treatment of ocular diseases are crucial for preventing the onset and progression of vision disorders. In clinical practice, ophthalmologists often need to integrate a wide range of patient data, including demographic information, medical history, biochemical markers such as blood glucose and lipid levels, risk factors, as well as various ophthalmic data, such as fundus images, OCT scans, and visual field tests, to make an accurate diagnosis and develop an appropriate treatment plan.

In an era where precision and personalized medicine are at the forefront of healthcare, the early detection and diagnosis of eye diseases, as well as the selection of suitable diagnostic and therapeutic strategies at different stages of the disease, have become significant challenges in clinical settings. Recent advancements in medical imaging and analysis techniques have greatly enhanced the accuracy and effectiveness of ocular disease diagnosis.

This study aims to develop an ophthalmic artificial intelligence-assisted decision-making system by integrating multimodal data from imaging and electronic medical records, in combination with deep learning techniques. The objective is to improve diagnostic accuracy, streamline clinical workflows, and provide more personalized treatment options for patients. Ultimately, this system seeks to enhance treatment outcomes and improve the overall quality of life for patients suffering from ocular diseases.

Study Timeline

• Study Start: 2024-12-01
• Study First Submitted: 2024-12-09
• Study First Submitted QC: 2024-12-23
• Study First Posted: 2025-01-01
• Status Verified: 2025-02
• Last Update Submitted: 2025-02-02
• Last Update Posted: 2025-02-05
• Primary Completion: 2026-12-30
• Study Completion: 2026-12-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2000

Inclusion Criteria

1. Age Criteria: No age restrictions apply for inclusion in the study.
2. Ophthalmic Disease Diagnosis: Eligible patients must have a diagnosis of one or more ophthalmic conditions, with the diagnosis confirmed by a qualified ophthalmologist.
3. Imaging and Clinical Data Requirements: Patients must be able to provide complete ophthalmic imaging data and electronic medical records (EMR) that are comprehensive and accessible for the purposes of the study.
4. Informed Consent: All patients, or their legal representatives in the case of minors or individuals unable to provide informed consent, must sign a consent form that clearly outlines the study's objectives, procedures, potential risks and discomforts, data usage, and the rights and responsibilities of participants. In the case of minors or those unable to consent, informed consent must be obtained from the patient's legal guardian.
5. Treatment Adherence: Participants must demonstrate the ability to understand and adhere to the study's requirements, including compliance with follow-up visits, examination schedules, and treatment recommendations. Patients must agree to participate in regular assessments and data collection, including imaging exams, laboratory tests, and follow-up evaluations as required by the study protocol.
6. Clinical Physician Assessment: The attending physician must determine that the patient meets all inclusion criteria and has the capacity to comply with the necessary treatment, diagnostic tests, and follow-up protocols throughout the study duration.

Exclusion Criteria

1. Acute or Severe Ocular Diseases: Patients with acute ocular conditions requiring immediate medical intervention, which necessitate exclusion from interventional studies due to the urgency of their treatment.
2. Serious Systemic Diseases: Patients with serious systemic illnesses that may interfere with the treatment of ocular diseases, impact the effectiveness of the intervention, or complicate the interpretation of study outcomes.
3. Prior Exposure to Study Interventions: Patients who have previously undergone the intervention being studied or participated in other experimental treatments within ongoing clinical trials, as this may introduce bias or confound the study results.
4. Incomplete Imaging or Clinical Data: Patients who are unable to provide complete or adequate ophthalmic imaging data or lack a comprehensive electronic medical record (EMR), which are essential for the integrity of the study data.
5. Pregnancy or Lactation: Pregnant or breastfeeding women, for whom there may be potential risks associated with ocular treatment or imaging procedures. Such cases will be evaluated on an individual basis to ensure patient safety.
6. Mental Health or Cognitive Impairment: Patients diagnosed with significant mental health disorders or cognitive impairments that prevent them from fully understanding the nature and risks of the study, or from complying with the treatment regimen and follow-up procedures.
7. Drug Allergies or Severe Reactions: Patients with known allergies or severe adverse reactions to any medications or ophthalmic treatments likely to be used during the study, which could pose a health risk to the patient.
8. Current Participation in Other Clinical Trials: Patients who are concurrently involved in other interventional clinical trials (especially those related to ophthalmology), as this may lead to conflicting treatments or interfere with the assessment of the study's outcomes.
9. Inability to Comply with Follow-up Requirements: Patients who, due to logistical, health-related, or personal factors, are unable to comply with the required follow-up visits, treatment regimens, or data collection, which are essential for the study's longitudinal analysis.
10. Other Clinical Exclusions: Patients whose participation, based on the clinical judgment of the treating physician, may not be in their best interest due to their health condition or other factors, or who may experience adverse outcomes from participating in the study.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Area Under the Curve (AUC)	2 years	AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Sensitivity	2 years	Sensitivity (also called True Positive Rate) is a measure of how well a model identifies positive instances. It is defined as the proportion of actual positive cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Specificity	2 years	Specificity (also called True Negative Rate) measures the proportion of actual negative cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Accuracy	2 years	Accuracy measures the proportion of all correct predictions (true positives and true negatives) out of the total number of cases evaluated by the model. No unit (a ratio or percentage, typically expressed as a percentage).
False Positive Rate	2 years	False Positive Rate (FPR) measures the proportion of actual negative cases that are incorrectly identified as positive by the model. No unit (a ratio or percentage, typically expressed as a percentage).
False Negative Rate	2 years	False Negative Rate (FNR) measures the proportion of actual positive cases that are incorrectly identified as negative by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Postoperative Complication Rate	2 years	Percentage (%) of patients experiencing postoperative complications.
Recurrence Risk Rate	2 years	Percentage (%) of patients experiencing recurrence during the follow-up period.
Survival Rate	2 years	Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.

Secondary Outcome Measures

Name	Time	Method
Treatment Adherence	2 years	Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.
Physician Acceptance of AI System	2 years	Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.
System Usability Score	2 years	Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.
AI System Response Time	2 years	Average time (seconds) taken for the AI to provide recommendations after data input.
System Failure Rate	2 years	Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).
User Interface Design Satisfaction	2 years	Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.
Patient Satisfaction Score	2 years	Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.
Effectiveness of Decision Support	2 years	Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Decision Time Efficiency	2 years	Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.

Trial Locations

Locations (5)

ZhuHai Hospital; 🇨🇳 Zhuhai, Guangdong, China

First Affiliated Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

Second Affiliated Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

The Eye Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

Macau University of Science and Technology Hospital; 🇲🇴 Macau, Macau