Clinical trial NCT06755060: Ophthalmic AI-Assisted Medical Decision-Making
Official title: A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: EARLY_PHASE1
Conditions: Ocular Diseases
Keywords: ocular diseases
Ophthalmic Multimodal AI-Assisted Medical Decision-Making
Artificial Intelligence
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
Collaborators: name: First Affiliated Hospital of Wenzhou Medical University
name: Second Affiliated Hospital of Wenzhou Medical University
name: ZhuHai Hospital
name: Macau University of Science and Technology Hospital
First posted: 2025-01-01
Enrollment: 2000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Area Under the Curve (AUC)
timeFrame: 2 years
measure: Sensitivity
timeFrame: 2 years
measure: Specificity
timeFrame: 2 years
measure: Accuracy
timeFrame: 2 years
measure: False Positive Rate
timeFrame: 2 years
measure: False Negative Rate
timeFrame: 2 years
measure: Postoperative Complication Rate
timeFrame: 2 years
measure: Recurrence Risk Rate
timeFrame: 2 years
measure: Survival Rate
timeFrame: 2 years
Secondary outcomes:
measure: System Usability Score
timeFrame: 2 years
measure: AI System Response Time
timeFrame: 2 years
measure: System Failure Rate
timeFrame: 2 years
measure: User Interface Design Satisfaction
timeFrame: 2 years
measure: Patient Satisfaction Score
timeFrame: 2 years
measure: Treatment Adherence
timeFrame: 2 years
measure: Physician Acceptance of AI System
timeFrame: 2 years
measure: Effectiveness of Decision Support
timeFrame: 2 years
measure: Decision Time Efficiency
timeFrame: 2 years
Other outcomes:
Locations:
city: Zhuhai
country: China
facility: ZhuHai Hospital
state: Guangdong
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: The Eye Hospital of Wenzhou Medical University
state: Zhejiang
city: Macau
country: Macau
facility: Macau University of Science and Technology Hospital
Study officials:
References: