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NCT Number: NCT06755060

Ophthalmic AI-Assisted Medical Decision-Making

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.

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Key information

Conditions

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

ZhuHai Hospital, Zhuhai, Guangdong, China

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About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age Criteria: No age restrictions apply for inclusion in the study.
  • Ophthalmic Disease Diagnosis: Eligible patients must have a diagnosis of one or more ophthalmic conditions, with the diagnosis confirmed by a qualified ophthalmologist.
  • 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.
  • 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.
  • 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.
  • 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

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Treatment and study plan

AI-associated strategy

Combination Product

The intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. Patients in the intervention group will undergo standard ophthalmic examinations, with clinical decisions guided by the recommendations generated by the AI system. In contrast, patients in the control group will receive only standard ophthalmic examinations and treatment, without the support of AI-assisted decision-making tools.

Primary outcomes

  1. Area Under the Curve (AUC)

    Time frame: 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).

  2. Sensitivity

    Time frame: 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).

  3. Specificity

    Time frame: 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).

  4. Accuracy

    Time frame: 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).

  5. False Positive Rate

    Time frame: 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).

  6. False Negative Rate

    Time frame: 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).

  7. Postoperative Complication Rate

    Time frame: 2 years

    Percentage (%) of patients experiencing postoperative complications.

  8. Recurrence Risk Rate

    Time frame: 2 years

    Percentage (%) of patients experiencing recurrence during the follow-up period.

  9. Survival Rate

    Time frame: 2 years

    Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.

Secondary outcomes

  1. System Usability Score

    Time frame: 2 years

    Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.

  2. AI System Response Time

    Time frame: 2 years

    Average time (seconds) taken for the AI to provide recommendations after data input.

  3. System Failure Rate

    Time frame: 2 years

    Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).

  4. User Interface Design Satisfaction

    Time frame: 2 years

    Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.

  5. Patient Satisfaction Score

    Time frame: 2 years

    Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.

  6. Treatment Adherence

    Time frame: 2 years

    Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.

  7. Physician Acceptance of AI System

    Time frame: 2 years

    Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.

  8. Effectiveness of Decision Support

    Time frame: 2 years

    Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.

  9. Decision Time Efficiency

    Time frame: 2 years

    Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.

Study contacts

Contact information is provided by the study sponsor or research team.

Lan Wang, MD

CONTACT

[email protected]

+86-0577-85397527

Sponsors and collaborators

Lead sponsor

The Eye Hospital of Wenzhou Medical University

Other

Collaborators

  • First Affiliated Hospital of Wenzhou Medical University
  • Macau University of Science and Technology Hospital
  • Second Affiliated Hospital of Wenzhou Medical University
  • ZhuHai Hospital

Registry information

Official study title

A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Jan 1, 2025
Registry last updated
Aug 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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