Ophthalmic Multimodal 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

Overview

This is a multi-center, retrospective 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.

Interventions

• Diagnostic test Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases
  This intervention involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. It integrates multi-modal data, including fundus photography, optical coherence tomography (OCT), and patient clinical records, to provide real-time, precise, and personalized diagnostic support. Unlike other models, this system utilizes a longitudinal patient dataset to predict disease progression and treatment outcomes.Key distinguishing f

Primary outcome measures

• Area Under the Curve (AUC) [Time frame: 1 years]
• Sensitivity [Time frame: 1 years]
• Accuracy Accuracy Accuracy [Time frame: 1 years]
• Specificity [Time frame: 1 years]
• False Positive Rate [Time frame: 1 years]
• False Negative Rate [Time frame: 1 years]
• Postoperative Complication Rate [Time frame: 1 years]
• Recurrence Risk Rate [Time frame: 1 years]
• Survival Rate [Time frame: 1 years]
• Effectiveness of Decision Support [Time frame: 1 years]

Secondary outcome measures (7)

• System Usability Score [Time frame: 1 years]
• AI System Response Time [Time frame: 1 years]
• System Failure Rate [Time frame: 1 years]
• User Interface Design Satisfaction [Time frame: 1 years]
• Patient Satisfaction Score [Time frame: 1 years]
• Treatment Adherence [Time frame: 1 years]
• Physician Acceptance of AI System [Time frame: 1 years]

Eligibility criteria

Inclusion criteria

1.All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.

2.Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests). 3.Patients with a clear and confirmed diagnosis of one or more ocular diseases. 4.Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.

• All ophthalmology patients who have previously received treatment at the Department of Ophthalmology, the Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, Zhuhai People's Hospital, and the University Hospital.
• Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests).
• Patients with a clear and confirmed diagnosis of one or more ocular diseases.
• Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.

Exclusion criteria

• Incomplete or missing critical EHR components.
• Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
• Duplicated or redundant data from the same patient.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Case-only

Study locations

China · 4 centers

• ZhuHai Hospital, zhuhai, guangdong — Zhuhai
• First Affiliated Hospital of Wenzhou Medical University — Wenzhou
• Second Affiliated Hospital of Wenzhou Medical Universit — Wenzhou
• The Eye Hospital of Wenzhou Medical University — Wenzhou

Macau · 1 center

• Macau University of Science and Technology Hospital — Macao

Identifiers

NCT: NCT06755190 · Ophthalmic Multimodal AI

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗