Ophthalmic Multimodal AI-Assisted Medical Decision-Making

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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

Первичные конечные точки

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

Вторичные конечные точки (7)

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

Критерии участия

Критерии включения

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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Только случаи

Центры проведения

Китай · 4 центра

• 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 центр

• Macau University of Science and Technology Hospital — Макао

Идентификаторы

NCT: NCT06755190 · Ophthalmic Multimodal AI

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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