Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening

Обзор

This study aims to evaluate the effectiveness of an artificial intelligence (AI)-assisted screening system in ophthalmic diagnosis. Using AI-based fundus photography, the system will assist physicians in diagnosing three common eye diseases: age-related macular degeneration and diabetic retinopathy (DR). The AI system will analyze fundus images from participants and rapidly generate detection results for ophthalmologists' reference in making final diagnoses and clinical decisions. The study will assess the clinical benefits of the AI-assisted diagnostic system, providing scientific evidence to enhance the efficiency of ophthalmic disease diagnosis and treatment.

Подробное описание

Artificial Intelligence (AI) has shown significant potential in medical imaging analysis and disease diagnosis, particularly in ophthalmology. Substantial advancements have been made in utilizing AI for diagnosing common ophthalmic diseases, enhancing early detection and improving patient outcomes. Early diagnosis of age-related macular degeneration (AMD) and diabetic retinopathy (DR) is crucial for effective treatment and disease management.

However, current clinical diagnoses rely heavily on ophthalmologists, leading to challenges such as low patient attendance rates and unequal distribution of diagnostic resources. To address these issues, this study will provide robust evidence to further validate the diagnostic performance of AI-assisted screening and clinical effectiveness of the VeriSee AI-assisted diagnostic system in the detection of diabetic DR and AMD.

VeriSee AMD and VeriSee DR are AI-powered medical software tools designed to screen for AMD and DR, respectively. These systems employ advanced AI algorithms to analyze color fundus photography images, assess disease conditions, and evaluate image quality. By integrating this software into clinical workflows, physicians receive instant diagnostic support, improving efficiency and accessibility in ophthalmic disease screening.

Вмешательства

• Другое The VeriSee AI-assisted diagnostic system
  VeriSee AMD, VeriSee DR, and VeriSee GLC are AI-based medical software devices designed for screening age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, respectively. These systems utilize advanced AI algorithms to analyze color fundus photography images for disease assessment. By installing the software on a computer, the system can evaluate image quality, predict disease conditions, and instantly provide results to clinical physicians, serving as a diagnostic aid.
• Другое Data collection from the patient's clinical history
  Data collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used.

Первичные конечные точки

• Sensitivity [Срок оценки: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month]
• Specificity [Срок оценки: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month]
• Concordance [Срок оценки: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month]

Вторичные конечные точки (1)

• Total Cost Analysis (Including Direct and Indirect Costs) [Срок оценки: From enrollment to 12 months after screening]

Критерии участия

Критерии включения

• VeriSee AMD is used in non-retinal subspecialty ophthalmology clinics for adults aged 50 and above.
• VeriSee DR is used in non-retinal subspecialty clinics for diabetic patients aged 20 and above.

Критерии исключения

• The patient does not agree to participate in the trial or is unable to provide informed consent.

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

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Скрининг

Центры проведения

Тайвань · 1 центр

• National Taiwan University Hospital — Taipei

Идентификаторы

NCT: NCT06843499 · 202412086DINC

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗