Clinical trial NCT06843499: Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening
Official title: Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Age-Related Macular Degeneration (AMD)
Diabetic Retinopathy (DR)
Glaucoma
Keywords: Artificial Intelligence
imaging analysis
disease diagnosis
macular degeneration
diabetic retinopathy
Glaucoma
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: Fu Jen Catholic University Hospital
name: Min-Sheng General Hospital
name: Ministry of Health and Welfare, Taiwan
First posted: 2025-02-25
Enrollment: 58000
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: We adopt a before-and-after study design, retrospectively collecting traditional fundus photography data (without AI) from ophthalmic patients between January 1, 2021, and December 1, 2024, to serve as the control group. Meanwhile, we will compare this with an intervention group that systematically includes patients meeting the inclusion criteria at the start of the study, undergoing AI-based fundus photography.
Primary purpose: SCREENING

Brief summary: 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, diabetic retinopathy (DR), and glaucoma. 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.

Detailed description: 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), diabetic retinopathy (DR), and glaucoma (GLC) 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 adopts a before-and-after trial design to assess the impact of the VeriSee AI-assisted diagnostic system. The study involves both prospective data collection from screenings and retrospective analysis of past patient data.

VeriSee AMD, VeriSee DR, and VeriSee GLC are AI-powered medical software tools designed to screen for AMD, DR, and GLC, 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.

Eligibility criteria: Inclusion Criteria:

* 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.
* VeriSee GLC is used in non-glaucoma subspecialty ophthalmology clinics for adults aged 20 and above with myopia less than 1000 degrees.

Exclusion Criteria:

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

Primary outcomes:
measure: age-related macular degeneration (AMD)
timeFrame: From screening to physician-confirmed diagnosis of AMD, an average of 1 month
measure: diabetic retinopathy (DR)
timeFrame: From screening to physician-confirmed diagnosis of DR, an average of 1 month
measure: Glaucoma
timeFrame: From screening to physician-confirmed diagnosis of Glaucoma, an average of 1 month
Secondary outcomes:
measure: Positive referral rate
timeFrame: From screening to official referral, an average of 1 week
measure: detection rate
timeFrame: From from screening to physician-confirmed diagnosis, an average of 1 month
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan University Hospital
Study officials:
References: