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

Not Applicable

Not yet recruiting

• Conditions: Diabetic Retinopathy (DR); Age-Related Macular Degeneration (AMD); Glaucoma

• Registration Number: NCT06843499
• Lead Sponsor: National Taiwan University Hospital

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.

Study Timeline

• Status Verified: 2025-02
• Study First Submitted: 2025-02-20
• Study First Submitted QC: 2025-02-20
• Last Update Submitted: 2025-02-20
• Study First Posted: 2025-02-25
• Last Update Posted: 2025-02-25
• Study Start: 2025-03-01
• Primary Completion: 2027-12-31
• Study Completion: 2027-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 58000

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.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
age-related macular degeneration (AMD)	From screening to physician-confirmed diagnosis of AMD, an average of 1 month	Cases screened and subsequently diagnosed with age-related macular degeneration
diabetic retinopathy (DR)	From screening to physician-confirmed diagnosis of DR, an average of 1 month	Cases screened and subsequently diagnosed with diabetic retinopathy
Glaucoma	From screening to physician-confirmed diagnosis of Glaucoma, an average of 1 month	Cases screened and subsequently diagnosed with Glaucoma

Secondary Outcome Measures

Name	Time	Method
detection rate	From from screening to physician-confirmed diagnosis, an average of 1 month	The proportion of individuals who are identified as having a specific disease among all those who underwent screening.
Positive referral rate	From screening to official referral, an average of 1 week	The proportion of individuals who tested positive during screening and required referral for further evaluation or diagnosis among all screened cases.

Trial Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan