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

NCT ID: NCT06843499

Last Updated: 2025-06-22

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

1000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-06-02

Study Completion Date

2027-12-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Age-Related Macular Degeneration (AMD) Diabetic Retinopathy (DR)

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

This trial is expected to use a diagnostic accuracy study to test the effectiveness of the VeriSee system in assisting ophthalmologists in diagnosis, and compare it with the traditional method of ophthalmologists making their own diagnosis through fundus photography to evaluate its sensitivity and specificity.
Primary Study Purpose

SCREENING

Blinding Strategy

NONE

Study Groups

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AI Intervention

Patients will undergo fundus photography screening using artificial intelligence-assisted diagnostic software (VeriSee). Ophthalmologists will independently interpret the same images, and the results will be compared with those generated by the AI.

Group Type OTHER

The VeriSee AI-assisted diagnostic system

Intervention Type OTHER

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.

Data collection from the patient's clinical history

Intervention Type OTHER

Data collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used.

Interventions

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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.

Intervention Type OTHER

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.

Intervention Type OTHER

Eligibility Criteria

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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.

Exclusion Criteria

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

20 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Fu Jen Catholic University Hospital

OTHER

Sponsor Role collaborator

Min-Sheng General Hospital

OTHER

Sponsor Role collaborator

Ministry of Health and Welfare, Taiwan

OTHER_GOV

Sponsor Role collaborator

National Taiwan University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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National Taiwan University Hospital

Taipei, Taiwan, Taiwan

Site Status RECRUITING

Countries

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Taiwan

Facility Contacts

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Yi-Ting Hsieh, Medical Doctor

Role: primary

+886-2-2312-3456 ext. 265018

Other Identifiers

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202412086DINC

Identifier Type: -

Identifier Source: org_study_id

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