Age-Related Macular Degeneration (AMD)NCT06843499
Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening
A study testing The VeriSee AI-assisted diagnostic system and Data collection from the patient's clinical history in people with Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR), run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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 and diabetic retinopathy (DR). Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (DR)
- What is being tested?
- The VeriSee AI-assisted diagnostic system and Data collection from the patient's clinical history
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteTaiwan
- Study dates
- 2 Jun 2025to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- The patient does not agree to participate in the trial or is unable to provide informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Hospital
Taipei, Taiwan, 100225
Yi-Ting Hsieh, Medical Doctor, +886-2-2312-3456 ext. 265018, ythyth@gmail.com
About this study
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.
Read the 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) 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.
- Being tested
- The VeriSee AI-assisted diagnostic system, Data collection from the patient's clinical history
- Keywords
- Artificial Intelligence, imaging analysis, disease diagnosis, macular degeneration, diabetic retinopathy
- Medical subject headings
- Macular Degeneration, Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The VeriSee AI-assisted diagnostic systemOther
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 historyOther
Data collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used.
| Study group | Role | What they receive |
|---|---|---|
| AI Intervention | Other | 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. Receives: Other: The VeriSee AI-assisted diagnostic system; Other: Data collection from the patient's clinical history |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
The sensitivity of the index test (VeriSee) was calculated as the proportion of participants with reference standard-confirmed disease who were correctly identified as positive by the AI-assisted diagnostic software.
Measured: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
Specificity
The specificity of the index test was calculated as the proportion of participants without the target condition, as determined by the reference standard, who were correctly classified as negative by the AI-assisted diagnostic tool.
Measured: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
Concordance
Concordance between the AI-assisted diagnosis and the ophthalmologists' interpretation was assessed using the overall agreement rate (i.e., the percentage of cases with identical classification results).
Measured: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
Secondary outcomes (1)
Total Cost Analysis (Including Direct and Indirect Costs)
This measure includes direct medical costs (e.g., screening, follow-up, medication, and treatment), healthcare-related indirect medical costs (e.g., IT system maintenance, healthcare personnel), and non-medical indirect costs (e.g., transportation and productivity loss due to blindness). Costs will be analyzed from both the National Health Insurance perspective and the broader societal perspective.
Measured: From enrollment to 12 months after screening
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2025
- Study start2 Jun 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Age-Related Macular Degeneration (AMD)Studies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital, Min-Sheng General Hospital, Ministry of Health and Welfare, Taiwan
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06843499
- Sponsor's protocol ID
- 202412086DINC
- Status last verified
- Jul 2026
- First submitted
- 20 Feb 2025
- Last updated
- 22 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening
- ClinicalTrials.gov ID
- NCT06843499
- Acronym / sponsor ID
- 202412086DINC
- Keywords
- Artificial Intelligence, imaging analysis, disease diagnosis, macular degeneration, diabetic retinopathy
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital, Min-Sheng General Hospital, Ministry of Health and Welfare, Taiwan
- Study start
- 2 Jun 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 25 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| AI Intervention | Other | Other: The VeriSee AI-assisted diagnostic system, Other: Data collection from the patient's clinical historyPatients 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- The patient does not agree to participate in the trial or is unable to provide informed consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The VeriSee AI-assisted diagnostic systemVeriSee 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. | Other | AI Intervention |
| Data collection from the patient's clinical historyData collection from the patient's clinical history was conducted because the VeriSee AI-assisted diagnostic system was not used. | Other | AI Intervention |
Primary outcome
Sensitivity
Time frame: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
The sensitivity of the index test (VeriSee) was calculated as the proportion of participants with reference standard-confirmed disease who were correctly identified as positive by the AI-assisted diagnostic software.
Specificity
Time frame: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
The specificity of the index test was calculated as the proportion of participants without the target condition, as determined by the reference standard, who were correctly classified as negative by the AI-assisted diagnostic tool.
Concordance
Time frame: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
Concordance between the AI-assisted diagnosis and the ophthalmologists' interpretation was assessed using the overall agreement rate (i.e., the percentage of cases with identical classification results).
1 secondary outcome
Total Cost Analysis (Including Direct and Indirect Costs)
Time frame: From enrollment to 12 months after screening
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing The VeriSee AI-assisted diagnostic system and Data collection from the patient's clinical history in people with Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR), run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 22 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jul 2026.