AI-Driven Genotype Prediction Using EHR and Multimodal Data
Recruiting
Observational Study
Genotype
No Placebo Group
Every participant receives an active treatment — no one gets a placebo.
Healthy Volunteers Welcome
You do not need to have the condition being studied to take part.
No Study Drug
Researchers observe your health over time — no experimental treatment is given.
Ready to participate?
Review the details below, then apply to join this clinical trial.
At a Glance
Sex
Any
Study type
Observational
Participants needed
100,000 (estimated)
Sponsor
The Eye Hospital of Wenzhou Medical University · Other
Who this trial is looking for
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The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. The study will evaluate whether predictive models based on this non-genetic data can accurately infer genetic information, which traditionally requires direct genetic testing.
Trial Locations
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Status
Eligibility Criteria
Inclusion Criteria:
1. Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).
2. Participants must have existing genetic testing data available for comparison, if applicable.
3. Particip…
Inclusion Criteria:
1. Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).
2. Participants must have existing genetic testing data available for comparison, if applicable.
3. Participants must be willing to provide consent for the use of their health data in the study.
4. Participants must have no active intervention related to genetic testing or prediction during the study period.
5. Participants should have complete and verifiable health data to allow for accurate prediction by the AI model.
Exclusion Criteria:
1. Participants without available EHR, lab results, or imaging data.
2. Participants with ambiguous, inaccurate, or unverifiable genetic testing results that cannot be used for comparison.
3. Patients with significant discrepancies or missing data that would prevent the AI model from making accurate predictions.
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