RecruitingNCT06791421

AI-Driven Genotype Prediction Using EHR and Multimodal Data

Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models

Sponsor

The Eye Hospital of Wenzhou Medical University

Enrollment

100,000 participants

Start Date

Jul 1, 2023

Study Type

OBSERVATIONAL

Conditions

Genotype

Summary

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.

Eligibility

Inclusion Criteria5

Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).
Participants must have existing genetic testing data available for comparison, if applicable.
Participants must be willing to provide consent for the use of their health data in the study.
Participants must have no active intervention related to genetic testing or prediction during the study period.
Participants should have complete and verifiable health data to allow for accurate prediction by the AI model.

Exclusion Criteria3

Participants without available EHR, lab results, or imaging data.
Participants with ambiguous, inaccurate, or unverifiable genetic testing results that cannot be used for comparison.
Patients with significant discrepancies or missing data that would prevent the AI model from making accurate predictions.

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Interventions

OTHERAI-Predictng Model

The intervention in this study involves an AI-based predictive model designed to analyze and integrate patient electronic health records (EHR), clinical lab results, and multimodal imaging data (e.g., X-rays, MRIs, CT scans). The AI model is trained to predict a patient's genotype based on these non-genetic data sources. This model uses machine learning algorithms to detect patterns and infer genetic information that would traditionally require direct genetic testing. There are no active treatments or genetic tests involved in this intervention; rather, the AI system serves as a tool to predict genetic information from available clinical data, offering a non-invasive and potentially more accessible alternative to genetic testing.