AI-Driven Genotype Prediction Using EHR and Multimodal Data

Wenzhou Medical University

Status: Enrolling

Conditions: Genotype

Treatments: Other: AI-Predictng Model

Study type: Observational

Funder types: Other

Identifiers

NCT06791421

Genotype

Details and patient eligibility

About

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.

Full description

This multi-center, retrospective clinical study aims to evaluate the use of electronic health records (EHR) and multimodal data (such as clinical lab results, imaging data, and medical history) in predicting a patient's genotype. The primary objective of the study is to develop an AI-based prediction model that can infer genetic information by analyzing available health data, eliminating the need for direct genetic testing.The AI model will be trained to process and integrate large datasets, including EHR, lab results, and imaging data such as X-rays, MRIs, and ultrasounds, in order to predict genotypic information. The study will compare the AI-based predictions to actual genetic testing results to evaluate the accuracy of the model. If successful, this method could provide a non-invasive, cost-effective tool for genotype prediction, which could be used in personalized medicine, early disease diagnosis, and risk stratification.Participants will not undergo any genetic testing as part of the study. Instead, their historical medical data will be analyzed by the AI system to predict genetic information and associated disease risks. The study will assess the model's ability to predict genetic predispositions to various health conditions based on the available health data. By doing so, the study aims to advance the use of AI in clinical decision-making and genetic diagnostics.

Enrollment: 100,000 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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.

Trial design

100,000 participants in 1 patient group

AI-Based Genotype Prediction Using EHR and Multimodal Data

Description:

This cohort consists of patients whose historical health data, including electronic health records (EHR), clinical lab results, and multimodal imaging data (such as X-rays, MRIs, and CT scans), will be analyzed by an AI-based prediction model to predict their genotype. There are no active interventions in this cohort, as the study aims to use non-genetic health data to infer genetic information. Participants will not undergo genetic testing but will provide their health data for analysis by the AI system. The goal of this group is to assess the accuracy of the AI model in predicting genotypes and identifying genetic predispositions to various diseases based on available health data.

Treatment:

Other: AI-Predictng Model