NCT06791421

AI-Driven Genotype Prediction Using EHR and Multimodal Data

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 100,000 (estimated)
• Sponsor: The Eye Hospital of Wenzhou Medical University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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.

Detailed 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.

Conditions

• Genotype

Interventions

Type	Name	Description
OTHER	AI-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.

Timeline

• Start date: 2023-07-01
• Primary completion: 2025-06-01
• Completion: 2025-06-01
• First posted: 2025-01-24
• Last updated: 2025-04-17

Locations

4 sites across 1 country: China