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AI-Driven Genotype Prediction Using EHR and Multimodal Data
Sponsor: The Eye Hospital of Wenzhou Medical University
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.
Official title: Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
100000
Start Date
2023-07-01
Completion Date
2025-06
Last Updated
2025-04-17
Healthy Volunteers
Yes
Conditions
Interventions
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.
Locations (4)
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong, China
Sun Yat-sen University Cancer Hospital
Guangzhou, Guangdong, China
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China