AI-Driven Genotype Prediction Using EHR and Multimodal Data

NCT06791421 | Recruiting

• 1 other identifier: Genotype
• Study Type: observational
• Target: 100,000
• Locations: 1 country
• Sites: 4

Brief 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.

Conditions

Genotype

Keywords

AI; AI-prediction; Genotype; Multimodal data

Outcome Measures

Primary Outcomes (2)

• Area Under the Curve (AUC)

  AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).

  1 year

• F1 Score

  The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.

  1 year

Secondary Outcomes (2)

• Sensitivity (True Positive Rate)

  1 year

• Specificity (True Negative Rate)

  1 year

Study Arms (1)

AI-Based Genotype Prediction Using EHR and Multimodal Data

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.

Other: AI-Predictng Model

Interventions

AI-Predictng Model OTHER

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.

AI-Based Genotype Prediction Using EHR and Multimodal Data

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will be selected from multiple healthcare centers that maintain comprehensive electronic health records (EHR) and have access to multimodal clinical data, including lab results, medical imaging (e.g., X-rays, MRIs, CT scans), and medical history. Participants will be individuals with a variety of health conditions for which genotype information is relevant, although no specific genetic characteristics will be used for selection. The focus will be on utilizing the available health data to predict genetic information through the AI model. The study aims to evaluate the accuracy and utility of using non-genetic data, such as EHR and multimodal imaging, for predicting patient genotypes, which may provide an alternative approach to traditional genetic testing methods.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (4)

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University

Guangzhou, Guangdong, China

RECRUITING

Sun Yat-sen University Cancer Hospital

Guangzhou, Guangdong, China

RECRUITING

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

COMPLETED

Second Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: July 1, 2023
• Primary Completion: June 1, 2025
• Study Completion: June 1, 2025
• Last Updated: April 17, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (4)