Clinical trial NCT06791421: AI-Driven Genotype Prediction Using EHR and Multimodal Data
Official title: Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Genotype
Keywords: AI
AI-prediction
Genotype
Multimodal data
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 100000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
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.

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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Area Under the Curve (AUC)
timeFrame: 1 year
measure: F1 Score
timeFrame: 1 year
Secondary outcomes:
measure: Sensitivity (True Positive Rate)
timeFrame: 1 year
measure: Specificity (True Negative Rate)
timeFrame: 1 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Hospital
state: Guangdong
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
Study officials:
References: