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NCT Number: NCT06791421

AI-Driven Genotype Prediction Using EHR and Multimodal Data

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.

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Key information

Conditions

Genotype

Sex eligibility

All sexes

Study type

Observational

Primary location

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China

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About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

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.

Primary outcomes

  1. Area Under the Curve (AUC)

    Time frame: 1 year

    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).

  2. F1 Score

    Time frame: 1 year

    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.

Secondary outcomes

  1. Sensitivity (True Positive Rate)

    Time frame: 1 year

    Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.

  2. Specificity (True Negative Rate)

    Time frame: 1 year

    Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.

Study contacts

Contact information is provided by the study sponsor or research team.

Fei Liu, MD

CONTACT

[email protected]

+86 13810512704

Sponsors and collaborators

Lead sponsor

The Eye Hospital of Wenzhou Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Jan 24, 2025
Registry last updated
Apr 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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