Health TrialsbyHealthPanel

AI-Driven Genotype Prediction Using EHR and Multimodal Data

ConditionGenotype
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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.

Trial 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.

Trial design

Design model

Case-only

Time perspective

Retrospective

Treatments tested in this trial

  • AI-Predictng Model

    Other intervention

    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.

Treatment groups

100,000 Participants
are divided into 1 treatment group
Group A: AI-Based Genotype Prediction Using EHR and Multimodal Data1 intervention

Trial outcomes

Primary outcomes

1

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).

Time frame
1 year
2

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.

Time frame
1 year

Secondary outcomes

1

Sensitivity (True Positive Rate)

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

Time frame
1 year
2

Specificity (True Negative Rate)

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.

Time frame
1 year

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

Wenzhou Medical University

Sponsor institution

Source ClinicalTrials.gov
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