AI-Driven Genotype Prediction Using EHR and Multimodal Data
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-Predictng Model.
- Who it may be relevant to
- Registry conditions: Genotype. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models
Overview
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.
Interventions
- Other 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 treatme
Primary outcome measures
- Area Under the Curve (AUC) [Time frame: 1 year]
- F1 Score [Time frame: 1 year]
Secondary outcome measures (2)
- Sensitivity (True Positive Rate) [Time frame: 1 year]
- Specificity (True Negative Rate) [Time frame: 1 year]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Case-only
Study locations
China · 4 centers
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University — Guangzhou
- Sun Yat-sen University Cancer Hospital — Guangzhou
- First Affiliated Hospital of Wenzhou Medical University — Wenzhou
- Second Affiliated Hospital of Wenzhou Medical University — Wenzhou
Identifiers
NCT: NCT06791421 · Genotype