AI-Driven Genotype Prediction Using EHR and Multimodal Data

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Другое 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

Первичные конечные точки

• Area Under the Curve (AUC) [Срок оценки: 1 year]
• F1 Score [Срок оценки: 1 year]

Вторичные конечные точки (2)

• Sensitivity (True Positive Rate) [Срок оценки: 1 year]
• Specificity (True Negative Rate) [Срок оценки: 1 year]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Только случаи

Центры проведения

Китай · 4 центра

• Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University — Гуанчжоу
• Sun Yat-sen University Cancer Hospital — Гуанчжоу
• First Affiliated Hospital of Wenzhou Medical University — Wenzhou
• Second Affiliated Hospital of Wenzhou Medical University — Wenzhou

Идентификаторы

NCT: NCT06791421 · Genotype

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗