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AI-Driven Prediction of Biological Age With EHR

NCT06791486 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000000

Last updated 2025-04-02

No results posted yet for this study

Summary

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.

Conditions

  • Biological Age

Interventions

OTHER

AI-assisted predictive model

This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes.

Sponsors & Collaborators

  • The Eye Hospital of Wenzhou Medical University

    lead OTHER

Eligibility

Min Age
0 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-03-01
Primary Completion
2025-04-02
Completion
2025-04-02

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06791486 on ClinicalTrials.gov