An observational study in Biological Age, sponsored by The Eye Hospital of Wenzhou Medical University. Recruiting at 4 sites in China. Open to participants aged 0 Years to 100 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-04-02.
Sponsored by The Eye Hospital of Wenzhou Medical University · Observational
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.
Biological age prediction is crucial for assessing overall health, determining the risk of age-related diseases, and providing personalized healthcare. While chronological age is a key factor, it does not always reflect an individual's true biological aging process. Early identification of accelerated biological aging and associated health risks can significantly impact early interventions and long-term health outcomes. In clinical practice, healthcare providers integrate a wide range of patient data, including medical history, laboratory test results, and clinical observations, to understand an individual's health status and predict potential future risks. As precision medicine becomes more important, the ability to predict biological age and personalize care plans is essential. Recent advancements in artificial intelligence and data analysis techniques have shown promise in enhancing the accuracy of biological age predictions. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, laboratory results, clinical observations, and patient demographics. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized healthcare for patients by predicting biological age, identifying at-risk individuals, and improving health outcomes.
The Eye Hospital of Wenzhou Medical University is the lead sponsor of 15 studies on the registry; 10 are open to participants now.
Counted across the registry records on this site, refreshed daily.
The study population consists of individuals who have received care at participating hospitals or healthcare centers with accessible electronic health records (EHR). Participants will include those with complete EHR data, including medical history, laboratory test results, imaging data, and lifestyle factors such as diet, physical activity, and smoking habits. The cohort will comprise both individuals who are healthy and those with chronic conditions or comorbidities to analyze biological age prediction across different health statuses. The study will be conducted across multiple healthcare facilities to ensure a diverse patient population representing a wide range of age groups, health conditions, and demographics.
Exclusion Criteria:
Participants whose biological age is predicted to be younger than their chronological age.
Other: AI-assisted predictive model
Participants whose biological age is predicted to be older than their chronological age.
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.
Biological Age Prediction Accuracy
The accuracy of the AI model in predicting biological age compared to chronological age. This will be evaluated using the Pearson Correlation Coefficient (PCC) to assess the strength of the correlation between predicted biological age and chronological age. Additionally, R-squared (R²) will be used to evaluate the proportion of variance in biological age explained by the model.
Time frame: 1 year
Health Risk Correlation
The correlation between predicted biological age and various health risks, such as the development of chronic diseases (e.g., cardiovascular disease, diabetes), using PCC to evaluate the relationship between biological age predictions and health outcomes.
Time frame: 1 year
Plan to share: No
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The Eye Hospital of Wenzhou Medical University