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Abstract #1411

Population based Deep Cardiac Atlas Phenotypes and Application in Biological Age Prediction

Mengting Sun1, Qirong Li1, Yan Li2, Yajing Zhang3, Longyu Sun1, Qing Li1, and Chengyan Wang1
1Human Phenome Institute, Fudan University, Shanghai, China, 2Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Cardiovascular, Cardiac Atlas, Phenotype

Motivation: Cardiac biological age serves as a crucial indicator of cardiovascular disease risk. It can be assessed through non-invasive imaging.

Goal(s): This study aims to construct the morphological atlas using CMR imaging, extract deep phenotypes, and validate their potential value in age prediction.

Approach: End-diastolic (ED) and end-systolic (ES) atlases from 1000 healthy volunteers were constructed to extract momenta as deep phenotypes, with a random forest model evaluating their predictive power against conventional indicators.

Results: 880 cardiac phenotypes based on ED and ES atlases were extracted. Integrating these with conventional biomarkers enhances age prediction accuracy, reflected by reduced MAE and increased R2 score.

Impact: Based on cardiac atlases of two key phases, momenta extracted as deep phenotypes could control deformation and encode age-related anatomical variations. Combining these new phenotypes with conventional biomarkers enables the development of more accurate models for predicting cardiac biological age.

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Keywords