Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract
Shinnosuke Sawano1, Satoshi Kodera1, Masataka Sato1
1Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan.
Insights
A deep neural network estimates vascular age from coronary angiography (CAG) images. Older predicted vascular age in acute coronary syndrome (ACS) patients is linked to worse cardiovascular outcomes.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Geriatric Cardiology
Background:
- Coronary angiography (CAG) is the gold standard for assessing coronary arteries, particularly in acute coronary syndrome (ACS).
- Age-related changes in coronary arteries are known, but their specific imaging features on CAG and prognostic impact require further characterization.
- The potential of artificial intelligence to derive prognostic information from cardiovascular imaging is an emerging area of research.
Purpose of the Study:
- To develop and validate a deep neural network (DNN) model for estimating vascular age using CAG.
- To investigate the association between DNN-predicted vascular age from CAG and clinical outcomes in patients with ACS.
Main Methods:
- A DNN was trained on 5,923 CAG videos from 572 patients to predict vascular age.
- The model's performance was evaluated on an independent set of 1,437 CAG videos from 144 patients.
- The association between DNN-predicted vascular age and major adverse cardiovascular events (MACE) was assessed in 298 ACS patients undergoing percutaneous coronary intervention (PCI).
Main Results:
- The DNN model accurately estimated vascular age with a mean absolute error of 4 years and R-squared of 0.72 (r = 0.856).
- ACS patients with older predicted vascular age experienced significantly more MACE compared to those with younger predicted vascular age (p = 0.017).
- Older vascular age independently predicted increased MACE in ACS patients after adjusting for clinical confounders (HR 2.14, p = 0.032).
Conclusions:
- Deep neural network analysis of CAG images can reliably estimate vascular age.
- Estimated vascular age from CAG is a significant predictor of adverse cardiovascular outcomes in ACS patients.
- This AI-driven approach offers a novel prognostic tool for cardiovascular risk stratification using existing coronary angiography data.
Abstract:
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 (r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.
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