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Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.

Hirotaka Ieki^1,2,3, Kaoru Ito⁴, Mike Saji³

¹Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

Communications Medicine

|December 9, 2022

Summary

This summary is machine-generated.

Artificial intelligence can estimate age from chest X-rays (CXRs). This "X-ray age" is linked to worse outcomes in heart failure and cardiovascular disease patients, indicating its clinical significance.

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Area of Science:

Radiology
Artificial Intelligence
Cardiology

Background:

Limited research exists on age estimation from chest X-rays (CXRs).
The clinical significance of AI-derived age from CXRs is not fully understood.

Purpose of the Study:

To develop and validate a deep neural network (DNN) model for age estimation from CXRs.
To determine the clinical significance of AI-estimated age in cardiovascular disease patients.

Main Methods:

Trained a DNN model on over 100,000 CXRs for age estimation.
Applied the DNN to CXRs from heart failure and cardiovascular disease intensive care unit patients.

Main Results:

The DNN's estimated age (X-ray age) strongly correlated with chronological age.
Elevated X-ray age was associated with worse clinical outcomes, including heart failure readmission and all-cause death.
Elevated X-ray age predicted a poorer prognosis in cardiovascular disease ICU patients.

Conclusions:

AI-estimated X-ray age can serve as a valuable indicator of cardiovascular abnormalities.
X-ray age may aid clinicians in predicting, preventing, and managing cardiovascular diseases.