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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

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Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional,

Daiju Ueda¹, Toshimasa Matsumoto¹, Shoichi Ehara²

¹Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan; Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan.

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Summary

A deep learning model can now detect valvular heart disease and assess cardiac function using chest X-rays. This AI tool offers a faster alternative to echocardiography, especially where specialists are unavailable.

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Assessing Cardiac Reprogramming using High Content Imaging Analysis

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Published on: October 26, 2020

Area of Science:

Artificial Intelligence in Medical Imaging
Cardiovascular Disease Diagnostics
Radiology and Cardiology

Background:

Chest radiography is a common imaging modality.
Assessing cardiac function and valvular disease from chest radiographs is challenging.
Existing methods lack comprehensive evaluation of cardiac parameters from chest X-rays.

Purpose of the Study:

To develop and validate a deep learning model for simultaneous detection of valvular heart disease and cardiac function from chest radiographs.
To assess the model's performance against echocardiography standards.
To explore the potential of AI in cardiac diagnostics using widely available imaging.

Main Methods:

A deep learning model was trained, validated, and externally tested using chest radiographs and echocardiograms from multiple institutions.
The model was designed to classify left ventricular ejection fraction, tricuspid regurgitant velocity, and various valvular diseases.
Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Main Results:

The model achieved high performance across various cardiac function and valvular disease classifications in the external test dataset.
For left ventricular ejection fraction, the AUC was 0.92 with 86% accuracy.
The model demonstrated strong performance in detecting mitral regurgitation (AUC 0.89) and tricuspid regurgitation (AUC 0.92).

Conclusions:

A deep learning model can accurately classify cardiac functions and valvular heart diseases from chest radiographs.
This AI approach offers a rapid, low-resource alternative to echocardiography.
The model has the potential to improve cardiac care accessibility, particularly in resource-limited settings.