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The aim of this study was to develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE) on chest radiographs. The authors determined that the DLCE-LAE was able to outperform and improve the performance of cardiothoracic radiologists in the detection of LAE, while also showing promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort.

Key points

  • Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs.
  • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm.
  • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.

Article: Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

Authors: Ju Gang Nam, Jinwook Kim, Keonwoo Noh, Hyewon Choi, Da Som Kim, Seung-Jin Yoo, Hyun-Lim Yang, Eui Jin Hwang, Jin Mo Goo, Eun-Ah Park, Hye Young Sun, Min-Soo Kim & Chang Min Park

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.