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

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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