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The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure.

Key points

  • Twenty-three candidate descriptors were manually extracted from cardiac computed tomography images, and seven of them were selected for subsequent modeling.
  • The random forest model presents the best predictive performance for pulmonary pressure among all methods.
  • The computed tomography-based machine learning model could predict post–Glenn shunt pulmonary pressure non-invasively.

Article: Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models

Authors: Lei Huang, Jiahua Li, Meiping Huang, Jian Zhuang, Haiyun Yuan, Qianjun Jia, Dewen Zeng, Lifeng Que, Yue Xi, Jijin Lin & Yuhao Dong

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