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      Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

      , , , ,

      Ultrasound in Medicine & Biology

      Elsevier BV

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          Abstract

          Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that the morphophysiological representations are correct. Clinical analysis is often initialized with the current view, and automatic classification can thus be useful in improving today's workflow. In this article, convolutional neural networks (CNNs) are used to create classification models predicting up to seven different cardiac views. Data sets of 2-D ultrasound acquired from studies totaling more than 500 patients and 7000 videos were included. State-of-the-art accuracies of 98.3% ± 0.6% and 98.9% ± 0.6% on single frames and sequences, respectively, and real-time performance with 4.4 ± 0.3 ms per frame were achieved. Further, it was found that CNNs have the potential for use in automatic multiplanar reformatting and orientation guidance. Using 3-D data to train models applicable for 2-D classification, we achieved a median deviation of 4° ± 3° from the optimal orientations.

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          Author and article information

          Journal
          Ultrasound in Medicine & Biology
          Ultrasound in Medicine & Biology
          Elsevier BV
          03015629
          November 2018
          November 2018
          Article
          10.1016/j.ultrasmedbio.2018.07.024
          30470606
          © 2018

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