Abstract

Purpose: Echocardiography is the most commonly used modality for assessing the heart in
clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from
different orientations and positions, thereby creating different viewpoints for assessing the
cardiac function. The determination of the probe viewpoint forms an essential step in automatic
echocardiographic image analysis.

Approach: In this study, convolutional neural networks are used for the automated identification
of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset
of 8732 videos acquired from 374 patients. Differentiable architecture search approach was
utilized to design small neural network architectures for rapid inference while maintaining high
accuracy. The impact of the image quality and resolution, size of the training dataset, and number
of echocardiographic view classes on the efficacy of the models were also investigated.

Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable
classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1%
to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms.

Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require
less training data. Such models can be used for real-time detection of the standard views.