Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

Automated summary

Machine learning methods are increasingly being applied in various fields, including ultrasound imaging, due to their effectiveness in solving challenging problems. This review focuses on the recent implementations of machine learning techniques in medical diagnostics and non-destructive evaluation in the field of ultrasound imaging. The studies were classified based on the human organ investigated and the methodology used, and solutions for detection/classification of material defects or patterns are discussed. The main merits of machine learning from the study analysis are summarized and discussed.