Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Abstract

Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

“…A good correlation between the COVID-19 lung lesions as determined by CT parameters and the clinical and biochemical markers of multiple organs has been observed [46]. Harmon et al showed that a series of DL algorithms delivered acceptable performance metrics for the classification of chest CT images Ultrasound is an option for real-time point-of-care testing without radiation exposure in various medical fields [30]. Although lung ultrasound requires closer physical proximity of the examiner to the patient, the risk of COVID-19 cross-infection can be minimized when a plastic disposable cover and individually packaged ultrasound gel on a portable probe are used [34,40].…”

“…This approach can be used to study disease progression and its influencing factors in a single patient [39]. Ultrasound is an option for real-time point-of-care testing without radiation exposure in various medical fields [30]. Although lung ultrasound requires closer physical proximity of the examiner to the patient, the risk of COVID-19 cross-infection can be minimized when a plastic disposable cover and individually packaged ultrasound gel on a portable probe are used [34,40].…”

“…In addition, myocarditis and cardiomyopathy can occur during COVID-19 [ 45 ]. Therefore, AI-based cardiac ultrasound image analysis is expected to improve the diagnostic accuracy of these cardiac complications at the point of care [ 30 ].…”

“…The medical field is no exception, and many AI-powered medical devices have been developed and are now being applied clinically [ 15 ]. In medical research, AI has been introduced into various tasks, including medical image analysis, such as radiological image analysis, endoscopic image analysis, and pathological image association analysis; omics analysis such as genome analysis, epigenome analysis, and proteome analysis; and natural language processing for drug discovery, electronic medical record information, and literature search [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Importantly, research on COVID-19 is being conducted worldwide, and AI is now being actively used in vaccine development, the development of new diagnostic methods, and the development of new therapeutic agents by extracting important features from vast amounts of data.…”

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

“…In recent years, the expectations for artificial intelligence (AI) have increased greatly, and machine learning (ML) technology has made remarkable progress, including the rise of deep learning technology [ 10 , 11 ]. It is important to note that ML has been actively used in the fields of medical and biological research [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], and it has already been used in actual clinical practice [ 24 , 25 , 26 ]. Single-cell analysis is another field in which ML techniques have been used to overcome challenges and make more effective use of its results.…”