Year: 2020
Type: article
Abstract: The application of artificial intelligence (AI) technology to medical imaging has recently brought about tremendous excitement, and AI is making its way into clinical practice, thanks to the technical prowess of current deep learning technology compared with the machine learning methods of the past, the wide availability of digital medical images, and the increased capabilities of computing hardware [1-4].AI has been tried for ultrasonography in various organs and systems, such as the thyroid, musculoskeletal system, breast, and abdomen, as discussed in detail in the focused review articles of this special issue [5-8], albeit not as extensively as some other radiological imaging modalities such as chest X-rays [9].The potential role of AI is anticipated to enhance the quality of ultrasonographic images, to provide various forms of diagnostic support (e.g., automated characterization of findings on ultrasonographic images; extraction of quantitative or predictive information from ultrasonographic images, which is difficult for a human examiner to do based on visual observations; and automated detection or segmentation of various structures on ultrasonographic images), and to improve workflow efficiency [10].The list of specific examples of AI applications to ultrasonography is expected to grow in the future.AI algorithms may augment the diagnostic accuracy and capability of ultrasonography examiners and are hoped to be particularly helpful for less-experienced examiners [11][12][13][14][15]. Ultrasonography is more widely used in clinical practice than computed tomography (CT) or magnetic resonance imaging (MRI), and it is performed by a more diverse range of medical professionals with varying levels of expertise, some of whom perform better than others.Typically, a single examiner interprets the findings and makes decisions on the fly while performing the examination.As a result, the greater operator-dependency and subjectivity of ultrasonography compared with CT or MRI are well-known issues.Therefore, one of the most eagerly anticipated benefits of applying AI in ultrasonography would be reduced variability between examiners.In this regard, AI may offer a unique opportunity to improve the performance of ultrasonography by removing variability between examiners.Nonetheless, it should be noted that the very nature of ultrasonography also poses challenges in the development and clinical implementation of AI for ultrasonography.First, the operator-dependency and subjectivity of ultrasonography introduce additional variability
Source: ULTRASONOGRAPHY
DOI: 10.14366/usg.20078
Author: Seong Ho Park
Institutions: Asan Medical Center, University of Ulsan
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W3095899746