Di Zhang, Xian-Ya Zhang, Ya-Yang Duan, Christoph F Dietrich, Xin-Wu Cui, Chao-Xue Zhang
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引用次数: 0
Abstract
Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of pathophysiology-related information from varied medical imaging and are progressively transforming medical practice. AI applications are extending into domains previously thought to be accessible only to human experts. Recent research has demonstrated that ultrasound-derived radiomics and deep learning represent an enticing opportunity to benefit preoperative evaluation and prognostic monitoring of diffuse and focal liver disease. This review summarizes the application of radiomics and deep learning in ultrasound liver imaging, including identifying focal liver lesions and staging of liver fibrosis, as well as the evaluation of pathobiological properties of malignant tumors and the assessment of recurrence and prognosis. Besides, we identify important hurdles that must be overcome while also discussing the challenges and opportunities of radiomics and deep learning in clinical applications.
期刊介绍：
The journal aims to promote ultrasound diagnosis by publishing papers in a variety of categories, including editorial letters, original papers, review articles, pictorial essays, technical developments, case reports, letters to the editor or occasional special reports (fundamental, clinical as well as methodological and educational papers).
The papers published cover the whole spectrum of the applications of diagnostic medical ultrasonography, including basic science and therapeutic applications.
The journal hosts information regarding the society''s activities, scheduling of accredited training courses in ultrasound diagnosis, as well as the agenda of national and international scientific events.
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