Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China.
Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Med Ultrason. 2023 Dec 27;25(4):445-452. doi: 10.11152/mu-4080. Epub 2023 Aug 22.
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.
在过去的几年中，人工智能（AI）的发展，特别是在放射组学和深度学习方面，已经能够从各种医学影像中提取与病理生理学相关的信息，并逐渐改变医学实践。AI 应用正在扩展到以前被认为只能由人类专家访问的领域。最近的研究表明，超声衍生的放射组学和深度学习为术前评估和弥漫性和局灶性肝病的预后监测提供了一个诱人的机会。本文综述了放射组学和深度学习在超声肝脏成像中的应用，包括识别局灶性肝病变和肝纤维化分期，以及评估恶性肿瘤的病理生物学特性和评估复发和预后。此外，我们确定了必须克服的重要障碍，同时还讨论了放射组学和深度学习在临床应用中的挑战和机遇。
