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用于肝脏疾病的基于人工智能的超声成像技术。

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases.

作者信息

Ma Longfei, Wang Rui, He Qiong, Huang Lijie, Wei Xingyue, Lu Xu, Du Yanan, Luo Jianwen, Liao Hongen

机构信息

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.

Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China.

出版信息

ILIVER. 2022 Nov 16;1(4):252-264. doi: 10.1016/j.iliver.2022.11.001. eCollection 2022 Dec.

Abstract

Ultrasound (US) imaging is a non-invasive, real-time, economical, and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases. Artificial intelligence (AI) technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data. This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases, promote clinical treatment of the liver, and predict the response of the liver after treatment. This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases. Covered topics include steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications. At present, the field of AI in US imaging is still in its early stages. With the future progress of AI technology, AI-based US imaging can further improve diagnosis, reduce medical costs, and optimize US-based clinical workflow. This technology has broad prospects for application to hepatic diseases.

摘要

超声(US)成像是一种非侵入性、实时、经济且便捷的成像方式,已广泛应用于肝脏疾病的诊断和治疗。人工智能(AI)技术可以根据临床专家的经验和从训练数据中获得的知识进行预测或决策。该技术可帮助临床医生提高肝脏疾病的检测效率并进行评估,推动肝脏疾病的临床治疗,并预测治疗后肝脏的反应。本综述总结了当前超声技术及相关人工智能方法在肝脏疾病诊断和治疗方面的快速发展。涵盖的主题包括脂肪变性分级、纤维化分期、肝脏局灶性病变的检测、超声图像分割、多模态图像配准及其他应用。目前,人工智能在超声成像领域仍处于早期阶段。随着人工智能技术未来的进步,基于人工智能的超声成像可进一步改善诊断、降低医疗成本并优化基于超声的临床工作流程。该技术在肝脏疾病应用方面具有广阔的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1135/12212738/a3ec0596b27f/gr1.jpg

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