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人工智能在肌肉骨骼成像中的应用:从请求到报告。

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.

机构信息

Department of Diagnostic Radiology, 54473McGill University Health Center, Montreal, Quebec, Canada.

Department of Radiology, 12297NYU Langone Medical Center/NYU Langone Orthopedic Center, New York, NY, USA.

出版信息

Can Assoc Radiol J. 2021 Feb;72(1):45-59. doi: 10.1177/0846537120947148. Epub 2020 Aug 18.

Abstract

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.

摘要

人工智能(AI)将改变影像学价值链的每一个环节,包括解释性和非解释性组件。放射科医生应该熟悉 AI 的发展,以便在临床实施中成为领导者。本文通过骨骼肌肉放射学的整个成像周期,从申请单的填写到报告的生成,探讨了 AI 的影响,并增加了加拿大的视角。AI 可能辅助的非解释性任务包括选择合适的影像学检查、自动检查方案设置、优化安排、缩短磁共振成像采集时间、减少伪影和辐射剂量的计算机断层成像、以及放射学报告生成和利用的新方法。AI 用于图像解释的应用包括骨龄测定、身体成分测量、骨质疏松症筛查、骨折识别、节段性脊柱病变评估、骨转移的检测和时间监测、原发性骨和软组织肿瘤的诊断以及骨关节炎分级。

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