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人工智能算法增强的运动抑制,模拟幼儿的骨软骨缺损。

AI algorithmically-enhanced motion suppression simulating an osteochondral defect in a young child.

作者信息

Aird Gregory A, Thacker Paul G, Amrami Kimberly K

机构信息

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

出版信息

Radiol Case Rep. 2024 Aug 24;19(11):5213-5215. doi: 10.1016/j.radcr.2024.07.135. eCollection 2024 Nov.

Abstract

Artificial intelligence (AI) in radiology has rapidly increased in our field and stands to allow more accurate diagnosis, quicker interpretations, easier workflows, and improved image quality. However, with superior image quality produced with the help of AI algorithms, one could begin to discount or even eliminate the review of nonalgorithmic enhanced images. At least currently, these images remain important. This case report demonstrates a unique anomaly simulating disease resulting from AI-enhanced motion suppression. On the original images, patient motion and an atypical linear motion artifact is obvious. However, the images reproduced using our AI motion artifact suppression algorithm suppressed nearly all (but not all) of the motion artifact resulting in what appeared to be an osteochondral lesion in a child's knee. This case illustrates the necessity for the interpreting radiologist to review both original acquisitions as well as AI-enhanced images, at least for the time being.

摘要

放射学领域中人工智能(AI)的应用迅速增加,有望实现更准确的诊断、更快的解读、更便捷的工作流程以及更高的图像质量。然而,借助人工智能算法生成的图像质量卓越,人们可能会开始忽视甚至不再审查非算法增强图像。至少目前而言,这些图像仍然很重要。本病例报告展示了一个因人工智能增强运动抑制而模拟疾病的独特异常情况。在原始图像上,患者的运动和非典型线性运动伪影很明显。然而,使用我们的人工智能运动伪影抑制算法重现的图像几乎抑制了所有(但并非全部)运动伪影,导致看起来像是儿童膝盖处的骨软骨损伤。该病例表明,至少目前,解读影像的放射科医生有必要同时审查原始采集图像和人工智能增强图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba38/11387521/36e3af11e38b/gr1.jpg

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