医学
手腕痛
手腕
磁共振成像
纤维软骨
放射科
眼泪
医学影像学
腕关节损伤
计算机断层摄影术
人工智能
医学物理学
计算机科学
外科
病理
骨关节炎
关节软骨
替代医学
出处
期刊:Arthroscopy
[Elsevier]
日期:2022-08-01
卷期号:38 (8): 2425-2426
被引量:2
标识
DOI:10.1016/j.arthro.2022.05.016
摘要
Accurate diagnosis of the etiology of ulnar-sided wrist pain and injury to the triangular fibrocartilage complex, particularly Palmar 1B tears, can prove to be challenging. Multiple peer-reviewed studies have demonstrated that accurate diagnosis and treatment of tears of the triangular fibrocartilage complex through nonoperative and operative means, including arthroscopy, can result in improved patient outcomes and function. One of the keys to successful treatment, however, is accurate diagnosis. While our current imaging modalities help to provide additional data for the assessment of this pathology, magnetic resonance imaging and computed tomography scans have limitations. Thus, employing the power of artificial intelligence and deep learning to ultrasound assessment of this injury is appealing. Efficient integration of this technology into daily practice has potential to bolster diagnostics not only in large medical centers but also in underserved areas with limited access to magnetic resonance imaging and computed tomography.
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