Abstract：
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
摘要：
本文将对肌肉骨骼疾病检测中最广泛研究的深度学习（DL）应用进行透视回顾，这些应用在未来十年最有可能转化为常规临床实践。用于检测骨折的深度学习方法，估计小儿骨龄，计算骨骼测量值，如下肢对齐和Cobb角，和在X线片上对骨关节炎进行分级已被证明具有高诊断性能,其中许多这些应用现在可在临床实践中使用。许多研究还证明了使用DL方法在磁共振成像（MRI）上检测关节病理和表征骨肿瘤的可行性。然而,在MRI上检测肌肉骨骼疾病很困难，因为它需要多任务，在具有不同组织对比度的多个图像切片上的复杂异常的多类别检测。由于常规MRI协议中使用的各种扫描仪和脉冲序列引起的图像质量波动，因此用于MRI上肌肉骨骼疾病检测的DL方法的通用性也具有挑战性。当前用于肌肉骨骼疾病检测的DL方法的诊断性能必须在精心设计的前瞻性研究中使用在具有不同成像参数和成像硬件的不同机构获得的大图像数据集进行进一步评估，然后才能在临床实践中完全实施。未来的研究还必须调查当前DL方法的真正临床益处，并确定它们是否可以提高质量，降低错误率，改进工作流程，并减少放射科医生的疲劳和倦怠，所有这些都权衡了成本。
