论文标题
基于深度学习的端到端端到端诊断系统，用于股骨血管的血管坏死
Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head
论文作者
论文摘要
作为股骨头（AVNFH）血管坏死的第一个诊断成像方式，对骨射线片的准确分期avnfh对骨科医生来说至关重要但又具有挑战性。因此，我们提出了一个基于深度学习的AVNFH诊断系统（AVN-NET）。拟议的AVN-NET读取骨盆的平原X光片，进行诊断并自动可视化结果。对深度卷积神经网络进行了训练，可以提供端到端诊断解决方案，涵盖股骨头检测任务，检查视图识别，侧分类，AVNFH诊断和关键的临床注释。 AVN-NET能够获得AVNFH检测中的0.97（95％CI：0.97-0.98）的最新测试AUC，并且与所有诊断测试中经验丰富的骨科医生相比（p <0.01）中经验丰富的骨科医生。此外，分别进行了两项现实世界中的试点研究，分别用于诊断支持和教育援助，以评估AVN-NET的效用。实验结果是有希望的。以AVN-NET诊断为参考，所有骨科医生的诊断准确性和一致性大大提高，同时仅需要1/4时间。与对照组相比，使用AVN-NET自我研究AVNFH诊断的学生可以更好，更快。据我们所知，这项研究是关于对AVNFH的深度学习诊断系统的前瞻性研究的首次研究，该研究通过进行了两项代表现实世界应用方案的试验研究。我们已经证明，拟议的AVN-NET实现了专家级的AVNFH诊断性能，在临床决策中提供了有效的支持，并有效地将临床经验传递给了学生。
As the first diagnostic imaging modality of avascular necrosis of the femoral head (AVNFH), accurately staging AVNFH from a plain radiograph is critical yet challenging for orthopedists. Thus, we propose a deep learning-based AVNFH diagnosis system (AVN-net). The proposed AVN-net reads plain radiographs of the pelvis, conducts diagnosis, and visualizes results automatically. Deep convolutional neural networks are trained to provide an end-to-end diagnosis solution, covering tasks of femoral head detection, exam-view identification, side classification, AVNFH diagnosis, and key clinical notes generation. AVN-net is able to obtain state-of-the-art testing AUC of 0.97 (95% CI: 0.97-0.98) in AVNFH detection and significantly greater F1 scores than less-to-moderately experienced orthopedists in all diagnostic tests (p<0.01). Furthermore, two real-world pilot studies were conducted for diagnosis support and education assistance, respectively, to assess the utility of AVN-net. The experimental results are promising. With the AVN-net diagnosis as a reference, the diagnostic accuracy and consistency of all orthopedists considerably improved while requiring only 1/4 of the time. Students self-studying the AVNFH diagnosis using AVN-net can learn better and faster than the control group. To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios. We have demonstrated that the proposed AVN-net achieves expert-level AVNFH diagnosis performance, provides efficient support in clinical decision-making, and effectively passes clinical experience to students.
