Department of Orthopaedic Surgery, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.
Sci Rep. 2022 Jun 14;12(1):9826. doi: 10.1038/s41598-022-14006-2.
Recent studies have focused on hammering sound analysis during insertion of the cementless stem to decrease complications in total hip arthroplasty. However, the nature of the hammering sound is complex to analyse and varies widely owing to numerous possible variables. Therefore, we performed a preliminary feasibility study that aimed to clarify the accuracy of a prediction model using a machine learning algorithm to identify the final rasping hammering sound recorded during surgery. The hammering sound data of 29 primary THA without complication were assessed. The following definitions were adopted. Undersized rasping: all undersized stem rasping before the rasping of the final stem size, Final size rasping: rasping of the final stem size, Positive example: hammering sound during final size rasping, Negative example A: hammering sound during minimum size stem rasping, Negative example B: hammering sound during all undersized rasping. Three datasets for binary classification were set. Finally, binary classification was analysed in six models for the three datasets. The median values of the ROC-AUC in models A-F among each dataset were dataset a: 0.79, 0.76, 0.83, 0.90, 0.91, and 0.90, dataset B: 0.61, 0.53, 0.67, 0.69, 0.71, and 0.72, dataset C: 0.60, 0.48, 0.57, 0.63, 0.67, and 0.63, respectively. Our study demonstrated that artificial intelligence using machine learning was able to distinguish the final rasping hammering sound from the previous hammering sound with a relatively high degree of accuracy. Future studies are warranted to establish a prediction model using hammering sound analysis with machine learning to prevent complications in THA.
最近的研究集中在敲击声分析上，以降低全髋关节置换术的并发症。然而，由于可能存在许多变量，敲击声的性质非常复杂，变化也非常大。因此，我们进行了一项初步的可行性研究，旨在使用机器学习算法来确定在手术过程中记录的最终锉削敲击声的预测模型的准确性。评估了 29 例无并发症的初次全髋关节置换术的敲击声数据。采用以下定义：
小尺寸锉削：所有小于最终尺寸的锉削之前的锉削，
最终尺寸锉削：最终尺寸的锉削，
阳性示例：最终尺寸锉削期间的敲击声，
阴性示例 A：最小尺寸的柄锉削期间的敲击声，
阴性示例 B：所有小尺寸锉削期间的敲击声。
为二进制分类设置了三个数据集。
最后，在六个模型中对三个数据集进行了二进制分类分析。在每个数据集的模型 A-F 中，ROC-AUC 的中位数分别为数据集 a：0.79、0.76、0.83、0.90、0.91 和 0.90，数据集 B：0.61、0.53、0.67、0.69、0.71 和 0.72，数据集 C：0.60、0.48、0.57、0.63、0.67 和 0.63。
我们的研究表明，使用机器学习的人工智能能够以相对较高的准确度区分最终锉削敲击声和之前的敲击声。需要进一步的研究来建立使用机器学习的敲击声分析预测模型，以预防全髋关节置换术的并发症。
