Fontalis Andreas, Zhao Baixiang, Putzeys Pierre, Mancino Fabio, Zhang Shuai, Vanspauwen Thomas, Glod Fabrice, Plastow Ricci, Mazomenos Evangelos, Haddad Fares S
Department of Trauma and Orthopaedic Surgery, University College Hospital, London, UK.
Division of Surgery and Interventional Science, University College London, London, UK.
Bone Jt Open. 2024 Aug 14;5(8):671-680. doi: 10.1302/2633-1462.58.BJO-2024-0020.R1.
Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement.
This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.
We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM's prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%).
This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential.
根据个体脊柱骨盆生物力学和表型精确植入物定位对于全髋关节置换术（THA）的稳定性至关重要。尽管有一些关于不稳定预测的研究，但利用人工智能（AI）的研究仍存在显著差距。我们的初步研究目的是评估开发一种针对个体脊柱骨盆力学和患者表型的人工智能算法以预测撞击的可行性。
这项跨两个中心的国际多中心前瞻性队列研究纳入了157名接受初次机器人手臂辅助THA的成年人。使用机器人软件的虚拟运动范围（ROM）工具识别特定屈伸姿势下的撞击。主要人工智能模型，即轻梯度提升机（LGBM），使用表格数据预测撞击的存在、方向（屈曲或伸展）和类型。评估了一个将表格数据与骨盆前后位平片相结合的二级模型，以评估预测准确性的任何潜在提高。
我们通过对基线脊柱骨盆特征和手术规划参数的分析确定了九个预测因素。使用五折交叉验证，LGBM的撞击预测准确率达到70.2%。对于撞击数据，LGBM估计方向的准确率为85%，而支持向量机（SVM）确定撞击类型的准确率为72.9%。将成像数据与多层感知器（表格）和卷积神经网络（X光片）相结合后，LGBM的预测准确率为68.1%。联合模型和仅使用LGBM的模型的撞击方向预测率相似（约84.5%）。
本研究是利用人工智能在THA中进行撞击预测的开创性努力，使用了全面的真实世界临床数据集。我们的机器学习算法在预测撞击、其类型和方向方面显示出有前景的准确性。虽然在深度学习算法中添加成像数据并未提高准确性，但诸如地标标记等精细注释的潜力为未来的改进提供了途径。在临床应用之前，对该算法进行外部验证和大规模测试至关重要。
