Faculty of Rehabilitation, Józef Piłsudski Academy of Physical Education, Warsaw, Poland.
School of Chemical and Environmental Engineering, Technical University of Crete, Chania, Greece.
PLoS One. 2024 Feb 13;19(2):e0297504. doi: 10.1371/journal.pone.0297504. eCollection 2024.
Hallux Valgus foot deformity affects gait performance. Common treatment options include distal oblique metatarsal osteotomy and chevron osteotomy. Nonetheless, the current process of selecting the appropriate osteotomy method poses potential biases and risks, due to its reliance on subjective human judgment and interpretation. The inherent variability among clinicians, the potential influence of individual clinical experiences, or inherent measurement limitations may contribute to inconsistent evaluations. To address this, incorporating objective tools like neural networks, renowned for effective classification and decision-making support, holds promise in identifying optimal surgical approaches. The objective of this cross-sectional study was twofold. Firstly, it aimed to investigate the feasibility of classifying patients based on the type of surgery. Secondly, it sought to explore the development of a decision-making tool to assist orthopedists in selecting the optimal surgical approach. To achieve this, gait parameters of twenty-three women with moderate to severe Hallux Valgus were analyzed. These patients underwent either distal oblique metatarsal osteotomy or chevron osteotomy. The parameters exhibiting differences in preoperative and postoperative values were identified through various statistical tests such as normalization, Shapiro-Wilk, non-parametric Wilcoxon, Student t, and paired difference tests. Two artificial neural networks were constructed for patient classification based on the type of surgery and to simulate an optimal surgery type considering postoperative walking speed. The results of the analysis demonstrated a strong correlation between surgery type and postoperative gait parameters, with the first neural network achieving a remarkable 100% accuracy in classification. Additionally, cases were identified where there was a mismatch with the surgeon's decision. Our findings highlight the potential of artificial neural networks as a complementary tool for surgeons in making informed decisions. Addressing the study's limitations, future research may investigate a wider range of orthopedic procedures, examine additional gait parameters and use more diverse and extensive datasets to enhance statistical robustness.
拇外翻足畸形会影响步态表现。常见的治疗选择包括远端斜行跖骨截骨术和 V 形截骨术。然而，目前选择合适截骨方法的过程存在潜在的偏差和风险，因为它依赖于主观的人为判断和解释。临床医生之间的固有变异性、个体临床经验的潜在影响或固有测量限制可能导致评估不一致。为了解决这个问题，引入神经网络等客观工具，这些工具以有效的分类和决策支持而闻名，可以为确定最佳手术方法提供帮助。本横断面研究有两个目的。首先，旨在研究基于手术类型对患者进行分类的可行性。其次，探讨开发一种决策工具以协助矫形外科医生选择最佳手术方法的可能性。为此，分析了 23 名患有中度至重度拇外翻的女性患者的步态参数。这些患者接受了远端斜行跖骨截骨术或 V 形截骨术。通过各种统计检验（如归一化、Shapiro-Wilk、非参数 Wilcoxon、学生 t 和配对差异检验），确定了术前和术后值存在差异的参数。基于手术类型和考虑术后行走速度来模拟最佳手术类型，构建了两个人工神经网络来对患者进行分类。分析结果表明，手术类型与术后步态参数之间存在很强的相关性，第一个神经网络在分类方面取得了惊人的 100%准确率。此外，还确定了一些与外科医生决策不匹配的病例。我们的研究结果强调了人工神经网络作为外科医生做出明智决策的补充工具的潜力。考虑到研究的局限性，未来的研究可能会调查更广泛的骨科手术、检查更多的步态参数，并使用更多样化和广泛的数据集来提高统计稳健性。
