基于数值模型的机器学习算法在桡骨远端骨折愈合不同阶段的开发。
Development of numerical model-based machine learning algorithms for different healing stages of distal radius fracture healing.
机构信息
Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia.
Centre for Limb Lengthening & Reconstruction, Epworth Hospital Richmond, Richmond, Victoria, Australia.
出版信息
Comput Methods Programs Biomed. 2023 May;233:107464. doi: 10.1016/j.cmpb.2023.107464. Epub 2023 Mar 5.
BACKGROUND AND OBJECTIVES
Early therapeutic exercises are vital for the healing of distal radius fractures (DRFs) treated with the volar locking plate. However, current development of rehabilitation plans using computational simulation is normally time-consuming and requires high computational power. Thus, there is a clear need for developing machine learning (ML) based algorithms that are easy for end-users to implement in daily clinical practice. The purpose of the present study is to develop optimal ML algorithms for designing effective DRF physiotherapy programs at different stages of healing.
METHOD
First, a three-dimensional computational model for the healing of DRF was developed by integrating mechano-regulated cell differentiation, tissue formation and angiogenesis. The model is capable of predicting time-dependant healing outcomes based on different physiologically relevant loading conditions, fracture geometries, gap sizes, and healing time. After being validated using available clinical data, the developed computational model was implemented to generate a total of 3600 clinical data for training the ML models. Finally, the optimal ML algorithm for each healing stage was identified.
RESULTS
The selection of the optimal ML algorithm depends on the healing stage. The results from this study show that cubic support vector machine (SVM) has the best performance in predicting the healing outcomes at the early stage of healing, while trilayered ANN outperforms other ML algorithms in the late stage of healing. The outcomes from the developed optimal ML algorithms indicate that Smith fractures with medium gap sizes could enhance the healing of DRF by inducing larger cartilaginous callus, while Colles fractures with large gap sizes may lead to delayed healing by bringing excessive fibrous tissues.
CONCLUSIONS
ML represents a promising approach for developing efficient and effective patient-specific rehabilitation strategies. However, ML algorithms at different healing stages need to be carefully chosen before being implemented in clinical applications.
背景与目的
对于使用掌侧锁定板治疗的桡骨远端骨折（DRF），早期治疗性运动至关重要。但是，目前使用计算模拟开发康复计划通常既耗时又需要高计算能力。因此，非常需要开发易于最终用户在日常临床实践中实施的基于机器学习（ML）的算法。本研究的目的是开发最佳的 ML 算法，以设计不同愈合阶段的有效 DRF 物理治疗方案。
方法
首先，通过整合机械调节细胞分化、组织形成和血管生成，开发了用于治疗 DRF 愈合的三维计算模型。该模型能够根据不同的生理相关加载条件、骨折几何形状、间隙大小和愈合时间预测时间依赖性愈合结果。在使用现有临床数据验证后，将开发的计算模型用于生成总共 3600 个临床数据，以训练 ML 模型。最后，确定了每个愈合阶段的最佳 ML 算法。
结果
选择最佳 ML 算法取决于愈合阶段。本研究的结果表明，在愈合早期，立方支持向量机（SVM）在预测愈合结果方面表现最佳，而在愈合后期，三层 ANN 优于其他 ML 算法。开发的最佳 ML 算法的结果表明，中等间隙的 Smith 骨折可以通过诱导更大的软骨痂来增强 DRF 的愈合，而大间隙的 Colles 骨折可能会通过带来过多的纤维组织而导致愈合延迟。
结论
ML 代表了开发高效和有效的患者特定康复策略的有前途的方法。但是，在将 ML 算法应用于临床应用之前，需要仔细选择不同愈合阶段的 ML 算法。
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