Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

BackgroundThis study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions.HypothesesTwo main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification.FindingsThis study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability.背景：本研究旨在借助一项新近开展的德尔菲共识（Delphi-consensus）研究所获取的专家知识，提升脑性瘫痪（cerebral palsy，CP）儿童关节运动步态模式的自动概率分类性能。为此，本研究分别采用朴素贝叶斯（Naïve Bayes）与逻辑回归（Logistic Regression）分类算法，并对专家知识（专家定义且离散化的特征）的使用程度进行了差异化设置。本研究共纳入356名受试者的1719次步态试验数据，用于验证11种关节运动的分类性能。 假设：本研究提出两项核心假设：其一，通过德尔菲共识研究获取的脑性瘫痪儿童关节运动步态模式，可通过概率方法实现自动分类，其分类精度与临床专家的手动分类精度相当；其二，在相关步态特征选择及连续特征离散化过程中融入临床专家知识，可提升关节运动模式自动概率分类的性能。 研究结果：本研究为第一项假设提供了客观佐证：借助德尔菲共识研究获取的专家知识开展的自动概率步态分类，其分类精度达91%，与两名专家评分者的分类精度（90%）相近，且高于非专家评分者的分类精度（78%）。针对第二项假设，本研究表明：相较于采用专家定义且离散化的特征，使用自动特征选择与离散化等更先进的机器学习技术，可使关节运动分类性能略有提升。但该性能提升幅度有限，且无法抵消额外增加的计算成本，以及临床可解释性丧失的更高风险，而后者会对该方法的临床接受度与应用前景造成不利影响。