Abstract
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.
Highlights
The most common physical disability in children is cerebral palsy (CP)
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
Performance, expressed in percentages, of Naïve Bayes (NB) and Logistic Regression (LR) for classification using expert-defined features and discretization compared with level of agreement by clinicians, expressed as percentage of agreement (POA) as reported in [9] for a group of 28 trained raters with clinical background (RG1) and two expert raters (RG2)
Summary
The most common physical disability in children is cerebral palsy (CP). The prevalence of this neuromotor disorder is estimated at 2.11 per 1000 live births [1]. Nieuwenhuys et al [6] highlighted additional advantages of gait classification: “Apart from research applications, gait classifications can improve communication among health care workers by providing a tool for describing, evaluating, and comparing gait between and among patients or groups of patients It could aid lecturers teaching about gait in CP, serve as a tool for assessing treatment outcome, and potentially lead to a more in-depth understanding of the neurological cause of specific joint motion patterns, which may be associated with specific treatment indications.”. This 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. Two 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
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