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Experimental Methods to Study Human Postural Control
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Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture

Carlo Dindorf1, Oliver Ludwig1, Steven Simon1

  • 1Department of Sport Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany.

Bioengineering (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning (ML) system using explainable artificial intelligence (XAI) to objectively diagnose hyperlordosis and hyperkyphosis. The system improves diagnostic accuracy and supports personalized medicine by providing human-friendly interpretations of posture data.

Keywords:
artificial intelligencebiomechanicsconfident learningexplainable artificial intelligencehuman-in-the-loophyperkyphosishyperlordosislabel errorsmachine learningposture

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Medical Imaging

Background:

  • Postural deficits like hyperlordosis and hyperkyphosis present diagnostic challenges due to subjective assessments.
  • Existing machine learning (ML) approaches for posture analysis have limited human-friendly interpretations.
  • Explainable artificial intelligence (XAI) offers potential for objective, data-driven medical decision support.

Purpose of the Study:

  • To develop an objective, data-driven ML system for diagnosing hyperlordosis and hyperkyphosis.
  • To enhance ML interpretability using counterfactual explanations (CFs) for user-friendly insights.
  • To improve diagnostic accuracy and support personalized therapeutic adaptations.

Main Methods:

  • Collected posture data from 1151 subjects using stereophotogrammetry.
  • Employed a Gaussian process classifier trained on expert-classified posture data.
  • Utilized counterfactual explanations (CFs) for model interpretation and confident learning for label re-evaluation.

Main Results:

  • Achieved very good classification performance for both hyperlordosis and hyperkyphosis.
  • Re-evaluation and correction of test labels significantly improved classification performance (MPRAUC = 0.97).
  • Statistical evaluation indicated the plausibility of the counterfactual explanations.

Conclusions:

  • The proposed ML system offers an objective approach to diagnosing postural deficits, reducing diagnostic errors.
  • The human-friendly CF interpretations can aid clinicians in decision-making and personalized treatment planning.
  • This approach could form the basis for preventive posture assessment applications.