RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Conclusion
This study demonstrated the potential of ML analysis of frontal plane kinematics during SU and SD tests to detect and classify EOA among recreational table tennis players. These findings contribute to a growing body of evidence supporting the use of functional biomechanical assessments in EOA diagnosis and management. While these results are promising, they should be considered preliminary given our relatively small and specific study population. Future research should focus on validating these findings in larger, more diverse cohorts, investigating the longitudinal changes in kinematic patterns during EOA progression, and exploring the integration of kinematic data with other clinical and imaging biomarkers to enhance the detection and prognosis of EOA.
Figure 1: Methodological framework for machine learning-based early osteoarthritis detection and classification.
The systematic approach implemented in this study, including participant selection, data acquisition, preprocessing of kinematic features, unsupervised clustering analysis, supervised model development with cross-validation, and feature importance evaluation. BMI, body mass index; EOA, early osteoarthritis; EOAQ, Early Osteoarthritis Questionnaire; AUC, area under the curve.

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DOI: 10.7717/peerj.19471/fig-1
Figure 2: Process of kinematic parameters extraction for step up and down.

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DOI: 10.7717/peerj.19471/fig-2
Figure 3: (A) Five yellow spherical markers in the anatomical landmarks for initial position during step-up using two-dimensional video analysis. (B) Assessment of the horizontal displacement based on trajectory of five key anatomical points during step-up in the frontal plane. (C) Assessment of the horizontal displacement based on trajectory of five key anatomical points during step-down in the frontal plane.

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DOI: 10.7717/peerj.19471/fig-3
Figure 4: Performance metrics of six machine learning algorithms in the training and test set.

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DOI: 10.7717/peerj.19471/fig-4
Figure 5: (A) Feature permutation importance of decision tree, (B) SHAP analyses of decision tree, (C) feature permutation importance of gradient boosting, (D) SHAP analyses of gradient boosting, (E) feature permutation importance of Random Forest, (F) SHAP analyses of Random Forest in the training set for classifying groups with and without EOA.
EOA, Early osteoarthritis; SU, step-up; SD, step-down; PHD, pelvic horizontal displacement; FHD, femur horizontal displacement; KHD, knee horizontal displacement; LHD, lower leg horizontal displacement; AHD, ankle horizontal displacement.

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DOI: 10.7717/peerj.19471/fig-5
Participants characteristics.
Comparisons of features between the clusters classified by Louvain clustering unsupervised machine learning.
Performance metrics of six machine-learning algorithms for classifying groups with and without EOA in the training and test datasets.