Discussion
This study employed both unsupervised and supervised machine-learning techniques to analyze the frontal plane kinematics during SU and SD for the detection and classification of EOA. The results of this study support our hypotheses. Significant differences in frontal plane kinematics were observed between participants with and without EOA during SU and SD tests, confirming our first hypothesis. The Louvain clustering algorithm successfully identified four distinct movement patterns with varying proportions of EOA cases, supporting our second hypothesis. Finally, the supervised ML models achieved excellent classification performance (AUC = 1.000 for Random Forest, gradient boosting, and decision tree algorithms on the test dataset), strongly supporting our third hypothesis that frontal plane kinematics during functional tasks can accurately predict EOA status. These methods show promising potential for identifying distinct movement patterns in individuals with EOA. They also enable accurate classification, providing insights into the biomechanical characteristics of EOA.

However, we acknowledge that the perfect classification performance (AUC = 1.000) achieved by our tree-based models on the test dataset warrants careful consideration of potential overfitting. Despite implementing several methodological safeguards—including strict separation of training and test sets, k-fold cross-validation during model development, regularization through hyperparameter tuning, and feature selection based on domain knowledge—the possibility of overfitting cannot be entirely ruled out, particularly given our relatively small sample size. Several factors may contribute to the high model performance: (1) the distinct biomechanical differences between individuals with and without EOA may be particularly pronounced in our specific population of recreational table tennis players; (2) our focus on frontal plane kinematics during challenging functional tasks may have captured highly discriminative movement patterns; and (3) our careful data preprocessing and feature extraction may have enhanced signal-to-noise ratio. Nevertheless, these exceptionally high performance metrics should be interpreted with caution until validated in larger, independent cohorts. External validation with diverse populations would be essential to establish the generalizability of these findings and confirm the true predictive power of our approach.

Compared to conventional diagnostic methods for OA (radiography, MRI, and arthroscopy), our approach offers several distinct advantages. Traditional imaging methods often fail to detect early cartilage changes ( Guermazi et al., 2011 ), have limited accessibility due to high cost, require specialized facilities, and need highly trained specialists for interpretation. Our approach of using 2D video analysis of functional tasks with machine learning focuses on functional impairments rather than structural changes, potentially detecting disease earlier. It requires minimal equipment (smartphone camera and basic markers) and can be implemented quickly in routine clinical assessment, making it more accessible for widespread screening and early detection efforts.

While deep learning approaches have shown promising results in medical applications, we opted for traditional machine learning methods in this study for several reasons. First, our sample size (86 legs) is more suitable for traditional ML methods, which can perform effectively with smaller datasets compared to deep learning approaches that typically require larger training sets. Second, traditional ML methods offer better interpretability of feature importance, which is crucial for understanding the specific biomechanical factors contributing to EOA detection. This interpretability is particularly important for clinical applications where clinicians need to understand the basis for the model’s predictions. Finally, traditional ML methods require less computational resources and can be more easily implemented in clinical settings, aligning with our goal of developing a practical diagnostic tool. These considerations, combined with the high performance achieved by our selected ML algorithms (as demonstrated in our results), support our methodological choice.

The Louvain clustering algorithm identified four distinct clusters, each with distinct kinematic profiles, aligned with the growing recognition of OA as a heterogeneous condition with distinct phenotypes ( Deveza et al., 2017 ; Van Spil et al., 2020 ). The varying proportions of EOA cases across clusters suggested that certain kinematic patterns may be more strongly associated with EOA.

C1 exhibited a valgus movement pattern during SD, consistent with previous findings of increased knee valgus during gait in individuals with medial compartment knee OA and those at risk of OA progression ( Chang et al., 2004 ; Felson et al., 2013 ; Sharma et al., 2001 ). This pattern may represent a compensatory mechanism to reduce the medial compartment loading, as suggested by several biomechanical studies ( Mündermann, Dyrby & Andriacchi, 2005 ; Simic et al., 2011 ). C2 was characterized by a higher BMI, which is supported by previous research showing that individuals with a higher BMI exhibited greater knee abduction moments during gait, potentially increasing the risk of OA development and progression ( Blazek et al., 2013 ; Harding et al., 2012 ; Messier et al., 2016 ). C3 demonstrated the highest outward kinematics during SD, particularly in the pelvis and femur horizontal displacements, aligning with studies that observed increased lateral trunk lean and altered hip kinematics during stair descent in individuals with knee OA ( Hicks-Little et al., 2012 ; Hicks-Little et al., 2011 ; Igawa & Katsuhira, 2014 ). C4 exhibited the highest outward kinematics during SU, particularly for the pelvis, femur, knee, and lower leg horizontal displacements. This exaggerated movement pattern may indicate compensatory strategies or instability associated with early joint degeneration, which is consistent with studies that have observed altered frontal plane kinematics during stair ascent in individuals with knee OA ( Gonçalves et al., 2017 ).

The supervised learning models demonstrated high performance in classifying the EOA status, with Random Forest, gradient boosting, and decision tree algorithms achieving perfect classification (AUC = 1.000) on the test dataset. These results surpass the performance of previous studies, such as Pedoia et al. (2019) , who achieved an AUC of 0.89 using MRI data ( Pedoia et al., 2019 ); Ashinsky et al. (2017) , who reported an accuracy of 0.75 using T2 maps ( Ashinsky et al., 2017 ); and Mezghani et al. (2017) , who achieved AUC of 0.85 in classifying knee OA severity using gait kinematics ( Mezghani et al., 2017 ). The superior performance in our study may be attributed to the use of functional kinematic data from the SU and SD tests, which potentially capture more relevant information about joint function and early degenerative changes than static imaging or level walking data. Moreover, our 2D video analysis approach offers significant advantages over traditional 3D motion capture or wearable sensor systems in terms of accessibility, cost-effectiveness, and ease of implementation in clinical settings, while still providing high classification accuracy for EOA detection.

The consistently high performance of the decision tree-based algorithms (Random Forest, gradient boosting, and decision tree) can be attributed to their ability to capture nonlinear relationships and interactions between features, handle high-dimensional data, and automatically select the most relevant features. These characteristics are particularly beneficial in the complex biomechanical context of EOA, where the relationship between joint movement and disease status may be highly nonlinear and context-dependent.

The effectiveness of SU and SD kinematics in EOA classification can be explained by the biomechanical challenges of these tasks. These activities require greater joint range of motion and higher forces than level walking, potentially exacerbating the subtle joint alterations present in EOA. Previous studies have shown that stair negotiation is more demanding and results in higher knee joint moments than level walking ( Nadeau, McFadyen & Malouin, 2003 ), potentially unmasking compensatory mechanisms or joint instabilities that are not apparent during less challenging tasks.

The feature importance analysis consistently highlighted AHD-SU, FHD-SU, PHD-SD, and FHD-SD as the most influential predictors. The prominence of AHD-SU as a predictive feature aligns with the concept of OA as a “whole-joint” disease affecting multiple tissues and biomechanical factors ( Brandt et al., 2006 ; Loeser et al., 2012 ). Increased ankle movement in the frontal plane during weight-bearing activities may indicate compensatory strategies to maintain balance or redistribute load in the presence of early knee joint changes. This observation is supported by studies reporting altered ankle kinematics and kinetics in individuals with knee OA ( Levinger et al., 2012 ; Mündermann, Dyrby & Andriacchi, 2005 ). Specifically, individuals with knee OA often exhibit greater lateral foot pressure and altered ankle eversion during weight-bearing tasks, which may manifest as increased horizontal ankle displacement in our 2D analysis. The importance of FHD-SU and PHD-SD in predicting the EOA status is consistent with previous research highlighting the role of proximal joint kinematics in knee OA. Studies have observed altered hip and pelvic kinematics during stair ascent and descent in individuals with knee OA, suggesting potential compensatory mechanisms to reduce knee joint loading ( Hicks-Little et al., 2012 ). Greater femoral horizontal displacement during step-up likely reflects altered frontal plane hip control, which can modify the distribution of forces across the knee joint surfaces. The association between increased PHD-SD and EOA may represent a compensatory lateral trunk lean strategy, which has been documented as a mechanism to reduce knee adduction moment in individuals with medial compartment knee OA ( Mündermann, Dyrby & Andriacchi, 2005 ; Simic et al., 2011 ). This altered movement pattern shifts the body’s center of mass laterally, potentially reducing medial compartment loading but creating abnormal movement patterns that may be detected in early disease stages.

The models showed divergent results regarding the direction of FHD-SD in predicting EOA. The Random Forest model indicated that increased outward movement was associated with EOA, whereas the gradient boosting and decision tree models suggested that inward (valgus) movement was predictive of EOA. This discrepancy may reflect the complexity of the EOA biomechanics and the potential existence of different subgroups within the EOA population. Similar conflicting findings have been reported in the literature ( Astephen et al., 2008 ; Favre, Erhart-Hledik & Andriacchi, 2014 ), underscoring the need for further research to elucidate the specific biomechanical patterns associated with the different stages and subtypes of knee OA. The divergent findings regarding FHD-SD across the different algorithms may also be attributed to the specific strengths of each method. Random Forest may capture a more global pattern of increased outward movement associated with EOA, whereas gradient boosting and decision tree may identify more localized or stage-specific patterns, where inward movement is indicative of EOA. This discrepancy highlights the potential of machine-learning approaches to uncover complex nonlinear relationships in biomechanical data that may not be apparent in traditional statistical analyses.

The present study has several key limitations that should be acknowledged. First, our cross-sectional design limits the ability to establish causal relationships between kinematic patterns and EOA development. Longitudinal studies are needed to determine whether the observed patterns precede or result from EOA. Second, while sufficient for the current analysis, our relatively small sample (43 participants, 86 legs) may limit the generalizability of our findings. Despite implementing methodological safeguards against overfitting (including five-fold cross-validation, maintaining a separate test set, and using multiple performance metrics), caution is warranted when interpreting the results. In addition, due to the relatively small dataset size, we were unable to create a separate validation set beyond our training and test sets. This three-way split (training, validation, and test sets) is considered ideal practice in machine learning as it allows for unbiased hyperparameter optimization on the validation set before final evaluation on the test set. Larger, more diverse cohorts should be examined to validate and refine these models. Third, our 2D video analysis methodology cannot capture out-of-plane movements, which may result in measurement errors when movements occur outside the primary recording plane. Additionally, perspective errors and marker occlusion can affect measurement accuracy. Despite these limitations, we selected 2D video analysis for its greater clinical applicability and feasibility compared to 3D motion capture, which typically requires specialized laboratory settings, expensive equipment, and technical expertise. Fourth, focusing only on frontal plane kinematics may not capture the full complexity of EOA-related movement patterns. Future studies should incorporate multiplane analysis and additional biomechanical variables ( e.g. , kinetics and muscle activation) for a more comprehensive understanding of EOA-related movement patterns. Fifth, while the EOAQ was developed by an international expert panel specifically for early OA detection, comprehensive validation studies establishing its sensitivity and specificity have not yet been published. The EOAQ represents one of the first standardized instruments designed specifically for detecting early-stage knee OA before significant structural changes are evident on conventional imaging, making it suitable for our research question. However, future studies would benefit from correlating EOAQ results with other clinical and imaging biomarkers to establish its diagnostic accuracy.

Future research should expand upon our current methodology in several key directions. First, deep learning models (such as convolutional neural networks and recurrent neural networks) could be implemented to potentially improve classification performance and automatically extract relevant features from raw kinematic data without the need for manual feature engineering. Second, investigating the ability of machine learning models to distinguish between different severity levels of EOA would provide valuable clinical insights for disease progression monitoring and treatment planning. Third, incorporating different view variations (including sagittal and transverse planes) would offer a more comprehensive three-dimensional analysis of movement patterns associated with EOA. Finally, developing techniques to address self-occlusion during movement analysis would improve the reliability of kinematic measurements, particularly in complex functional tasks. These advancements would collectively enhance the clinical applicability and diagnostic accuracy of machine learning-based EOA detection using kinematic data.