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Explainable AI-based clinical decision support system for flatfoot classification using deep learning
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DOI：10.3389/fdgth.2026.1852739.png)
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Abstract
En 中文
IntroductionFlatfoot; also known as pes planus; is a deformity of the foot characterized by a decrease or absence of the medial longitudinal arch; which may lead to postural and locomotion abnormalities.MethodsThis study presents a comparative analysis of handcrafted feature-based machine learning (LBP with Random Forest; Decision Tree; Logistic Regression) and deep learning models (InceptionResNetV2; ResNet101V2; DenseNet201; DenseNet169; InceptionV3; Xception) for flatfoot classification. Monte Carlo cross-validation was employed with subject-wise splitting. SHAP and Grad-CAM were used for explainability.ResultsRandom Forest achieved 70% accuracy with LBP features. ResNet101V2-RMSprop achieved 92.86% ± 2.82% mean accuracy and 98.21% best single-run accuracy. Deep features with Decision Tree achieved 97% accuracy; 95% recall; and 100% specificity.DiscussionSHAP and Grad-CAM confirmed that the model focuses on clinically relevant regions: the medial longitudinal arch and calcaneus for pes planus; and the talus-navicular region for normal feet. The hybrid approach is suitable for clinical screening.
Keywords:
explainable AI
interpretable machine learning
local binary pattern
monte carlo cross validation
pes planus
ResNet101V2
RMSprop
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