Implementation and validation of a clinical decision support system for quantifying plantar contact areas in podiatry

The present study demonstrates the MV 7000 digital podoscope's effectiveness in precisely analyzing foot prints using advanced image processing technologies. The system uses an algorithm that combines adaptive shading, Gaussian filtering, and morphological operations to optimize segmentation, with a notable innovation to the previous work: the dynamic threshold control that automatically adjusts parameters to maximize contrast. The results of validation against ImageJ (reference standard) showed a 95% concordance with an average error of 3.49%, within the clinically acceptable margin (5%). The metric calibration achieves a spatial resolution of 14.15009 px/cm, guaranteeing accuracy in measurements. The analysis of 50 plantar images confirms the robustness of the system in contour detection and calculation areas, supported by a strong statistical correlation (r=0.990, R 2=0.979). These results validate the reliability of the MV 7000 for possible clinical applications, including quantification of pressure areas and morphological analysis, with potential to implement convolutional neural networks (CNN) in future developments for automated classification of pathological patterns.