Explainable machine learning with wavelet-enhanced multi-scale texture analysis of diabetes-related heel pad ultrasound texture differences.
Authors
Affiliations (8)
Affiliations (8)
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.
- Suzhou Xiacheng Intelligent Technology Co. Ltd., Suzhou, China.
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.
- JiangWan Hospital of Hongkou district, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Faculty of Intelligence Technology, Shanghai Institute of Technology, Shanghai, China.
Abstract
Diabetes is associated with structural and functional differences in plantar soft tissues, which may be reflected in ultrasound texture patterns. However, existing ultrasound texture studies are limited by single-scale feature extraction and limited feature-level interpretability. To develop an explainable machine learning framework incorporating a wavelet-enhanced multi-scale texture feature extraction module (WEMS-TFE), and to investigate whether heel pad ultrasound texture features differ between patients with diabetes without active foot ulcers and healthy controls. Heel pad ultrasonography was performed in 76 patients with diabetes (without active foot ulcers) and 47 healthy controls. Ultrasound images of the heel pad were analyzed at the microchamber and macrochamber levels. The WEMS-TFE module was proposed to extract multi-scale, multi-directional texture features via two-dimensional discrete wavelet decomposition combined with the gray-level co-occurrence matrix (GLCM). Features with significant between-group differences were selected and evaluated across seven machine learning classifiers using repeated cross-validation with participant-disjoint data partitions. SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions in a representative XGBoost model. The WEMS-TFE scheme achieved the highest cross-model composite performance and robustness among the five evaluated feature extraction schemes. Cross-classifier importance analysis identified 6 features that recurred across multiple classifiers. Consistently, most of these also ranked among the important features in the XGBoost SHAP analysis, and two representative features showed clear value-dependent contribution patterns. The identified features therefore reflected group-associated image-texture differences, although their associations may be affected by residual demographic confounding. The proposed framework, with the WEMS-TFE module as its core component, enables quantitative characterization of group-associated differences in heel pad ultrasound texture features. The findings provide imaging-based evidence of group-level texture differences in the heel pad and support further investigation of plantar tissue characteristics in diabetes, while acknowledging that these differences may be affected by residual demographic confounding and do not establish diabetes-specific structural changes or causal tissue mechanisms.