Plantar Soft Tissue Stiffness Automatic Estimation in Ultrasound Imaging using Deep learning

Open Access
Conference Proceedings
Authors: Yori PusparaniBen-Yi LiauYih-Kuen JanHsu-Tang ChengPeter ArdhiantoFityanul AkhyarChi-Wen LungChih-Yang Lin

Abstract: Preventing diabetic foot ulcers (DFU) is critical for diabetes mellitus (DM) patients. Increased stiffness of plantar foot may cause higher plantar pressure leading to a higher risk of DFU. Soft tissue stiffness can be determined by measuring the soft tissue thickness with indentation depth and stress. Therefore, we hypothesized that the deep learning model could detect the ultrasound image pixel change under soft tissue compression. This study aimed to apply the deep learning model to analyze the ultrasound image pixel thickness of plantar foot, then predict the soft tissue indentation depth and loading force for estimating the stiffness. This study has developed a motor-driven ultrasound indentation system to apply programmable compression and simultaneously assess soft tissue mechanical properties and responses in indentation depth and loading force. In addition, the effective Young's modulus was calculated to characterize mechanical properties of soft tissues in the first metatarsal head. The deep learning method employed the YOLOv5x model to train and detect the small object in the indentation depth, such as ultrasound image pixel changes. Finally, the dataset images were processed with labeling annotation from the soft tissue indentation depth and loading force. The deep learning results showed 0.995 in mean Average Precision (mAP), 0.999 in precision, 1.000 in Recall, and 0.013 in Loss. A significant correlation was found between the ultrasound image pixel changes and soft tissue indentation depth (r = 0.98, p < 0.05). Furthermore, a significant correlation was observed between the ultrasound image pixel changes and the loading force in the first metatarsal head (r = 0.85, p < 0.05). The validation and prediction models were lower than the training models in the effective Young's modulus results. However, the results of the initial modulus were similar between the three models. Our findings recommend that applying deep learning in the ultrasound image can predict soft tissue indentation depth and loading force to calculate the stiffness of the plantar foot.

Keywords: Diabetes, Effective Young's Modulus, Inflated Air Pressure Insole, YOLO-v5

DOI: 10.54941/ahfe1002612

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