DSPNet: A Self-ONN Model for Robust DSPN Diagnosis From Temperature Maps

Diabetic sensorimotor polyneuropathy (DSPN) leads to pain, diabetic foot ulceration (DFU), amputation, and death. The diagnosis of advanced DSPN to identify those at risk is key to preventing DFU and amputation. Alterations in foot pressure and temperature may help to detect DSPN and the risk of DFU. We have applied a robust machine-learning approach to identify patients with severe DSPN using standing foot temperature maps generated using temperature sensor data. A robust shallow operational neural network model DSPNet is proposed. The study utilized a labeled dataset from the University Hospital Magdeburg, Magdeburg, Germany, consisting of temperature sensor data from eight different points on the foot in seating and standing positions in patients with severe DSPN (n =25) and healthy controls (n =18). The proposed network achieved an F1 score of 90.3% for identifying patients with DSPN and outperformed current state-of-the-art deep-learning network methods. This is the first of its kind of research where the results confirm that temperature maps are not only effective in the detection of those at high risk of DFU but also in identifying patients with severe DSPN. Such sensors could easily be incorporated into smart insoles.

Automated summary

Diabetic sensorimotor polyneuropathy (DSPN) can result in pain, diabetic foot ulceration (DFU), amputation, and death. This study proposes a robust machine-learning approach called DSPNet to identify patients with severe DSPN using standing foot temperature maps. The study achieved an F1 score of 90.3% and outperformed current deep-learning network methods, indicating the effectiveness of temperature maps in detecting high-risk DFU patients and identifying severe DSPN patients. Such sensors can be easily incorporated into smart insoles.