DSPnet: a self-onn model for robust DSPN diagnosis from temperature maps.

• Main Authors: Khandakar, Amith, Chowdhury, Muhammad E. H., Ibne Reaz, Mamun, Kiranyaz, Serkan, Hasan, Anwarul, Rahman, Tawsifur, Md. Ali, Sawal Hamid, Shapiai At. Abd. Razak,, Mohd. Ibrahim, A. Bakar, Ahmad Ashrif, Podder, Kanchon Kanti, Chowdhury, Moajjem Hossain, Faisal, Md. Ahasan Atick
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: T58.6-58.62 Management information systems
• Online Access: http://eprints.utm.my/104946/; http://dx.doi.org/10.1109/JSEN.2023.3235252

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.