Artificial Intelligence in diabetic foot and ulcer assessment in diabetes
Artificial Intelligence (AI) has the potential to significantly enhance the assessment of diabetic feet, especially in detecting and managing diabetic foot ulcers (DFUs). This series of studies aims to evaluate the effectiveness of Machine Learning (ML) techniques and Deep Learning (DL) algorithms in stratifying diabetic foot risk and assessing the healing of DFUs.
Key aspects under investigation include: The generalisability of the developed models and the role of plantar pressure and temperature in improving model accuracy.
Ongoing research is focused on enhancing the automated detection of DFU complications. The primary goal of these studies is to improve the diagnostic accuracy of diabetic foot complications and to increase the efficiency of predicting wound healing, which could inform treatment decisions.
Team
Publications
1. Lucho, S., Naemi, R., Castañeda, B., & Treuillet, S. (2024). Can deep learning wound segmentation algorithms developed for a dataset be effective for another dataset? A specific focus on diabetic foot ulcers. IEEE Access. Can Deep Learning Wound Segmentation Algorithms Developed for a Dataset Be Effective for Another Dataset? A Specific Focus on Diabetic Foot Ulcers
2. Gerlein, E.A.; Calderón, F.; Zequera-Díaz, M.; Naemi, R. (2024) Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques. Algorithms, 17, 519. Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques
3. Calderon, F.C., Zequera-Díaz, M., Gerlein, E.A., Naemi, R. (2025). The Development of A Comprehensive Library for Thermal Image Analysis of Diabetic Feet: ThermalDiabetesTools. In: Ballarin, V.L., Martinez-Licona, F., Pérez-Buitrago, S.M., Ibarra-Ramírez, E.A., Berriere, L.R. (eds) 1st IFMBE Latin American Conference on Digital Health. CLASD 2024. IFMBE Proceedings, vol 119. Springer, Cham. The Development of A Comprehensive Library for Thermal Image Analysis of Diabetic Feet: ThermalDiabetesTools