A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy From Standing Balance by Leveraging Multi-Domain Features

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 2388 - 2397
• Main Authors: Mengarelli, Alessandro, Tigrini, Andrea, Verdini, Federica, Scattolini, Mara, Mobarak, Rami, Burattini, Laura, Rabini, Rosa Anna, Fioretti, Sandro
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Algorithms; Artificial neural networks; Asymptomatic; Center of pressure; computer aided diagnosis; Diabetes; Diabetes mellitus; Diabetic Neuropathies - diagnosis; Diabetic Neuropathies - physiopathology; Diabetic neuropathy; Diagnosis; Diagnosis, Computer-Assisted - methods; Diseases; Effectiveness; Female; Humans; Information processing; Information retrieval; Limiting; machine learning; Male; Middle Aged; peripheral neuropathy; Postural Balance - physiology; Regulation; Reproducibility of Results; Standing Position; static posture; Task analysis; Trajectory
• DOI: 10.1109/TNSRE.2024.3419235
• ISSN: 1534-4320; 1558-0210

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease.