Distinguish different sensorimotor performance of the hand between the individuals with diabetes mellitus and chronic kidney disease through deep learning models

Abstract

Diabetes mellitus and chronic kidney disease represent escalating global epidemics with comorbidities akin to neuropathies, resulting in various neuromuscular symptoms that impede daily performance. Interestingly, previous studies indicated differing sensorimotor functions within these conditions. If assessing sensorimotor features can effectively distinguish between diabetes mellitus and chronic kidney disease, it could serve as a valuable and non-invasive indicator for early detection, swift screening, and ongoing monitoring, aiding in the differentiation between these diseases. This study classified diverse diagnoses based on motor performance using a novel pinch-holding-up-activity test and machine learning models based on deep learning. Dataset from 271 participants, encompassing 3263 hand samples across three cohorts (healthy adults, diabetes mellitus, and chronic kidney disease), formed the basis of analysis. Leveraging convolutional neural networks, three deep learning models were employed to classify healthy adults, diabetes mellitus, and chronic kidney disease based on pinch-holding-up-activity data. Notably, the testing set displayed accuracies of 95.3% and 89.8% for the intra- and inter-participant comparisons, respectively. The weighted F1 scores for these conditions reached 0.897 and 0.953, respectively. The study findings underscore the adeptness of the dilation convolutional neural networks model in distinguishing sensorimotor performance among individuals with diabetes mellitus, chronic kidney disease, and healthy adults. These outcomes suggest discernible differences in sensorimotor performance across the diabetes mellitus, chronic kidney disease, and healthy cohorts, pointing towards the potential of rapid screening based on these parameters as an innovative clinical approach.