Game-Based Assessment of Peripheral Neuropathy Combining Sensor-Equipped Insoles, Video Games, and AI: Proof-of-Concept Study

• Published in: Journal of medical Internet research Vol. 26; no. 12; p. e52323
• Main Authors: Ming, Antao, Clemens, Vera, Lorek, Elisabeth, Wall, Janina, Alhajjar, Ahmad, Galazky, Imke, Baum, Anne-Katrin, Li, Yang, Li, Meng, Stober, Sebastian, Mertens, Nils David, Mertens, Peter Rene
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 01.10.2024; JMIR Publications
• Subjects: Adult; Aged; Analysis; Artificial Intelligence; Cohort Studies; Diabetes; Diabetics; Diagnosis; Female; Humans; Machine learning; Male; Methods; Middle Aged; Neurophysiology; Original Paper; Peripheral Nervous System Diseases - diagnosis; Polyneuropathies; Prevention; Proof of Concept Study; Sensors; Technology application; Video Games; Visualization (Computers); Wearable Electronic Devices; Taiwan; Germany; metabolic syndrome; sensor-equipped insoles; feature extraction; diabetes mellitus; machine learning; peripheral neuropathy; video games
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/52323

Detecting peripheral neuropathy (PNP) is crucial in preventing complications such as foot ulceration. Clinical examinations for PNP are infrequently provided to patients at high risk due to restrictions on facilities, care providers, or time. A gamified health assessment approach combining wearable sensors holds the potential to address these challenges and provide individuals with instantaneous feedback on their health status. We aimed to develop and evaluate an application that assesses PNP through video games controlled by pressure sensor-equipped insoles. In the proof-of-concept exploratory cohort study, a complete game-based framework that allowed the study participant to play 4 video games solely by modulating plantar pressure values was established in an outpatient clinic setting. Foot plantar pressures were measured by the sensor-equipped insole and transferred via Bluetooth to an Android tablet for game control in real time. Game results and sensor data were delivered to the study server for visualization and analysis. Each session lasted about 15 minutes. In total, 299 patients with diabetes mellitus and 30 with metabolic syndrome were tested using the game application. Patients' game performance was initially assessed by hypothesis-driven key capabilities that consisted of reaction time, sensation, skillfulness, balance, endurance, and muscle strength. Subsequently, specific game features were extracted from gaming data sets and compared with nerve conduction study findings, neuropathy symptoms, or disability scores. Multiple machine learning algorithms were applied to 70% (n=122) of acquired data to train predictive models for PNP, while the remaining data were held out for final model evaluation. Overall, clinically evident PNP was present in 247 of 329 (75.1%) participants, with 88 (26.7%) individuals showing asymmetric nerve deficits. In a subcohort (n=37) undergoing nerve conduction study as the gold standard, sensory and motor nerve conduction velocities and nerve amplitudes in lower extremities significantly correlated with 79 game features (|R|>0.4, highest R value +0.65; P<.001; adjusted R =0.36). Within another subcohort (n=173) with normal cognition and matched covariates (age, sex, BMI, etc), hypothesis-driven key capabilities and specific game features were significantly correlated with the presence of PNP. Predictive models using selected game features achieved 76.1% (left) and 81.7% (right foot) accuracy for PNP detection. Multiclass models yielded an area under the receiver operating characteristic curve of 0.76 (left foot) and 0.72 (right foot) for assessing nerve damage patterns (small, large, or mixed nerve fiber damage). The game-based application presents a promising avenue for PNP screening and classification. Evaluation in expanded cohorts may iteratively optimize artificial intelligence model efficacy. The integration of engaging motivational elements and automated data interpretation will support acceptance as a telemedical application.