Artificial intelligence model to identify elderly patients with locomotive syndrome: A cross-section study.

BACKGROUND

Identifying elderly individuals with locomotive syndrome is important to prevent disability in this population. Although screening tools for locomotive syndrome are available, these require time commitment and are limited by an individual's ability to complete questionnaires independently. To improve on this limitation, we developed a screening tool that uses information on the distribution of pressure on the plantar surface of the foot with an artificial intelligence (AI)-based decision system to identify patients with locomotor syndrome. Herein, we describe our AI-based system and evaluate its performance.

METHODS

This was a cross-sectional study of 409 participants (mean age, 73.5 years). A foot scan pressure system was used to record the planter pressure distribution during gait. In the image processing step, we developed a convolutional neural network (CNN) to return the logit of the probability of locomotive syndrome based on foot pressure images. In the logistic regression step of the AI model, we estimated the predictor coefficients, including age, sex, height, weight, and the output of the CNN, based on foot pressure images.

RESULTS

The AI model improved the identification of locomotive syndrome among elderly individuals compared to clinical data, with an area under curve of 0.84 (95% confidence interval, 0.79-0.88) for the AI model compared to 0.80 (95% confidence interval, 0.75-0.85) for the clinical model. Including the footprint force distribution image significantly improved the prediction algorithm (the net reclassification improvement was 0.675 [95% confidence interval, 0.45-0.90] P < 0.01; the integrated discrimination improvement was 0.059 [95% confidence interval, 0.039-0.088] P < 0.01).

CONCLUSIONS

The AI system, which includes force distribution over the plantar surface of the foot during gait, is an effective tool to screen for locomotive syndrome.