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

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 i...

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Bibliographic Details
Published in:Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association 2023-05, Vol.28 (3), p.656-661
Main Authors: Takahashi, Shinji, Nonomiya, Yuta, Terai, Hidetomi, Hoshino, Masatoshi, Ohyama, Shoichiro, Shintani, Ayumi, Nakamura, Hiroaki
Format: Article
Language:English
Subjects:
Aged
Aging
Artificial Intelligence
Cross-Sectional Studies
Foot pressure
Gait
Humans
Locomotion
Locomotive syndrome
Mobility Limitation
Syndrome
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Summary: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. 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. 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 
ISSN:0949-2658
1436-2023
DOI:10.1016/j.jos.2022.01.010