Artificial intelligence model to identify elderly patients with locomotive syndrome: A cross-section study.
Aging
Artificial intelligence
Foot pressure
Locomotive syndrome
Journal
Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association
ISSN: 1436-2023
Titre abrégé: J Orthop Sci
Pays: Japan
ID NLM: 9604934
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
01
11
2021
revised:
12
01
2022
accepted:
18
01
2022
medline:
8
5
2023
pubmed:
13
2
2022
entrez:
12
2
2022
Statut:
ppublish
Résumé
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 < 0.01; the integrated discrimination improvement was 0.059 [95% confidence interval, 0.039-0.088] P < 0.01). 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 35148912
pii: S0949-2658(22)00023-9
doi: 10.1016/j.jos.2022.01.010
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
656-661Informations de copyright
Copyright © 2022 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this manuscript.