AUTHOR=Wang ChenGuang , Li Chao , Zhang Rui , Li ZhiJun , Zhang HuaFeng , Zhang Yuan , Liu Shen , Chi XiaoYue , Zhao Rui 

TITLE=Identification of radiographic characteristics associated with pain in hallux valgus patients: A preliminary machine learning study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.943026

DOI=10.3389/fpubh.2022.943026

ISSN=2296-2565

ABSTRACT=Abstract
Objective To investigate the association between the structural deformity and foot pain in hallux valgus (HV) patients using a multi-variate pattern analysis (MVPA) approach, ultimately to provide new insights into the design of novel insoles and physiotherapy or surgical correction.
Methods Plain radiographic metrics were calculated from 36 painful and 36 pain-free HV feet. In analysis 1, univariate analyses were performed to investigate the clinical and radiographic differences between painful and pain-free HV. In analysis 2, we investigated the pattern differences for radiographic metrics between these two groups using a MVPA approach utilizing a support vector machine. In analysis 3, sequential backward selection and exhaustive search were performed as a feature-selection procedure to identify an optimal feature subtype. In analysis 4, hierarchical clustering analysis was used to identify the optimal radiographic HV subtype associated with pain in HV.
Results We found that: (1) relative to feet with pain-free HV, the painful ones exhibited a higher hallux valgus angle, i.e., the magnitude of distal metatarsal and phalangeal deviation; (2) painful HV could be accurately differentiated from pain-free HV via MVPA. Using sequential backward selection and exhaustive search, a 5-feature subset was identified with optimal performance for classifying HV as either painful or pain-free; and (3) by applying hierarchical clustering analysis, a radiographic subtype with an 80% pain incidence was identified.
Conclusion The pain in HV is multifactorial and associated with a radiographic pattern measured by various angles on plain radiographs. The combination of HVA, IPA, DMAA, MAA and MPD showed the optimal classification performance between painful and pain-free HV.
Keyword Hallux Valgus; Multi-Variate Pattern Analysis; Pain; Support Vector Machine; Hierarchical Clustering.