Machine learning identifies factors related to early joint space narrowing in dysplastic and non-dysplastic hips

Klontzas M. E., Volitakis E., Aydingoz U., Chlapoutakis K., Karantanas A. H.

EUROPEAN RADIOLOGY, vol.32, no.1, pp.542-550, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1007/s00330-021-08070-x
  • Journal Name: EUROPEAN RADIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Page Numbers: pp.542-550
  • Keywords: Osteoarthritis, hip, Dysplasia, congenital hip, Machine learning, Artificial intelligence, Femoroacetabular impingement, FEMOROACETABULAR IMPINGEMENT, OSTEOARTHRITIS, ASSOCIATION, MORPHOLOGY, COHORT, RISK
  • Hacettepe University Affiliated: Yes

Abstract

Objectives To utilise machine learning, unsupervised clustering and multivariate modelling in order to predict severe early joint space narrowing (JSN) from anatomical hip parameters while identifying factors related to joint space width (JSW) in dysplastic and non-dysplastic hips. Methods A total of 507 hip CT examinations of patients 20-55 years old were retrospectively examined, and JSW, center-edge (CE) angle, alpha angle, anterior acetabular sector angle (AASA), and neck-shaft angle (NSA) were recorded. Dysplasia and severe JSN were defined with CE angle < 25(o) and JSW< 2 mm, respectively. A random forest classifier was developed to predict severe JSN based on anatomical and demographical data. Multivariate linear regression and two-step unsupervised clustering were performed to identify factors linked to JSW. Results In dysplastic hips, lateral or anterior undercoverage alone was not correlated to JSN. AASA (p < 0.005) and CE angle (p < 0.032) were the only factors significantly correlated with JSN in dysplastic hips. In non-dysplastic hips, JSW was inversely correlated to CE angle, AASA, and age and positively correlated to NSA (p < 0.001). A random forest classifier predicted severe JSN (AUC 69.9%, 95%CI 47.9-91.8%). TwoStep cluster modelling identified two distinct patient clusters one with low and one with normal JSW and different anatomical characteristics. Conclusion Machine learning predicted severe JSN and identified population characteristics related to normal and abnormal joint space width. Dysplasia in one plane was found to be insufficient to cause JSN, highlighting the need for hip anatomy assessment on multiple planes.