ArticleWorld journal of experimental medicine2024
HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia.
Article in World journal of experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Three-Dimensional Estimation of the Femoral Head Center From Trochanteric Landmarks in Japanese Female Patients With Developmental Dysplasia of the Hip.Orthopaedic surgery · 2026Article
- Patients with borderline hip dysplasia present with inferior patient-reported outcomes compared to true hip dysplasia.Journal of experimental orthopaedics · 2025Article
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6 authors.
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Abstract
backgroundHip dysplasia (HD) is characterized by insufficient acetabular coverage of the femoral head, leading to a predisposition for osteoarthritis. While radiographic measurements such as the lateral center edge angle (LCEA) and Tönnis angle are essential in evaluating HD severity, patient-reported outcome measures (PROMs) offer insights into the subjective health impact on patients.
aimTo investigate the correlations between machine-learning automated and manual radiographic measurements of HD and PROMs with the hypothesis that artificial intelligence (AI)-generated HD measurements indicating less severe dysplasia correlate with better PROMs.
methodsRetrospective study evaluating 256 hips from 130 HD patients from a hip preservation clinic database. Manual and AI-derived radiographic measurements were collected and PROMs such as the Harris hip score (HHS), international hip outcome tool (iHOT-12), short form (SF) 12 (SF-12), and Visual Analogue Scale of the European Quality of Life Group survey were correlated using Spearman's rank-order correlation.
resultsThe median patient age was 28.6 years (range 15.7-62.3 years) with 82.3% of patients being women and 17.7% being men. The median interpretation time for manual readers and AI ranged between 4-12 minutes per patient and 31 seconds, respectively. Manual measurements exhibited weak correlations with HHS, including LCEA (
conclusionThis study suggests AI can aid in HD assessment, but weak PROM correlations highlight their continued importance in predicting subjective health and outcomes, complementing AI-derived measurements in HD management.
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