HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia

Abstract

BACKGROUND

Hip 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.

AIM

To 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.

METHODS

Retrospective 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.

RESULTS

The 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 (r = 0.18) and Tönnis angle (r = -0.24). AI-derived metrics showed similar weak correlations, with the most significant being Caput-Collum-Diaphyseal (CCD) with iHOT-12 at r = -0.25 (P = 0.042) and CCD with SF-12 at r = 0.25 (P = 0.048). Other measured correlations were not significant (P > 0.05).

CONCLUSION

This 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.

Keywords: Hip dysplasia; Patient reported outcome measures; Deep-learning; Artificial intelligence; Radiographs; Lateral center edge angle

Core Tip: In this study, we compared an artificial intelligence (AI) tool measuring anteroposterior hip radiographs against manual readers for assessing hip dysplasia (HD) associations with patient-reported outcome measures (PROMs). The AI tool, HIPPO, efficiently generated radiographic measurements but showed poor correlations with PROMs, highlighting its current limitations in predicting clinical outcomes solely from radiological data. This indicates that while AI can aid radiographic assessments, PROMs remain crucial for capturing subjective patient experiences. The findings underscore the importance of integrating PROMs as an additional element in the clinical decision-making processes for HD, while also incorporating efficient radiographic assessment by AI tools.