Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Exp Med. Dec 20, 2024; 14(4): 99359
Published online Dec 20, 2024. doi: 10.5493/wjem.v14.i4.99359
HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia
Ahmed Alshaikhsalama, Holden Archer, Yin Xi, Richard Ljuhar, Joel E Wells, Avneesh Chhabra
Ahmed Alshaikhsalama, Holden Archer, Department of Radiology, University of Texas Southwestern, Dallas, TX 75390, United States
Yin Xi, Avneesh Chhabra, Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75235, United States
Richard Ljuhar, Department of Radiology, Image Biopsy, Vienna 1190, Austria
Joel E Wells, Department of Orthopedic Surgery, Baylor Scott and White, Dallas, TX 75235, United States
Author contributions: Alshaikhsalama A and Archer H were involved in the conception, design, data collection, writing and editing of the manuscript; Xi Y supervised and edited the manuscript and performed the statistical analysis; Ljuhar H, Wells J, and Chhabra A involved in the conception, design, and supervision of the manuscript; all of the authors read and approved the final version of the manuscript to be published.
Institutional review board statement: The study was reviewed and approved by the University of Texas Southwestern Institutional Review Board (approval No. Stu-2022-1014).
Informed consent statement: The University of Texas Southwestern Institutional Review Board determined informed consent was not required for this study since the data is fully anonymized.
Conflict-of-interest statement: Wells JE had received fees for serving as a consultant for Ethicon; Ljuhar R was an employee of Image Biopsy Labs that developed HIPPO AI software; Chhabra A had received fees for serving as a consultant for ICON Medical and TREACE Medical Concepts Inc and for serving as a Siemens Medical advisor for Image Biopsy Inc.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at ahmed.alshaikhsalama@utsouthwestern.edu. Consent was not obtained but the presented data are anonymized and risk of identification is low.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ahmed Alshaikhsalama, BSc, Research Associate, Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd, Dallas, TX 75390, United States. ahmed.alshaikhsalama@utsouthwestern.edu
Received: July 20, 2024
Revised: September 23, 2024
Accepted: October 24, 2024
Published online: December 20, 2024
Processing time: 102 Days and 15.8 Hours
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.