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

作　　者：Ahmed Alshaikhsalama Holden Archer Yin Xi Richard Ljuhar Joel E Wells Avneesh Chhabra 

机构地区：[1]Department of Radiology,University of Texas Southwestern,Dallas,TX 75390,United States [2]Department of Radiology,University of Texas Southwestern Medical Center,Dallas,TX 75235,United States [3]Department of Radiology,Image Biopsy,Vienna 1190,Austria [4]Department of Orthopedic Surgery,Baylor Scott and White,Dallas,TX 75235,United States

出　　处：《World Journal of Experimental Medicine》2024年第4期136-146,共11页世界实验医学杂志（英文）

基　　金：the University of Texas Southwestern Institutional Review Board(approval No.Stu-2022-1014).

摘　　要：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.

关 键 词：Hip dysplasia Patient reported outcome measures Deep-learning Artificial intelligence RADIOGRAPHS Lateral center edge angle 

分 类 号：R68[医药卫生—骨科学]