TY - JOUR

T1 - A Deep Learning Algorithm for Radiographic Measurements of the Hip in Adults—A Reliability and Agreement Study

AU - Jensen, Janni

AU - Graumann, Ole

AU - Overgaard, Søren

AU - Gerke, Oke

AU - Lundemann, Michael

AU - Haubro, Martin Haagen

AU - Varnum, Claus

AU - Bak, Lene

AU - Rasmussen, Janne

AU - Olsen, Lone B.

AU - Rasmussen, Benjamin S.B.

N1 - Publisher Copyright: © 2022 by the authors.

PY - 2022

Y1 - 2022

N2 - Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.

AB - Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.

KW - hip dysplasia

KW - machine learning

KW - radiography

KW - radiology

KW - X-ray

U2 - 10.3390/diagnostics12112597

DO - 10.3390/diagnostics12112597

M3 - Journal article

C2 - 36359441

AN - SCOPUS:85141774031

VL - 12

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 11

M1 - 2597

ER -