TY - JOUR
T1 - Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination
AU - Archer, Holden
AU - Reine, Seth
AU - Xia, Shuda
AU - Vazquez, Louis Camilo
AU - Ashikyan, Oganes
AU - Pezeshk, Parham
AU - Kohli, Ajay
AU - Xi, Yin
AU - Wells, Joel E.
AU - Hummer, Allan
AU - Difranco, Matthew
AU - Chhabra, Avneesh
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to International Skeletal Society (ISS) 2023.
PY - 2024/5
Y1 - 2024/5
N2 - Purpose: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length. The primary aim of this study was to evaluate the agreements in LLD and knee alignment measurements between an AI software and two board-certified radiologists in patients without metal implants. The secondary aim was to assess time savings achieved by AI. Methods: The measurements assessed in the study were hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg length discrepancy (LLD), and mechanical axis deviation (MAD). These measurements were performed by two radiologists and the AI software on 164 legs. Intraclass-correlation-coefficients (ICC) and Bland–Altman analyses were used to assess the AI’s performance. Results: The AI software set incorrect landmarks for 11/164 legs. Excluding these cases, ICCs between the software and radiologists were excellent for 12/13 variables (11/13 with outliers included), and the AI software met performance targets for 11/13 variables (9/13 with outliers included). The mean reading time for the AI algorithm and two readers, respectively, was 38.3, 435.0, and 625.0 s. Conclusion: This study demonstrated that, with few exceptions, this AI-based software reliably generated measurements for most variables in the study and provided substantial time savings.
AB - Purpose: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length. The primary aim of this study was to evaluate the agreements in LLD and knee alignment measurements between an AI software and two board-certified radiologists in patients without metal implants. The secondary aim was to assess time savings achieved by AI. Methods: The measurements assessed in the study were hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg length discrepancy (LLD), and mechanical axis deviation (MAD). These measurements were performed by two radiologists and the AI software on 164 legs. Intraclass-correlation-coefficients (ICC) and Bland–Altman analyses were used to assess the AI’s performance. Results: The AI software set incorrect landmarks for 11/164 legs. Excluding these cases, ICCs between the software and radiologists were excellent for 12/13 variables (11/13 with outliers included), and the AI software met performance targets for 11/13 variables (9/13 with outliers included). The mean reading time for the AI algorithm and two readers, respectively, was 38.3, 435.0, and 625.0 s. Conclusion: This study demonstrated that, with few exceptions, this AI-based software reliably generated measurements for most variables in the study and provided substantial time savings.
KW - Artificial intelligence
KW - Deep learning
KW - Knee deformities
KW - Leg length discrepancy
KW - Radiographs
UR - http://www.scopus.com/inward/record.url?scp=85176462023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176462023&partnerID=8YFLogxK
U2 - 10.1007/s00256-023-04502-5
DO - 10.1007/s00256-023-04502-5
M3 - Article
C2 - 37964028
AN - SCOPUS:85176462023
SN - 0364-2348
VL - 53
SP - 923
EP - 933
JO - Skeletal radiology
JF - Skeletal radiology
IS - 5
ER -