Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements

Abstract

Purpose: Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI tool compared to three manual readers.

Methods: A sample of 320 legs was used. Three readers' measurements were compared to AI output for 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). Intraclass correlation coefficients (ICCs) and Bland-Altman analysis were used to track performance.

Results: AI output was successfully produced for 272/320 legs in the study. The reader versus AI pairwise ICCs were mostly in the excellent range: 12/13, 12/13, and 9/13 variables were in the excellent range (ICC > 0.75) for readers 1, 2, and 3, respectively. There was better agreement for leg length, femur length, tibia length, LLD, and HKA than for other variables. The median reading times for the three readers and AI were 250, 282, 236, and 38 s, respectively.

Conclusion: This study showed that AI-based software provides reliable assessment of LLD and lower extremity alignment with substantial time savings.

Keywords: Artificial intelligence; Deep learning; Knee deformities; Leg length discrepancy; Radiographs.