The Role and Efficiency of an AI-Powered Software in the Evaluation of Lower Limb Radiographs before and after Total Knee Arthroplasty

Abstract

The rapid evolution of artificial intelligence (AI) in medical imaging analysis has significantly impacted musculoskeletal radiology, offering enhanced accuracy and speed in radiograph evaluations. The potential of AI in clinical settings, however, remains underexplored. This research investigates the efficiency of a commercial AI tool in analyzing radiographs of patients who have undergone total knee arthroplasty. The study retrospectively analyzed 200 radiographs from 100 patients, comparing AI software measurements to expert assessments. Assessed parameters included axial alignments (MAD, AMA), femoral and tibial angles (mLPFA, mLDFA, mMPTA, mLDTA), and other key measurements including JLCA, HKA, and Mikulicz line. The tool demonstrated good to excellent agreement with expert metrics (ICC = 0.78–1.00), analyzed radiographs twice as fast (p < 0.001), yet struggled with accuracy for the JLCA (ICC = 0.79, 95% CI = 0.72–0.84), the Mikulicz line (ICC = 0.78, 95% CI = 0.32–0.90), and if patients had a body mass index higher than 30 kg/m2 (p < 0.001). It also failed to analyze 45 (22.5%) radiographs, potentially due to image overlay or unique patient characteristics. These findings underscore the AI software’s potential in musculoskeletal radiology but also highlight the necessity for further development for effective utilization in diverse clinical scenarios. Subsequent studies should explore the integration of AI tools in routine clinical practice and their impact on patient care.