Purpose The purpose of this study was to evaluate the reliability of a newly developed AI-algorithm for the evaluation of long leg radiographs (LLR) after total knee arthroplasties (TKA). Methods In the validation cohort 200 calibrated LLRs of eight diferent common unconstrained and constrained knee systems were analysed. Accuracy and reproducibility of the AI-algorithm were compared to manual reads regarding the hipknee-ankle (HKA) as well as femoral (FCA) and tibial component (TCA) angles. In the evaluation cohort all institutional LLRs with TKAs in 2018 (n = 1312) were evaluated to assess the algorithms' ability of handling large data sets. Intraclass correlation (ICC) coeicient and mean absolute deviation (sMAD) were calculated to assess conformity between the AI software and manual reads. Results Validation cohort: The AI-software was reproducible on 96% and reliable on 92.1% of LLRs with an output and showed excellent reliability in all measured angles (ICC > 0.97) compared to manual measurements. Excellent results were found for primary unconstrained TKAs. In constrained TKAs landmark setting on the femoral and tibial component failed in 12.5% of LLRs (n = 9). Evaluation cohort: Mean measurements for all postoperative TKAs (n = 1240) were 0.2° varus ± 2.5° (HKA), 89.3° ± 1.9° (FCA), and 89.1° ± 1.6° (TCA). Mean measurements on preoperative revision TKAs (n = 74) were 1.6 varus ± 6.4° (HKA), 90.5° ± 3.1° (FCA), and 88.9° ± 4.1° (TCA). Conclusions AI-powered applications are reliable for automated analysis of lower limb alignment on LLRs with TKAs. They are capable of handling large data sets and could, therefore, lead to more standardized and eicient postoperative quality controls. Level of evidence Diagnostic Level III.
Purpose Unexpected-positive-intraoperative-cultures (UPIC) are common in presumed aseptic revision-total-knee-arthroplasties (rTKA). However, the clinical signiicance is not entirely clear. In contrast, in some presumably septic rTKA, identiication of an underlying pathogen was not possible, so-called unexpected-negative-intraoperative-cultures (UNIC). The purpose of this study was to evaluate the potential use of synovial alpha-defensin (AD) levels in these patients. Methods Synovial AD levels from 143 rTKAs were evaluated retrospectively from our prospectively maintained institutional periprostetic joint infection (PJI) biobank and database. The 2018-International Consensus Meeting (ICM) criteria was used to deine the study groups. Samples from UPICs with a minimum of one positive intraoperative culture (ICM 2-≥ 6) (n = 20) and UNIC's (ICM ≥ 6) (n = 14) were compared to 34 septic culture-positive samples (ICM ≥ 6) and 75 aseptic culture-negative (ICM 0-1). Moreover, AD-lateral-low-assay (ADLF) and an enzyme-linked-immunosorbent-assay (ELISA) in detecting the presence of AD in native and centrifuged synovial luid specimens was performed. Concentration of AD determined by ELISA and ADLF methods, as well as microbiological, and histopathological results, serum and synovial parameters along with demographic factors were analysed. Results AD was positive in 31/34 (91.2%) samples from the septic culture-positive group and in 14/14 (100%) samples in the UNIC group. All UPIC samples showed a negative AD result. Positive AD samples were highly associated with culture positive and histopathological results (p < 0.001). No high-virulent microorganisms (0/20) were present in the UPIC group, compared to infected-group (19/34; 55.9%). High virulent microorganisms showed a positive AD result in 89.5% (17/19) of the cases. Methicillin resistant Staphylococcus epidermis (MRSE) infections had signiicantly higher AD levels than with methicillin susceptible S. epidermdis (MSSE) (p = 0.003). ELISA and ADLF tests were positive with centrifuged (8/8) and native (8/8) synovial luid. Conclusion AD showed a solid diagnostic performance in infected and non-infected revisions, and it provided an additional value in the diagnosis of UPIC and UNIC associated to rTKAs. Pathogen virulence as well as antibiotic resistance pattern may have an efect on AD levels. Centrifugation of synovial luid had no inluence on ADLF results.
Purpose Despite advances of three-dimensional imaging pelvic radiographs remain the cornerstone in the evaluation of the hip joint. However, large inter- and intra-rater variabilities were reported due to subjective landmark setting. Artificial intelligence (AI)–powered software applications could improve the reproducibility of pelvic radiograph evaluation by providing standardized measurements. The aim of this study was to evaluate the reliability and agreement of a newly developed AI algorithm for the evaluation of pelvic radiographs. Methods Three-hundred pelvic radiographs from 280 patients with different degrees of acetabular coverage and osteoarthritis (Tönnis Grade 0 to 3) were evaluated. Reliability and agreement between manual measurements and the outputs of the AI software were assessed for the lateral-center-edge (LCE) angle, neck-shaft angle, sharp angle, acetabular index, as well as the femoral head extrusion index. Results The AI software provided reliable results in 94.3% (283/300). The ICC values ranged between 0.73 for the Acetabular Index to 0.80 for the LCE Angle. Agreement between readers and AI outputs, given by the standard error of measurement (SEM), was good for hips with normal coverage (LCE-SEM: 3.4°) and no osteoarthritis (LCE-SEM: 3.3°) and worse for hips with undercoverage (LCE-SEM: 5.2°) or severe osteoarthritis (LCE-SEM: 5.1°). Conclusion AI-powered applications are a reliable alternative to manual evaluation of pelvic radiographs. While being accurate for patients with normal acetabular coverage and mild signs of osteoarthritis, it needs improvement in the evaluation of patients with hip dysplasia and severe osteoarthritis.
Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020. Automated landmark placement was conducted using the AI-algorithm LAMA™ and the measurements were used to determine femoral, tibial and total leg-length. Linear regression equations were subsequently derived for stature estimation. The estimated regression equations have a shallower slope and larger intercept in males and females (Femur-male: slope = 2.08, intercept = 77.49; Femur-female: slope = 1.9, intercept = 79.81) compared to the formulae previously derived by Trotter and Gleser 1952 (Femur-male: slope = 2.38, intercept = 61.41; Femur-female: slope = 2.47, intercept = 54.13) and Trotter and Gleser 1958 (Femur-male: slope = 2.32, intercept = 65.53). All long-bone measurements showed a high correlation (r ≥ 0.76) with stature. The linear equations we derived tended to overestimate stature in short persons and underestimate stature in tall persons. The differences in slopes and intercepts from those published by Trotter and Gleser (1952, 1958) may result from an ongoing secular increase in stature. Our study illustrates that AI-algorithms are a promising new tool enabling large-scale measurements.
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