Age and sex differences in coronal lower extremity alignment in a healthy Asian population.

BACKGROUND: The purpose of this study was to analyze the coronal alignment of lower extremities according to age and sex in a healthy population and demonstrate the differences. METHODS: Standing full-lower limb anteroposterior (AP) radiographs of healthy volunteers (670 males and 782 females) aged 18-69 years were retrospectively analyzed. The hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), joint line convergence angle and femoral bowing angle (FBA) were measured. The radiographic parameters were compared according to groups of age and sex. The proportion of volunteers with varus or valgus alignment more than 3° were also analyzed. RESULTS: With increasing age, HKA and LDFA varus increased. With increasing age, femoral medial bowing decreased. In addition, the HKA showed more varus alignment in males than in females (178.01° vs. 178.82°, P < 0.001). The MPTA was about 1° smaller in males than in females (P < 0.001). The proportion of patients with varus alignment of more than 3° increased with increasing age, with 16.9% in the 10-19 years old and 38.0% in the 60-69 years old groups. CONCLUSION: This study demonstrated that males showed more varus tibial alignments than females. Varus limb alignment, LDFA, and FBA also increases with age. In contrast, tibial alignment was constant across all age groups. Therefore, differences in lower extremity alignment according to age and sex should be considered in estimating individual prearthritic alignments.

Deep learning-based landmark recognition and angle measurement of full-leg plain radiographs can be adopted to assess lower extremity alignment.

PURPOSE: Evaluating lower extremity alignment using full-leg plain radiographs is an essential step in diagnosis and treatment of patients with knee osteoarthritis. The study objective was to present a deep learning-based anatomical landmark recognition and angle measurement model, using full-leg radiographs, and validate its performance. METHODS: A total of 11,212 full-leg plain radiographs were used to create the model. To train the data, 15 anatomical landmarks were marked by two orthopaedic surgeons. Mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip-knee-ankle angle (HKAA) were then measured. For inter-observer reliability, the inter-observer intraclass correlation coefficient (ICC) was evaluated by comparing measurements from the model, surgeons, and students, to ground truth measurements annotated by an orthopaedic specialist with 14 years of experience. To evaluate test-retest reliability, all measurements were made twice by each measurer. Intra-observer ICCs were then derived. Performance evaluation metrics used in previous studies were also derived for direct comparison of the model's performance. RESULTS: Inter-observer ICCs for all angles of the model were 0.98 or higher (p < 0.001). Intra-observer ICCs for all angles were 1.00, which was higher than that of the orthopaedic specialist (0.97-1.00). Measurements made by the model showed no significant systemic variation. Except for JLCA, angles were precisely measured with absolute error averages under 0.52 degrees and proportion of outliers under 4.26%. CONCLUSIONS: The deep learning model is capable of evaluating lower extremity alignment with performance as accurate as an orthopaedic specialist with 14 years of experience. LEVEL OF EVIDENCE: III, retrospective cohort study.

Deep Learning-Based Muscle Segmentation and Quantification of Full-Leg Plain Radiograph for Sarcopenia Screening in Patients Undergoing Total Knee Arthroplasty.

Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. Here, we present a deep-learning-based model for automatic muscle segmentation and quantification of full-leg plain radiographs. We illustrated the potential of the model to predict sarcopenia in patients undergoing total knee arthroplasty (TKA). A U-Net-based deep learning model for automatic muscle segmentation was developed, trained and validated on the plain radiographs of 227 healthy volunteers. The radiographs of 403 patients scheduled for primary TKA were reviewed to test the developed model and explore its potential to predict sarcopenia. The proposed deep learning model achieved mean IoU values of 0.959 (95% CI 0.959-0.960) and 0.926 (95% CI 0.920-0.931) in the training set and test set, respectively. The fivefold AUC value of the sarcopenia classification model was 0.988 (95% CI 0.986-0.989). Of seven key predictors included in the model, the predicted muscle volume (PMV) was the most important of these features in the decision process. In the preoperative clinical setting, wherein laboratory tests and radiographic imaging are available, the proposed deep-learning-based model can be used to screen for sarcopenia in patients with knee osteoarthritis undergoing TKA with high sarcopenia screening performance.

Low muscle mass is an independent risk factor for postoperative blood transfusion in total knee arthroplasty: a retrospective, propensity score-matched cohort study.

BACKGROUND: Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. This study examined the characteristics of sarcopenic patients undergoing primary total knee arthroplasty (TKA), and identified low muscle mass as an independent risk factor for postoperative TKA complications. METHODS: A retrospective cohort study examined 452 patients who underwent TKA. The skeletal muscle index (SMI) was obtained via bioelectrical impedance analysis (BIA), along with demographics, the Charlson Comorbidity Index, and medication, laboratory and operative data for 2018-2021. Patients were categorized into normal (n = 417) and sarcopenic (n = 35) groups using the SMI cut-off suggested by the Asian Working Group for Sarcopenia 2019 (males, < 7.0 kg/m2; females, < 5.7 kg/m2). Three postoperative complications were analysed: blood transfusion, delirium, and acute kidney injury (AKI). Baseline characteristics were propensity score-matched to address potential bias and confounding factors. RESULTS: The proportion of sarcopenic patients in primary TKA was 7.7% (35/452). The sarcopenic group had a lower preoperative haemoglobin (12.18 ± 1.20 vs. 13.04 ± 1.73 g/dL, p = 0.004) and total protein (6.73 ± 0.42 vs. 7.06 ± 0.44 mg/dL, p = 0.001). Propensity scoring matching and logistic regression showed that more patients in the sarcopenic group received postoperative blood transfusions (OR = 6.60, 95% CI: 1.57-45.5, p = 0.021); there was no significant difference in AKI or delirium. Univariate receiver operating characteristic curve analysis of the propensity-matched group, to determine the predictive value of SMI for postoperative transfusion, gave an AUC of 0.797 (0.633-0.96) and SMI cut-off of 5.6 kg/m2. CONCLUSIONS: Low muscle mass determined by BIA was an independent risk factor for postoperative transfusion in TKA. Multifrequency BIA can serve as a screening tool for sarcopenia that may influence the orthopaedic decision-making process or treatment planning in patients with sarcopenia undergoing primary TKA. LEVEL OF EVIDENCE: III, retrospective cohort study.

Radiographic Follow-Up of Fibrous Dysplasia in 138 Patients.

OBJECTIVE. The purpose of this study was to explore the temporal changes in fibrous dysplasia (FD) at radiographic follow-up. MATERIALS AND METHODS. A total of 138 patients with FD who had undergone extremity radiography at least twice with a minimum 12-month interval between examinations were enrolled in this study. FD was monostotic in 99 patients and polyostotic in 39 patients. Patients were also classified according to skeletal maturity as follows: Patients 16 years old or younger were classified in the skeletally immature group (n = 34), and patients 17 years old or older were classified in the skeletally mature group (n = 104). We compared the initial and follow-up radiographs for the following findings: lesion size, opacity, sclerotic rim, calcification, and trabeculation. RESULTS. Of the 138 patients, radiographic follow-up showed no change in lesion size in 101 patients (73.2%), progression in 31 (22.5%), and regression in six (4.3%). FD in immature bones progressed more often than FD in mature bones (23/34 [67.6%] vs 8/104 [7.7%], respectively; p = 0.000), and polyostotic FD had a greater chance of regressing than monostotic FD (4/39 [10.3%] vs 2/99 [2.0%]; p = 0.032). A temporal change in FD lesion opacity was noticed in a minority of patients (19/138, 13.8%). Variable changes were observed in the sclerotic rim, calcification, and trabeculation. CONCLUSION. The radiographic follow-up of FD showed that approximately a quarter of lesions changed in size over time. Regardless of the change in lesion size, opacity and several morphologic features of FD changed during the follow-up period, which might reflect the histopathologic evolution of FD.