Background: Haglund’s deformity, which is characterized by a bony prominence of the posterosuperior aspect of the calcaneus, causes posterior heel pain. To date, there is no standard radiographic parameter to diagnose symptomatic Haglund’s deformity. Herein, we proposed novel radiographic measurements to distinguish between patients with and without symptomatic Haglund’s deformity. Methods: We retrospectively evaluated ankle radiographs of 43 patients who underwent surgery for symptomatic Haglund’s deformity (Haglund group) and 41 healthy individuals (control group) free of heel complaints. Fowler–Phillip angle (FPA), Heneghan–Pavlov parallel pitch lines (PPL), Haglund’s deformity height, bump height, and bump-calcaneus ratio were measured and compared between the groups. Furthermore, the reliability and cut-off value of each parameter were validated via ICC and ROC curve analysis, respectively. Results: The bump height (p < 0.001) and the bump-calcaneus ratio (p < 0.001) showed significant differences between the control and Haglund groups, unlike FPA, PPL, and Haglund’s deformity height. ROC curve analysis revealed that the AUC of bump-calcaneus ratio was larger than that of bump height. The optimal threshold was 4 mm or higher for bump height and 7.5% or higher for bump-calcaneus ratio. The intra- and inter- observer ICCs were, respectively, 0.965 and 0.898 for bump height and 0.930 and 0.889 for bump-calcaneus ratio. Conclusions: This study proposes two novel radiographic parameters to identify operatively treated Haglund’s deformity, namely bump height and bump-calcaneus ratio. They are easy to measure and intuitive. Both of them are effective diagnostic parameters for Haglund’s deformity. Furthermore, bump-calcaneus ratio is more reliable diagnostic parameter than bump height.

Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson’s correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.

This study investigated the differences in ankle alignment changes after TKA in patients with varying preexisting ankle deformities. We retrospectively examined 90 knees with osteoarthritis and varus deformity in 78 patients who underwent TKA. Preoperative and postoperative radiographic parameters were analyzed. According to their preexisting ankle deformity, patients were assigned to the valgus or varus group. Overall, 14 (15.6%) cases were of preoperative valgus ankle deformity; the remainder were of preoperative varus ankle deformity. Hip–knee–ankle angle (HKA), tibial plafond–ground angle (PGA), and talus–ground angle (TGA) all exhibited significant correction in both groups; however, tibial plafond–talus angle (PTA) and superior space of ankle joint (SS) only changed in the varus group. The median PTA and SS significantly decreased from 1.2° to 0.3° (p < 0.001) and increased from 2.5 to 2.6 mm (p = 0.013), respectively. Notably, ∆PTA positively correlated with ∆HKA in the varus group (r = 0.247, p = 0.032) but not in the valgus group. Between-group differences in postoperative PTA (p < 0.001) and ∆PTA (p < 0.001) were significant. The degree of ankle alignment correction after TKA differed between patients with preexisting varus and valgus ankle deformities. TKA could not effectively correct the preexisting ankle valgus malalignment.

Purpose: Maintaining gap balance is critical for total knee arthroplasty (TKA). This study aimed to elucidate if the extension–flexion gaps would be changed with posterior cruciate ligament (PCL) intact (PI) and PCL resection (PR) during TKA. The flexion gaps were measured using two methods, open-(Fo) and closed-chain position (Fc), based on the definition of kinetic chain position, respectively. Methods: This retrospective study enrolled a total of 33 patients who underwent posterior-stabilized (PS) TKA for symptomatic advanced osteoarthritis of knees. After bone cuts were completed, the extension–flexion gaps before and after PCL resection during TKA were measured using a calibrated tensioning device set at a 100 Nm distraction force. To further differentiate the effect of thigh weight on the 90° flexion gap, two varied methods of examination, either in closed chain (Fc) or open chain (Fo) were performed. Results: The increases in the 90° knee flexion gap after PCL resection were measured by both methods, i.e., ΔFc (PR-Fc—PI-Fc): 2.04 ± 2.06 mm, p < 0.001; and mean ΔFo (PR-Fo—PI-Fo): 1.64 ± 1.36 mm, p < 0.001. However, there were no differences between ΔFc and ΔFo before and after PCL resection. A greater amount of flexion gap was identified in open chain than in closed chain after PCL resection, and the PR-Fo and PR-Fc were 14.36 ± 3.13 and 11.40 ± 3.47 (p < 0.001), respectively. Conclusions: The resection of PCL during TKA distinctly increased the flexion gap, but not the extension gap. This disproportionate increase of the gap will cause a gap balance mismatch. The tensioning maneuver in open-chain was more effective to detect the gap differences than in closed-chain before and after PCL resection during TKA.

Background/Objectives: Intraoperative robotic hip-knee-ankle angle (HKA) measurements are obtained with the patient supine under operative conditions and may not directly represent postoperative standing weight-bearing alignment. This study quantified the correspondence and systematic differences between intraoperative ROSA HKA and standing radiographic HKA after robotic-assisted total knee arthroplasty. Methods: This retrospective single-center study included 208 knees from 178 consecutive patients, including 30 bilateral patients. The primary comparison was post-trial-component intraoperative ROSA HKA versus postoperative standing radiographic HKA; the secondary comparison was pre-cut intraoperative ROSA HKA versus preoperative standing radiographic HKA. Patient-cluster bootstrap confidence intervals used 10,000 resamples. Agreement was evaluated using Bland–Altman analysis, absolute-agreement ICC(A,1), and weighted kappa; Spearman ρ described association. Results: For the primary comparison, mean bias (ROSA minus radiographic HKA) was −1.75° (95% CI −2.08 to −1.43°), conventional limits of agreement were −6.36° to 2.85°, and ICC(A,1) was 0.472 (95% CI 0.379 to 0.557). Differences were within 3° in 70.2% of knees and exceeded 3° in 29.8%; proportional bias was present (slope −0.569, p < 0.001). The secondary comparison showed bias 0.97° and ICC(A,1) 0.956. Exact femoral and tibial size agreement was 42.3% and 80.8%, respectively. The standardized surgeon-entered 10 mm starting value was retained in 57.7% of knees. These systematic and angle-dependent differences may partly reflect the differing loading, positioning, and measurement conditions between the intraoperative trial construct and postoperative standing radiography. Conclusions: Post-trial-component ROSA HKA showed systematic and angle-dependent differences from postoperative standing HKA and should not be considered interchangeable with, or a fixed patient-level predictor of, postoperative standing alignment.

Background: Osteoporosis is a major public health concern and a leading cause of fragility fractures, yet screening with dual-energy X-ray absorptiometry (DXA) remains limited by accessibility and utilization. Opportunistic artificial intelligence (AI)-based analysis of routinely acquired chest radiographs (CXRs) may facilitate osteoporosis risk stratification. This study aimed to independently validate a regulatory-approved deep learning model (VeriOsteo™ OP) in a real-world, male-predominant cohort. Methods: We retrospectively analyzed 2336 patients who underwent both CXR and DXA at a medical center in 2023, including 1688 males (72.3%) and 648 females (27.7%). Osteoporosis was defined as a DXA T-score ≤ −2.5. AI performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC), with sex-specific subgroup analyses. Results: The AI model achieved an accuracy of 76.97%, sensitivity of 69.67%, specificity of 84.64%, PPV of 82.66%, and NPV of 72.65%. The overall AUC was 0.854 (95% CI, 0.839–0.868). The AUC was 0.825 (95% CI, 0.794–0.854) in males and 0.849 (95% CI, 0.831–0.866) in females. PPV was lower in males than in females (60.00% vs. 83.39%), while other performance measures were broadly comparable. The optimal AI cutoff also differed between males (≤−1.9) and females (≤−2.5). Conclusions: The AI model demonstrated good discrimination for opportunistic osteoporosis risk stratification using CXRs in a real-world cohort. Its moderate sensitivity and lower PPV among males highlight potential sex-related differences in AI-assisted osteoporosis assessment. The model may be most appropriately used as an adjunctive risk-stratification and DXA-prioritization tool to identify individuals who may benefit from subsequent DXA evaluation rather than a standalone diagnostic test.