The obesity challenge in joint replacement: a multifaceted analysis of self-reported health status and exercise capacity using NHANES data - a population-based study.

BACKGROUND: Joint replacement is successful for end-stage osteopathy, with obesity linked to evaluated risk. But the impact of obesity on self-reported health and exercise capacity among joint replacement patients remains complex and requires investigation. METHODS: This study utilizes data from the National Health and Nutrition Examination Survey (NHANES) to examine the relationship between obesity severity, demographic factors, medical comorbidities, and self-reported health status. The relationship between general health status and BMI was analyzed using multivariable regression, and further illustrated using a restricted cubic spline. Additionally, a bibliometric analysis and systemic review was done to frame the research within the broader context of existing knowledge and demographic specifics. RESULTS: Analysis of NHANES data involving 327 joint replacement patients yielded intriguing insights. The difference in self-reported health between BMI groups did not achieve conventional statistical significance (P=0.06), and multivariable analysis showed that even severely obese patients did not exhibit significantly elevated risk of poor/fair self-reported health compared to normal weight subjects. Among severely obese individuals (BMI>40), 40.63% still rated their health positively. However, stratified analyses indicated that obesity correlated with negative health reports across gender, age, and education strata. Notably, physical functioning emerged as a robust predictor of self-reported health, with those reporting no walking difficulties having significantly lower odds of poor/fair health (Odds Ratio=0.37, P=0.01). CONCLUSION: The study highlights the need for healthcare providers to consider individual physical abilities and comorbidities alongside obesity severity when discussing treatment options with joint replacement patients. It supports tailored interventions and informed shared decision-making. Future research could explore effective weight management strategies for obese individuals undergoing joint replacement.

Which way forward? Comparing radiological outcomes using three different surgical approaches in robotic assisted total hip arthroplasty.

OBJECTIVES: To assess the accuracy and precision of acetabular component placement in robot-assisted surgery total hip arthroplasty (RAS-THA) using three different approaches. METHODS: This study is a secondary analysis from a multicenter, randomized controlled trial comparing the Trex RS Hip 1.0 robot navigation system across different surgical approaches. It involved 145 patients treated at three Chinese medical centers from June 2021 to July 2022. Patients with end-stage joint disease were randomly assigned to either the RAS or control group. Acetabular component positioning was evaluated radiographically, and registration accuracy was measured using Root Mean Square Error (RMSE). RESULTS: The overall RMSE was 0.72 mm (SD = 0.24 mm), indicating consistent accuracy regardless of surgical approach. Significant variations in anteversion were noted across groups (p = 0.001). Lateral RAS-THA showed enhanced precision. The RAS Direct Anterior Approach (DAA) group had the least deviation in the rotation center's horizontal distance (0.89 ± 1.14 mm, p = 0.0014) and minimal leg length discrepancy (2.41 ± 1.17 mm). The RAS DAA approach also produced more consistent results. CONCLUSION: Robotic assistance in THA, especially via the DAA approach, enhances the accuracy and precision of acetabular component positioning. Consistent registration accuracy across various surgical approaches confirms the reliability of these methods for THA. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov identifier is ChiCTR2100044124.

Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning.

Background: Cystic lesions are frequently observed in knee joint diseases and are usually associated with joint pain, degenerative disorders, or acute injury. Magnetic resonance imaging-based, artificial intelligence-assisted cyst detection is an effective method to improve the whole knee joint analysis. However, few studies have investigated this method. This study is the first attempt at auto-detection of knee cysts based on deep learning methods. Methods: This retrospective study collected data from 282 subjects with knee cysts confirmed at our institution from January to October 2021. A Squeeze-and-Excitation (SE) inception attention-based You only look once version 5 (SE-YOLOv5) model was developed based on a self-attention mechanism for knee cyst-like lesion detection and differentiation from knee effusions, both characterized by high T2-weighted signals in magnetic resonance imaging (MRI) scans. Model performance was evaluated via metrics including accuracy, precision, recall, mean average precision (mAP), F1 score, and frames per second (fps). Results: The deep learning model could accurately identify knee MRI scans and auto-detect both obvious cyst lesions and small ones with inconspicuous contrasts. The SE-YOLO V5 model constructed in this study yielded superior performance (F1 = 0.879, precision = 0.887, recall = 0.872, all class mAP0.5 = 0.944, effusion mAP = 0.945, cyst mAP = 0.942) and improved detection speed compared to a traditional YOLO model. Conclusion: This proof-of-concept study examined whether deep learning models could detect knee cysts and distinguish them from knee effusions. The results demonstrated that the classical Yolo V5 and proposed SE-Yolo V5 models could accurately identify cysts.

One Novel Phantom-Less Quantitative Computed Tomography System for Auto-Diagnosis of Osteoporosis Utilizes Low-Dose Chest Computed Tomography Obtained for COVID-19 Screening.

Background: The diagnosis of osteoporosis is still one of the most critical topics for orthopedic surgeons worldwide. One research direction is to use existing clinical imaging data for accurate measurements of bone mineral density (BMD) without additional radiation. Methods: A novel phantom-less quantitative computed tomography (PL-QCT) system was developed to measure BMD and diagnose osteoporosis, as our previous study reported. Compared with traditional phantom-less QCT, this tool can conduct an automatic selection of body tissues and complete the BMD calibration with high efficacy and precision. The function has great advantages in big data screening and thus expands the scope of use of this novel PL-QCT. In this study, we utilized lung cancer or COVID-19 screening low-dose computed tomography (LDCT) of 649 patients for BMD calibration by the novel PL-QCT, and we made the BMD changes with age based on this PL-QCT. Results: The results show that the novel PL-QCT can predict osteoporosis with relatively high accuracy and precision using LDCT, and the AUC values range from 0.68 to 0.88 with DXA results as diagnosis reference. The relationship between PL-QCT BMD with age is close to the real trend population (from ∼160 mg/cc in less than 30 years old to ∼70 mg/cc in greater than 80 years old for both female and male groups). Additionally, the calculation results of Pearson's r-values for correlation between CT values with BMD in different CT devices were 0.85-0.99. Conclusion: To our knowledge, it is the first time for automatic PL-QCT to evaluate the performance against dual-energy X-ray absorptiometry (DXA) in LDCT images. The results indicate that it may be a promising tool for individuals screened for low-dose chest computed tomography.