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Bone is in a continuous state of remodeling whereby old bone is absorbed and new bone is formed in its place. During this process, new formations reinforce the bone in the direction of the dominant stress trajectories through a functional adaptation. In normal aging, the balance between bone resorption and formation can be shifted. How this affects the functional adaptation remains to be investigated. Furthermore, how or whether the bone continues to change beyond the age of 85 is not yet studied in detail. In this study we examined the age-related changes in the cortical and trabecular bone in old age, and assessed whether we can find evidence of the presence of functional adaptation. We measured cortical and trabecular parameters from micro-computed tomography scans of the femoral head extracted from hip fracture patients between the age of 70 and 93 years. A significant decrease in global trabecular bone mineral density (38.1%) and cortical thickness (13.0%) was seen from the 9th to the 10th decade of life. The degree of anisotropy was maintained globally as well as locally in both high and low stress regions. The local trabecular bone mineral density decreased in both high stress and low stress regions between the 9th and 10th decade of life with similar trends. This suggests that the role of functional adaptation in maintaining the bone structural integrity in old age may be limited. This study highlights the need for a controlled clinical trial examining the cause of the continued bone degradation throughout old age.

IntroductionForearm dual-energy X-ray absorptiometry (DXA) is often performed in clinics where central DXA is unavailable. Accurate bone mineral density (BMD) measurement is crucial for clinical assessment. Forearm rotation can affect BMD measurements, but this effect remains uncertain. Thus, we aimed to conduct a simulation study using CT images to clarify the effect of forearm rotation on BMD measurements.Materials and methodsForearm CT images of 60 women were analyzed. BMD was measured at the total, ultra-distal (UD), mid-distal (MD), and distal 33% radius regions with the radius located at the neutral position using digitally reconstructed radiographs generated from CT images. Then, the rotation was altered from − 30° to 30° (supination set as positive) with a one-degree increment, and the percent BMD changes from the neutral position were quantified for all regions at each angle for each patient.ResultsThe maximum mean BMD changes were 5.8%, 7.0%, 6.2%, and 7.2% for the total, UD, MD, and distal 33% radius regions, respectively. The analysis of the absolute values of the percent BMD changes from the neutral position showed that BMD changes of all patients remained within 2% when the rotation was between − 5° and 7° for the total region, between − 3° and 2° for the UD region, between − 4° and 3° for the MD region, and between − 3° and 1° for the distal 33% radius region.ConclusionSubtle rotational changes affected the BMD measurement of each region. The results showed the importance of forearm positioning when measuring the distal radius BMD.

Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has improved all segmentation accuracy and structure volume/density evaluation metrics compared to a shallower baseline model with a smaller training database (N = 20). The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs ≥ .95) in detecting inaccurate and failed segmentations. Furthermore, the study has shown an impact of the disease severity status on the model’s predictive uncertainties when applied to a large-scale database. The high segmentation and muscle volume/density estimation accuracy and the high accuracy in failure detection based on the predictive uncertainty exhibited the model’s reliability for analyzing individual MSK structures in large-scale CT databases.

While accurate measurement of bone mineral density (BMD) is essential in the diagnosis of osteoporosis and in evaluating the treatment of osteoporosis, it is unclear how region of interest (ROI) settings affect measurement of BMD at the total proximal femur region. In this study, we performed a simulation analysis to clarify the effect on BMD measurement of changing the ROI using hip computed tomography (CT) images of 75 females (mean age, 62.4 years). Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the change in BMD when the proximal boundary of the ROI was altered by 0–10 mm, and when the distal boundary of the ROI was altered by 0–30 mm. Further, changes in BMD were compared across BMD classification groups. A mean BMD increase of 0.62% was found for each 1-mm extension of the distal boundary. A mean BMD decrease of 0.18% was found for each 1-mm alteration of the proximal boundary. Comparing BMD classification groups, patients with osteoporosis and osteopenia demonstrated greater BMD changes than patients with normal BMD for the distal boundary (0.68%, 0.64%, and 0.54%, respectively) and patients with osteoporosis demonstrated greater BMD changes than patients with osteoporosis and normal BMD for the proximal boundary (0.37%, 0.13%, and 0.03%, respectively). In conclusion, our study found that a consistent ROI setting, especially on the distal boundary, is necessary for the accurate measurement of total proximal femur BMD. Based on the findings, we recommend confirming that the ROI setting shown on the BMD result form is consistent with changes in serial BMD.

Research is lacking on the effect of intraoperative pelvic tracker displacement relative to the pelvis on cup orientation accuracy in computed tomography (CT)-based navigation (CTN) or multivariable analysis to detect factors associated with CTN accuracy. Here, we asked: (1) how pelvic tracker displacement influences the CTN accuracy of cup orientation in total hip arthroplasty (THA)? and (2) what factors are associated with CTN accuracy on multivariable analysis? Regarding cup orientation in 446 THA procedures using CTN, we evaluated clinical error defined as the difference between postoperative measurement and preoperative planning and measurement error defined as the difference between postoperative and intraoperative measurements. Multivariable regression analyses detected the associated factors. Subjects with an intraoperative tracker displacement of < 2 mm were classified in the verified group. Mean absolute clinical and measurement errors were < 1.5° in the verified group, whereas the measurement error of 2.6° for cup inclination and 1.3° for anteversion was larger in the non-verified versus verified group. Tracker displacement and screw fixation were associated with larger clinical errors, while tracker displacement and surgeon inexperience were associated with larger measurement errors. Clinical and measurement accuracies were high for CTN cup placement with rigid pelvic tracker fixation.

This paper presents methods of decomposition of musculoskeletal structures from radiographs into multiple individual muscle and bone structures. While existing solutions require dual-energy scan for the training dataset and are mainly applied to structures with high-intensity contrast, such as bones, we focused on multiple superimposed muscles with subtle contrast in addition to bones. The decomposition problem is formulated as an image translation problem between (1) a real X-ray image and (2) multiple digitally reconstructed radiographs, each of which contains a single muscle or bone structure, and solved using unpaired training based on the CycleGAN framework. The training dataset was created via automatic computed tomography (CT) segmentation of muscle/bone regions and virtually projecting them with geometric parameters similar to the real X-ray images. Two additional features were incorporated into the CycleGAN framework to achieve a high-resolution and accurate decomposition: hierarchical learning and reconstruction loss with the gradient correlation similarity metric. Furthermore, we introduced a new diagnostic metric for muscle asymmetry directly measured from a plain X-ray image to validate the proposed method. Our simulation and real-image experiments using real X-ray and CT images of 475 patients with hip diseases suggested that each additional feature significantly enhanced the decomposition accuracy. The experiments also evaluated the accuracy of muscle volume ratio measurement, which suggested a potential application to muscle asymmetry assessment from an X-ray image for diagnostic and therapeutic assistance. The improved CycleGAN framework can be applied for investigating the decomposition of musculoskeletal structures from single radiographs.

Periprosthetic joint infection (PJI) is a catastrophic complication after total hip arthroplasty. A new drug-loaded carbon fiber-reinforced polymer (CFRP) prosthesis with a sustained drug-release mechanism is being developed for one-stage surgery. We aimed to examine the diffusion dynamics of vancomycin from vancomycin paste-loaded CFRP implants. The differences in the in vitro diffusion dynamics of vancomycin paste were investigated using the elution test by varying parameters. These included the mixing ratio of vancomycin and distilled water (1:0.8, 1:1.2, and 1:1.4) for vancomycin paste, and hole diameter (1 mm and 2 mm) on the container. The in vivo diffusion dynamics were investigated using a rabbit model with vancomycin-loaded CFRP implants placed subcutaneously. The in vitro experiments showed that the diffusion effect of vancomycin was highest in the parameters of vancomycin paste with distilled water mixed in a ratio of 1:1.4, and with a 2 mm hole diameter. The in vivo experiments revealed diffusion dynamics similar to those observed in the in vitro study. The drug diffusion effect tended to be high for vancomycin paste with a large water ratio, and a large diameter of holes. These results indicate that the drug diffusion dynamics from a CFRP implant with holes can be adjusted by varying the water ratio of the vancomycin paste, and the hole size on the CFRP implant.

This study used a rabbit model to investigate the osteocompatibility of Si 3 N 4 -coated carbon fiber-reinforced polyetheretherketone (CFRP) and hydroxyapatite (HA)-coated CFRP with antibiotics (vancomycin [VCM]) and antithrombotic drugs (polyvinylpyrrolidone [PVP]). HA-coated cylindrical CFRP implants were used as the controls (HA), and HA-coated implants treated with VCM and PVP were prepared (HA-VP) as the test groups; a cylindrical CFRP coated with Si 3 N 4 was also prepared (SiN). Ten implants from each group were randomly inserted into the femoral diaphysis of rabbits. The pull-out test, radiological analysis using micro-computed tomography (µ-CT), and histological analysis were performed. The pull-out strength of the SiN group was lower than that of the HA group. µ-CT analysis revealed that the amount of bone formation around the implant in the SiN group was inferior to that in the HA group. Conversely, the HA-VP group had equivalent pull-out strength and bone formation as analyzed by µ-CT compared to the HA group. In conclusion, the additional surface treatment of the HA-coated CFRP with VCM and PVP provided sufficient bone fixation and formation.

Introduction Recently, large-scale language models, such as ChatGPT (OpenAI, San Francisco, CA), have evolved. These models are designed to think and act like humans and possess a broad range of specialized knowledge. GPT-3.5 was reported to be at a level of passing the United States Medical Licensing Examination. Its capabilities continue to evolve, and in October 2023, GPT-4V became available as a model capable of image recognition. Therefore, it is important to know the current performance of these models because they will be soon incorporated into medical practice. We aimed to evaluate the performance of ChatGPT in the field of orthopedic surgery. Methods We used three years' worth of Japanese Board of Orthopaedic Surgery Examinations (JBOSE) conducted in 2021, 2022, and 2023. Questions and their multiple-choice answers were used in their original Japanese form, as was the official examination rubric. We inputted these questions into three versions of ChatGPT: GPT-3.5, GPT-4, and GPT-4V. For image-based questions, we inputted only textual statements for GPT-3.5 and GPT-4, and both image and textual statements for GPT-4V. As the minimum scoring rate acquired to pass is not officially disclosed, it was calculated using publicly available data. Results The estimated minimum scoring rate acquired to pass was calculated as 50.1% (43.7-53.8%). For GPT-4, even when answering all questions, including the image-based ones, the percentage of correct answers was 59% (55-61%) and GPT-4 was able to achieve the passing line. When excluding image-based questions, the score reached 67% (63-73%). For GPT-3.5, the percentage was limited to 30% (28-32%), and this version could not pass the examination. There was a significant difference in the performance between GPT-4 and GPT-3.5 (p < 0.001). For image-based questions, the percentage of correct answers was 25% in GPT-3.5, 38% in GPT-4, and 38% in GPT-4V. There was no significant difference in the performance for image-based questions between GPT-4 and GPT-4V. Conclusions ChatGPT had enough performance to pass the orthopedic specialist examination. After adding further training data such as images, ChatGPT is expected to be applied to the orthopedics field.

SummaryThe patient’s position may affect the bone mineral density (BMD) measurements; however, the extent of this effect is undefined. This CT image-based simulation study quantified changes in BMD induced by hip flexion, adduction, and rotations to recommend appropriate patient positioning when acquiring dual-energy x-ray absorptiometry images.PurposeSeveral studies have analyzed the effect of hip rotation on the measurement of bone mineral density (BMD) of the proximal femur by dual-energy x-ray absorptiometry (DXA). However, as the effects of hip flexion and abduction on BMD measurements remain uncertain, a computational simulation study using CT images was performed in this study.MethodsHip CT images of 120 patients (33 men and 87 women; mean age, 82.1 ± 9.4 years) were used for analysis. Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the BMD of the proximal femur region. BMD at the neutral position was quantified, and the percent changes in BMD when hip internal rotation was altered from −30° to 15°, when hip flexion was altered from 0° to 30°, and when hip abduction was altered from –15° to 30° were quantified. Analyses were automatically performed with a 1° increment in each direction using computer programming.ResultsThe alteration of hip angles in each direction affected BMD measurements, with the largest changes found for hip flexion (maximum change of 17.7% at 30° flexion) and the smallest changes found for hip rotation (maximum change of 2.2% at 15° internal rotation). The BMD measurements increased by 0.34% for each 1° of hip abduction, and the maximum change was 12.3% at 30° abduction.ConclusionThis simulation study quantified the amount of BMD change induced by altering the hip position. Based on these results, we recommend that patients be positioned carefully when acquiring DXA images.