Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Background: The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diagnostic features, indicating precise tumor segmentation is essential. Therefore, we build a CT image segmentation method based on an encoder–decoder structure combined with a Transformer for volumetric segmentation of adrenal tumors. Methods: This study included a total of 182 patients with adrenal metastases, and an adrenal tumor volumetric segmentation method combining encoder–decoder structure and Transformer was constructed. The Dice Score coefficient (DSC), Hausdorff distance, Intersection over union (IOU), Average surface distance (ASD) and Mean average error (MAE) were calculated to evaluate the performance of the segmentation method. Results: Analyses were made among our proposed method and other CNN-based and transformer-based methods. The results showed excellent segmentation performance, with a mean DSC of 0.858, a mean Hausdorff distance of 10.996, a mean IOU of 0.814, a mean MAE of 0.0005, and a mean ASD of 0.509. The boxplot of all test samples’ segmentation performance implies that the proposed method has the lowest skewness and the highest average prediction performance. Conclusions: Our proposed method can directly generate 3D lesion maps and showed excellent segmentation performance. The comparison of segmentation metrics and visualization results showed that our proposed method performed very well in the segmentation....
Background: Fractures are the most common orthopedic diseases. It is known that static magnetic fields (SMFs) can contribute to the maintenance of bone health. However, the effect and mechanism of SMFs on fracture is still unclear. This study is aim to investigate the effect of moderate static magnetic fields (MMFs) on bone structure and metabolism during fracture healing. Methods: Eight-week-old male C57BL/6J mice were subjected to a unilateral open transverse tibial fracture, and following treatment under geomagnetic field (GMF) or MMF. The micro-computed tomography (Micro-CT) and three-point bending were employed to evaluate the microarchitecture and mechanical properties. Endochondral ossification and bone remodeling were evaluated by bone histomorphometric and serum biochemical assay. In addition, the atomic absorption spectroscopy and ELISA were utilized to examine the influence of MMF exposure on iron metabolism in mice. Results: MMF exposure increased bone mineral density (BMD), bone volume per tissue volume (BV/TV), mechanical properties, and proportion of mineralized bone matrix of the callus during fracture healing. MMF exposure reduced the proportion of cartilage in the callus area during fracture healing. Meanwhile, MMF exposure increased the number of osteoblasts in callus on the 14th day, and reduced the number of osteoclasts on the 28th day of fracture healing. Furthermore, MMF exposure increased PINP and OCN levels, and reduced the TRAP-5b and β-CTX levels in serum. It was also observed that MMF exposure reduced the iron content in the liver and callus, as well as serum ferritin levels while elevating the serum hepcidin concentration. Conclusions: MMF exposure could accelerate fracture healing via promote the endochondral ossification and bone formation while regulating systemic iron metabolism during fracture healing. This study suggests that MMF may have the potential to become a form of physical therapy for fractures....
Background: We aim to study the association between spasticity and active range of motion (ROM) during four repetitive functional tasks such as cone stacking (CS), fast flexion– extension (FFE), fast ball squeezing (FBS), and slow ball squeezing (SBS), and predicted spasticity models. Methods: An experimental study with control and stroke groups was conducted in a Medical Center. A total of sixty-four participants, including healthy control (n = 22; average age (years) = 54.68 ± 9.63; male/female = 12/10) and chronic stroke survivors (n = 42; average age = 56.83 ± 11.74; male/female = 32/10) were recruited. We employed a previously developed smart glove device mounted with multiple inertial measurement unit (IMU) sensors on the upper limbs of healthy and chronic stroke individuals. The recorded ROMs were used to predict subjective spasticity through generalized estimating equations (GEE) for the affected side. Results: The models have significant (p ≤ 0.05 *) prediction of spasticity for the elbow, thumb, index, middle, ring, and little fingers. Overall, during SBS and FFE activities, the maximum number of upper limb joints attained the greater average ROMs. For large joints, the elbow during CS and the wrist during FFE have the highest average ROMs, but smaller joints and the wrist have covered the highest average ROMs during FFE, FBS, and SBS activities. Conclusions: Thus, it is concluded that CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for the thumb and finger joints in chronic stroke survivors....
In the United States, racial disparities have been observed in complications following total joint arthroplasty (TJA), including readmissions and mortality. It is unclear whether such disparities also exist for periprosthetic joint infection (PJI). The clinical data registry of a large New England hospital system was used to identify patients who underwent TJA between January 2018 and December 2021. The comorbidities were evaluated using the Elixhauser Comorbidity Index (ECI). We used Poisson regression to assess the relationship between PJI and race by estimating cumulative incidence ratios (cIRs) and 95% confidence intervals (CIs). We adjusted for age and sex and examined whether ECI was a mediator using structural equation modeling. The final analytic dataset included 10,018 TJAs in 9681 individuals [mean age (SD) 69 (10)]. The majority (96.5%) of the TJAs were performed in non-Hispanic (NH) White individuals. The incidence of PJI was higher among NH Black individuals (3.1%) compared with NH White individuals (1.6%) [adjusted cIR = 2.12, 95%CI = 1.16–3.89; p = 0.015]. Comorbidities significantly mediated the association between race and PJI, accounting for 26% of the total effect of race on PJI incidence. Interventions that increase access to high-quality treatments for comorbidities before and after TJA may reduce racial disparities in PJI....
Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)- driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer....
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