RESUMO
Background: Hip fractures, including femoral neck fractures, are a significant cause of morbidity and mortality in the elderly population and are typically diagnosed using plain radiography. However, diagnosing non-displaced femoral neck fractures can be challenging due to their subtle appearance on hip radiographs. Previous deep-learning models have shown low accuracy in identifying these fractures on anteroposterior (AP) radiographs; however, no studies have used lateral radiographs. This study aimed to evaluate the potential of using deep-learning with both AP and lateral hip radiographs to automatically identify non-displaced femoral neck fractures. Methods: We conducted a retrospective analysis of patients with femoral neck fractures at The First Affiliated Hospital of Xiamen University. All the hip radiographs were reviewed, and cases of non-displaced femoral neck fractures were included in the study. Additionally, 439 participants with normal hip radiographs were also included in the study. A vision transformer (Vit) model was developed using 1,536 AP and lateral hip radiograph. The model's performance was compared to the performance of two groups of human observers: an expert group comprising orthopedic surgeons and radiologists, and a non-expert group, including emergency physicians and general practice doctors. We also carried out the external validation using two additional data sets to assess the generalizability of the model. Results: The Vit model showed exceptional performance in detecting non-displaced femoral neck fractures on paired AP and lateral hip radiographs, achieving a binary accuracy of 95.8% [95% confidence interval (CI): 94.9%, 96.8%] and an area under the curve (AUC) of 0.988. Compared to the human observers, the model had a higher accuracy of 96.7% (95% CI: 93.9%, 99.5%) on the paired AP and lateral hip radiographs, while the accuracy of the expert group was 90.5% (95% CI: 85.7%, 95.2%). Further, the model maintained good performance during the external validation, with an AUC of 0.959 on the paired AP and lateral views. Conclusions: Our Vit model showed expert-level performance in identifying non-displaced femoral neck fractures on paired AP and lateral hip radiographs. This model has the potential to enhance diagnosis accuracy and improve patient outcomes by reducing the need for additional examinations and preoperative time.
RESUMO
This study focused on the separation, characterization, content determination, and antiviral efficacy research on colloidal particles with different sizes in Maxing Shigan Decoction(MXSG). The mixed colloidal phase of MXSG was initially separated into small colloidal particle segment(S), medium colloidal particle segment(M), and big colloidal particle segment(B) using ultrafiltration. Further fine separation was performed using size-exclusion chromatography. Dynamic light scattering(DLS) and transmission electron microscopy(TEM) were employed to characterize the size and morphology of the separated colloidal particles. UPLC-MS/MS was used to determine the content of ephedrine, amygdalin, glycyrrhizic acid, and the EDTA complexometric titration was used to measure the calcium(Ca~(2+)) content in different colloidal phases. Finally, a respiratory syncytial virus(RSV) infection mouse model was established using intranasal administration. The experimental groups included a blank group, a model group, a ribavirin group, an MXSG group, an S group, an M group, and a B group. Oral administration was given for treatment, and pathological changes in mouse lung tissue and organ indices were evaluated. The results of the study showed that the distribution of ephedrine, amygdalin, glycyrrhizic acid, and Ca~(2+) content was not uniform among different colloidal segments. Among them, the B segment had the highest proportions of the three components, except for Ca~(2+), accounting for 46.35%, 53.72%, and 92.36%, respectively. Size-exclusion chromatography separated colloidal particles with uniform morphology in the size range of 100-500 nm. Compared to the S and M segments, the B segment showed an increased lung index inhibition rate(38.31%), spleen index, and thymus index in RSV-infected mice, and it improved the infiltration of inflammatory cells and lung injury in the lung tissue of mice. The complex components in MXSG form colloidal particles of various sizes and morphologies through heating, and small-molecule active components such as ephedrine, amygdalin, glycyrrhizic acid, and Ca~(2+) participate in the assembly to varying degrees. The main material basis for the antiviral effect of MXSG is the colloidal particles with certain particle sizes formed by the assembly of active components during the heating process.
