Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

OBJECTIVE: To compare and analyze the biomechanical differences between different transosseous techniques in arthroscopic repairment of rotator cuff injuries by finite element analysis. METHODS: Finite element models of traditional arthroscopic transosseous(ATO) technique, giant needle technique, and ArthroTunneler(AT) technique were established based on the shoulder CT data of a healthy adult. Then, loads of 10 N and 20 N were applied to the sutures on the different technical models, respectively. Compare and analyze the stress changes of the bone tunnels and sutures of the three models were compared and analyzed. RESULTS: Under the same condition of loading, the stress on the lateral bone tunnels and sutures of the traditional ATO technology model was the largest, followed by the giant needle technology model. The stress on the mid-section bone tunnels and sutures of the AT technology model was the largest, followed by the giant needle technology model. Under the different conditions of loading, the high-stress areas of the three models were mainly concentrated on the contact area between the sutures and the bone tunnels. Besides, compared with the traditional ATO technology model, the stress distribution of the lateral bone tunnels and sutures of the giant needle technology and AT technology model were more dispersed, but there was obvious stress concentration phenomena in the stress distribution in the mid-section bone tunnels and sutures in the AT technology model. CONCLUSION Compared with the traditional ATO technique, both the giant needle technique and the AT technique can reduce the risk of cutout between the bone tunnel and suture, and may be better treatments for rotator cuff tear. However, compared with the giant needle technique, the application of AT technique in patients with osteoporosis may be limited.
Adult ; Humans ; Rotator Cuff Injuries/surgery* ; Rotator Cuff/surgery* ; Finite Element Analysis ; Arthroscopy/methods* ; Suture Techniques

Anterior Cruciate Ligament Injuries ; Biomechanical Phenomena ; Humans ; Knee Joint/surgery* ; Posterior Cruciate Ligament/surgery* ; Posterior Cruciate Ligament Reconstruction ; Reconstructive Surgical Procedures ; Robotics