Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To compare the application value of three image post-processing techniques volume rendering(VR),multiplanar reformation(MPR)and curved planar reformation(CPR)in the identifi-cation of rib fracture malunion.Methods The types and numbers of rib fracture malunion in 75 pa-tients were recorded,and the sensitivity,specificity,accuracy and Youden index of VR,MPR and CPR in the diagnosis of rib fracture malunion were compared.Receiver operator characteristic(ROC)curve was drawn and area under the curve(AUC)was calculated,and the detection rates of three image post-processing techniques for different types of rib fracture malunion were compared.Results A total of 243 rib fractures were malunion in 75 patients.The diagnostic sensitivity of VR,MPR and CPR for rib fracture malunion was 52.67%,79.84%and 91.36%,the specificity was 99.58%,97.89%and 99.15%,the accuracy was 83.66%,91.76%and 96.51%,the Youden index was 0.52,0.78 and 0.91,the AUC was 0.761,0.889 and 0.953,respectively.Compared with VR,there were statistically signifi-cant differences in the number of broken rib end misalignment over 1/3,broken rib end overlap,bro-ken rib end angulation and intercostal bridge detected in MPR(P<0.05).Compared with VR,there was a statistically significant difference in the number of different types of rib fracture malunion de-tected by CPR(P<0.05).Compared with MPR,there were statistically significant differences in the number of broken rib end misalignment over 1/3,broken rib end separation and intercostal bridge de-tected in CPR(P<0.05).Conclusion The three image post-processing techniques are of great signifi-cance for the identification of rib fracture malunion.Especially CPR is highly effective in the diagno-sis of rib fracture malunion,and can be used as the main post-processing technique for forensic clini-cal identification of rib fracture malunion.

Objective:To seek a correlation between short-chain fatty acids (SCFAs) and skill in the activities of daily living (ADL) after an ischemic stroke.Methods:Ninety ischemic stroke survivors were assessed using the Barthel Index (BI). Fecal samples were collected and analyzed for the concentration of acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid using gas chromatography. Spearman correlation analysis was conducted to identify SCFAs that correlated with the total BI score. Linear regressions were evaluated to explore the correlation between the total BI score and SCFAs.Results:The concentrations of propionic and butyric acids in the feces were found to correlate significantly with the total BI scores. Data including propionic acid and butyric acid levels, age, gender, body mass index, disease duration, any history of hypertension or diabetes, and other SCFAs were included in the regression models. Propionic and butyric acid levels were found to be potentially useful predictors of total BI scores.Conclusions:The concentration of propionic and butyric acids in the feces after an ischemic stroke can predict the survivor′s total BI score. Those concentrations could therefore be useful for predicting ADL ability.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Purpose: This study was conducted to evaluate the efficacy of bladder outlet surgery in patients with detrusor underactivity (DU) and to identify factors associated with successful outcomes. Methods: We conducted a retrospective review of men diagnosed with DU in urodynamic studies who underwent bladder outlet surgery for lower urinary tract symptoms between May 2018 and April 2023. The International Prostate Symptom Score (IPSS) questionnaire, uroflowmetry (UFM), and multichannel urodynamic studies were administered. Successful treatment outcomes were defined as either an IPSS improvement of at least 50% or the regaining of spontaneous voiding in patients urethral catheterization prior to surgery. Results: The study included 93 male patients. Men diagnosed with significant or equivocal bladder outlet obstruction (BOO) experienced significant postoperative improvements in IPSS (from 20.6 to 6.0 and from 17.4 to 6.5, respectively), maximum urine flow rate (from 5.0 mL/sec to 14.4 mL/sec and from 8.8 mL/sec to 12.2 mL/sec, respectively) and voiding efficiency (from 48.8% to 86.0% and from 61.2% to 85.1%, respectively). However, in the group without obstruction, the improvements in IPSS and UFM results were not significant. The presence of detrusor overactivity (odds ratio [OR], 3.152; P=0.025) and preoperative urinary catheterization (OR, 2.756; P=0.040) were associated with favorable treatment outcomes. Conversely, an unobstructed bladder outlet was identified as a negative prognostic factor. Conclusions In men with DU accompanied by equivocal or significant BOO, surgical intervention to alleviate the obstruction may enhance the IPSS, quality of life, and UFM results. However, those with DU and an unobstructed bladder outlet face a comparatively high risk of treatment failure. Preoperative detrusor overactivity and urinary catheterization are associated with more favorable surgical outcomes. Consequently, active deobstructive surgery should be considered for patients with DU who are experiencing urinary retention.

Objective: To determine the feasibility of using temporary permanent pacemaker (TPPM) in patients with high-degree atrioventricular block (AVB) after transcatheter aortic valve replacement (TAVR) as bridging strategy to reduce avoidable permanent pacemaker implantation. Methods: This is a prospective observational study. Consecutive patients undergoing TAVR at Beijing Anzhen Hospital and the First Affiliated Hospital of Zhengzhou University from August 2021 to February 2022 were screened. Patients with high-degree AVB and TPPM were included. Patients were followed up for 4 weeks with pacemaker interrogation at every week. The endpoint was the success rate of TPPM removal and free from permanent pacemaker at 1 month after TPPM. The criteria of removing TPPM was no indication of permanent pacing and no pacing signal in 12 lead electrocardiogram (EGG) and 24 hours dynamic EGG, meanwhile the last pacemaker interrogation indicated that ventricular pacing rate was 0. Routinely follow-up ECG was extended to 6 months after removal of TPPM. Results: Ten patients met the inclusion criteria for TPPM, aged (77.0±11.1) years, wirh 7 females. There were 7 patients with third-degree AVB, 1 patient with second-degree AVB, 2 patients with first degree AVB with PR interval>240 ms and LBBB with QRS duration>150 ms. TPPM were applied on the 10 patients for (35±7) days. Among 8 patients with high-degree AVB, 3 recovered to sinus rhythm, and 3 recovered to sinus rhythm with bundle branch block. The other 2 patients with persistent third-degree AVB received permanent pacemaker implantation. For the 2 patients with first-degree AVB and LBBB, PR interval shortened to within 200 ms. TPPM was successfully removed in 8 patients (8/10) at 1 month without permanent pacemaker implantation, of which 2 patients recovered within 24 hours after TAVR and 6 patients recovered 24 hours later after TAVR. No aggravation of conduction block or permanent pacemaker indication were observed in 8 patients during follow-up at 6 months. No procedure-related adverse events occurred in all patients. Conclusion: TPPM is reliable and safe to provide certain buffer time to distinguish whether a permanent pacemaker is necessary in patients with high-degree conduction block after TAVR.
Female ; Humans ; Atrioventricular Block/therapy* ; Feasibility Studies ; Transcatheter Aortic Valve Replacement ; Pacemaker, Artificial ; Bundle-Branch Block