Purpose: Various problems have been reported with tension-band wire (TBW) fixation. With the devel-opment of anatomical plates and the improvement of fixation forces, plate fixation is currently being performed for non-comminuted, displaced, transverse olecranon fractures (Mayo Type 2A). This study compared the usefulness of the above two procedures applied in non-comminuted, displaced, transverse olecranon fractures. Materials and Methods: Fifty-three patients with Mayo Type 2A were studied retrospectively. Twenty-nine patients underwent TBW fixation, while the other 24 underwent plate fixation. The averageoutpatient follow-up period was 10 months for both groups. Both groups were analyzed radiologically and clinically. The radiological assessment included the time to bone union, joint stability, and presence of traumatic osteoarthritis at the final follow-up. The clinical assessment included the operation time,range of motion of the elbow joint, Mayo Elbow Performance Score (MEPS), Disability of the Arm, Shoulder and Hand (DASH) score, and the presence of postoperative complications. Results: Both groups showed stable elbow joints, proper union of fractures, and no traumatic osteo-arthritis at the final follow-up. The range of motion for the TBW fixation group was 142° (range, 3°-145°), while that of the plate fixation group was 135° (range, 4°-139°) at the final follow-up (p=0.219). The MEPS was 98.2 and 97.7 for the TBW fixation and plate fixation groups, respectively (p=0.675). The DASH score was 10.7 and 13.9 for the TBW fixation and plate fixation groups, respectively. Both groups showed excellent results, and the differences were not statistically significant (p=0.289). Conclusion TBW fixation and plate fixation were compared in non-comminuted, displaced, transverse olecranon fractures, and good results were obtained without significant differences between the two groups. Hence, surgeons should choose a technique they are more confident with and can be applied more efficiently.

Angiography ; Bias (Epidemiology) ; Constriction, Pathologic ; Coronary Angiography ; Fractional Flow Reserve, Myocardial ; Humans ; Ischemia ; Patient-Specific Modeling ; Sensitivity and Specificity

Purpose: To compare image quality in selective intracoronary contrast-injected computed tomography angiography (SelectiveCTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). Materials and Methods: Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. Results: Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078).The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08). Conclusion Our feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.

Purpose: To describe the incidence and mortality of upper tract urothelial carcinoma (UTUC) from 2002–2020 using data from the Korean National Health Insurance Service, which contains data from the entire Korean population. Materials and Methods: Reimbursement records for 43,255 patients diagnosed with primary UTUC (according to the International Classification of Disease 10th revision code C65 and C66) between 2002–2020 were retrieved. The study period was split into four: period I (2002–2005), period II (2006–2010), period III (2011–2015), and period IV (2016–2020). Trends were quantified by calculating the annual percentage change (APC). Mortality data were obtained from the Statistics Korea. Results: From 2002–2020, the incidence of UTUC in Korea increased gradually from 9.34 to 11.40 per 100,000 person-years. Although there was a male predominance, the male to female ratio did not change significantly over time; however, age at the time of diagnosis, the comorbidity index, and the proportion of patients undergoing open/laparoscopic surgery increased significantly over time. There was a modest improvement in 5-year survival (both all cause- and cancer-specific) over the study period. Multivariate analysis identified age at diagnosis, sex, the comorbidity index, and open/laparoscopic surgery as being associated with survival. Conclusions Between 2002 and 2020, the incidence of UTUC in Korea showed a general upward trend; however, survival outcomes have improved. These representative datasets from the Korean population might provide crucial information that enables clinicians to better understand of the epidemiology of UTUC in Korea.

Purpose: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. Materials and Methods: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. Results: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p<0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. Conclusion The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.