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.

Purpose: To evaluate the prevalence of intraocular lens rotation (IOL) and related factors during combined vitrectomy and cataract surgery. Methods: A university hospital, non-comparative pilot study. The medical records of patients who underwent combined vitrectomy and cataract surgery were retrospectively investigated. Surgical videos were analyzed to observe the degree and timing of rotation of IOL during vitrectomy. Scleral indentation and peripheral vitrectomy were started from 6 o’clock of the eyeball and proceeded counterclockwise, and the presence of rotation was defined when IOL rotated more than 2 hours (60 degrees). Results: A total of 181 eyes were enrolled in this study, and the rotation of IOL occurred in 13 eyes (7.2%). IOL rotation was observed in 11 out of 117 eyes (9.4%) using 1-piece IOL and 2 out of 64 eyes (3.1%) using 3-piece IOL, and the difference was not significant. The direction of IOL rotation was counterclockwise in 11 eyes (84.6%) and rotated during peripheral vitrectomy and scleral indentation in 12 eyes (92.3%). Conclusions Since the rotation of IOL may occur during combined vitrectomy and cataract surgery, the axis of astigmatism should be checked at the end of the operation when using toric IOL.

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.

Purpose: This study aimed to investigate changes in cytokine levels after intravitreal bevacizumab injection in patients with chronic central serous chorioretinopathy (CSC). Methods: In a prospective interventional trial, 12 eyes from 12 patients with chronic CSC and six eyes from six patients who underwent cataract surgery were included as controls. Patients diagnosed as with CSC received a single intravitreal injection of bevacizumab (1.25 mg/0.05 mL). Aqueous humor samples were collected from the patients and controls. Best-corrected visual acuity and foveal thickness were evaluated, and aqueous samples were obtained before and 4 weeks after injection. The aqueous concentrations of interleukin (IL)-6, IL-8, interferon-induced protein (IP)-10, monocyte chemotactic protein (MCP)-1, platelet-derived growth factor (PDGF)-AA, and vascular endothelial growth factor (VEGF) were measured using a multiplex bead assay. Results: After injection, the foveal thickness decreased significantly from 328.08 μm (range, 210–477 μm) to 283.91 μm (range, 168–356 μm; p = 0.048), but the best-corrected visual acuity was not significantly different (p = 0.066). The aqueous levels of IL-8 increased significantly from 3.3 pg/mL (range, 1.5–8.3 pg/mL) to 4.7 pg/mL (range, 2.2–11.6 pg/mL) at 4 weeks after the injection (p = 0.046). The aqueous levels of VEGF decreased significantly from 31.4 pg/mL (range, 17.0–53.3 pg/mL) to 15.2 pg/mL (range, 7.7–21.5 pg/mL; p < 0.01). No significant changes in levels of IL-6 (p = 0.455), IP-10 (p = 0.055), MCP-1 (p = 0.076), and PDGF-AA (p = 0.339) were noted 4 weeks after injection. Conclusions In this study we found intravitreal bevacizumab injection decreased VEGF and increased IL-8 in the eyes of patients with chronic CSC. This study suggests the possibility that the pathogenesis of CSC may be related to abnormal circulation of the choroidal blood vessels through VEGF and IL-8 cytokine level changes.

Objectives: The aim of this study was to investigate dietary intakes and nutritional status among Uganda primary school children from two selected schools in urban and rural areas. Methods: Data were collected from 350 pupils (6-14 years) in Mpigi district, Uganda. All participants were offered a school lunch meal (usually maize porridge and boiled beans). Dietary survey was conducted in October 2016. Data for dietary intake levels were collected by the 24-hour recall method with trained school staffs. The data were converted into nutrient intakes using the CAN-Pro 5.0 Program and compared with KDRIs to evaluate the nutritional status of the subjects. Diet quality indexes such as nutrient density, nutrient adequacy ratio (NAR), mean adequacy ratio (MAR), and index of nutritional quality (INQ) and a diet diversity index such as diet diversity score (DDS) were calculated to evaluate nutritional status among subjects. Data were analyzed using SPSS statistical programs. Results: Results show that the intakes of most nutrients were significantly different by schools. The nutritional status of micro-nutrients was very low in both schools according to analysis of nutritional indexes such as NARs and INQs. Students from both schools should improve intakes of micro-nutrients related to child growth such as calcium, Vitamin B6, zinc and folate. According to the analysis of dietary diversity, there was difference in dietary patterns by schools presumably due to their locations. Conclusions This suggests that current meals could not provide adequate nutrients for the subjects and urgent nutrition interventions for school food services are needed to improve their nutritional well-being. New foods supplements based on local cuisine are also needed to ensure dietary diversity and sustainable development plans.