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.

Purpose: This study aimed to develop and assess the feasibility and effectiveness of digital therapeutics for supportive care after gastrectomy.Materials and Method: The study included 39 patients with gastric cancer who underwent minimally invasive gastrectomy and were able to use a mobile application (app) on their smartphones. The developed research app automatically calculates and provides daily targets for calorie and protein intake based on the patient’s body mass index (BMI). Patients recorded their daily diets, weights, and symptoms in the app and completed special questionnaires to assess the feasibility of the app in real-world clinical practice. Results: At the 10-week follow-up, the mean questionnaire scores for ease of learning, usability, and effectiveness of the app (primary endpoint) were 2.32±0.41, 2.35±0.43, and 2.4±0.39 (range: 0–3), respectively. Patients were classified as underweight (<18.5, n=4), normal (18.5–24.9, n=24), or overweight (≥25.0, n=11) according to predischarge BMI.Underweight patients showed higher compliance with app usage and a higher rate of achieving the target calorie and protein intake than normal weight and overweight patients (98% vs. 77% vs. 81%, p=0.0313; 102% vs. 75% vs. 61%, P=0.0111; 106% vs. 79% vs. 64%, P=0.0429). Two patients transitioned from underweight to normal weight (50.0%), one patient (4.3%) transitioned from normal weight to underweight, and two patients (22.2%) transitioned from overweight to normal weight. Conclusions The mobile app is feasible and useful for postoperative supportive care in terms of ease of learning, usability, and effectiveness. Digital therapeutics may be an effective way to provide supportive care for postgastrectomy patients, particularly in terms of nutrition.

Background: Difficult airway occurs due to anatomical abnormalities of the airway that can be predicted through airway assessments; however, abnormalities beyond the vocal cord can be clinically asymptomatic and undetected until intubation failure to advance the endotracheal tube.Case: We present a case of an unanticipated difficult airway in a stuporous 80-year-old female with a recent history of intracerebral hemorrhage and prolonged intubation. She required emergency ventriculo-peritoneal shunt surgery due to the progression of her hydrocephalus. Under anesthesia, facemask ventilation was easy and video laryngoscopy provided a full view of the glottis; however, endotracheal tube (ETT) entry failed. We suspected stenosis beyond the vocal cord, and a smaller diameter ETT was inserted and maintained for airway management during emergency surgery. Postoperative neck computed tomography findings revealed laryngotracheal stenosis (LTS). Conclusions Anesthesiologists should be aware that LTS may be asymptomatic and consider difficult airway guidelines in patients with history of prolonged endotracheal intubation.

Background: The rupture of the central slip of an extensor tendon of a finger causes a boutonniere (or buttonhole) deformity, characterized by pathologic flexion at the proximal interphalangeal (PIP) joint and hyperextension at the distal interphalangeal (DIP) joint. Currently, there are no standard treatment guidelines for this deformity. This study aimed to report clinical results of surgery to correct chronic boutonniere deformity. Methods: This retrospective case series was conducted between January 2010 and December 2018 and only 13 patients with trauma-induced chronic deformity were included. After excision of elongated scar tissue, a direct anatomic end-to-end repair using a loop suture technique with supplemental suture anchor augmentation was conducted. Total active motion was assessed before and after surgery and self-satisfaction scores were collected from phone surveys. Results: All patients presented with Burton stage I deformities defined as supple and passively correctable joints. The initial mean extension lag of the PIP joint (43.5°) was improved by an average of 21.9° at the final follow-up (p < 0.001). The mean hyperextension of the DIP joint averaged 19.2° and improved by 0.8° flexion contracture (p < 0.001). The average total active motion was 220.4° (range, 160°–260°). Based on the Souter’s criteria, 69.2% (9/13) of the patients had good results. Only 1 patient reported fair outcome and 23.1% (3/13) reported poor outcome. The average Strickland formula score was 70 (range, 28.6–97.1). In total, 10 patients (77%) had excellent or good results. Of 10 patients contacted by phone, self-reported satisfaction score was very satisfied in 2, satisfied in 3, average in 3, poor in 1, and very poor in 1. Three patients reported a relapse of the deformity during range of motion exercises, 1 of whom underwent revision surgery. One patient complained of PIP joint flexion limitation, and 2 complained of DIP joint flexion limitation at final follow-up. Conclusions In chronic boutonniere deformity, central slip reconstruction with anchor suture augmentation can be an easily applicable surgical option, which offers fair to excellent outcome in 77% of the cases. The risk of residual extension lag and recurrence of deformity should be discussed prior to surgery.

Background: The rupture of the central slip of an extensor tendon of a finger causes a boutonniere (or buttonhole) deformity, characterized by pathologic flexion at the proximal interphalangeal (PIP) joint and hyperextension at the distal interphalangeal (DIP) joint. Currently, there are no standard treatment guidelines for this deformity. This study aimed to report clinical results of surgery to correct chronic boutonniere deformity. Methods: This retrospective case series was conducted between January 2010 and December 2018 and only 13 patients with trauma-induced chronic deformity were included. After excision of elongated scar tissue, a direct anatomic end-to-end repair using a loop suture technique with supplemental suture anchor augmentation was conducted. Total active motion was assessed before and after surgery and self-satisfaction scores were collected from phone surveys. Results: All patients presented with Burton stage I deformities defined as supple and passively correctable joints. The initial mean extension lag of the PIP joint (43.5°) was improved by an average of 21.9° at the final follow-up (p < 0.001). The mean hyperextension of the DIP joint averaged 19.2° and improved by 0.8° flexion contracture (p < 0.001). The average total active motion was 220.4° (range, 160°–260°). Based on the Souter’s criteria, 69.2% (9/13) of the patients had good results. Only 1 patient reported fair outcome and 23.1% (3/13) reported poor outcome. The average Strickland formula score was 70 (range, 28.6–97.1). In total, 10 patients (77%) had excellent or good results. Of 10 patients contacted by phone, self-reported satisfaction score was very satisfied in 2, satisfied in 3, average in 3, poor in 1, and very poor in 1. Three patients reported a relapse of the deformity during range of motion exercises, 1 of whom underwent revision surgery. One patient complained of PIP joint flexion limitation, and 2 complained of DIP joint flexion limitation at final follow-up. Conclusions In chronic boutonniere deformity, central slip reconstruction with anchor suture augmentation can be an easily applicable surgical option, which offers fair to excellent outcome in 77% of the cases. The risk of residual extension lag and recurrence of deformity should be discussed prior to surgery.

Purpose: This study aimed to identify correlations between the status of the umbilical cord and neonatal health status. Methods: In total, 172 newborns were enrolled who were admitted to the newborn nursery with a gestational age of 35 weeks or older and a body weight of 2 kg or above. Data were collected on the basic personal information of the newborns, the diameter and soft tissue status of the umbilical cord, and neonatal health status after birth. Analyses were performed using t-test, analysis of variance, x2 test, and Fisher exact test. Results: Umbilical cord diameter exhibited a statistically significant difference by sex (t=2.71, p=.007). A thin umbilical cord diameter was associated with a 1-minute Apgar score less than 8 points (t=2.47, p=.015) and with being transferred to the intensive care unit (t=2.45, p=.015). Poor soft tissue status of the umbilical cord was associated with a 1-minute Apgar score of less than 8 points (x2=16.68, p<.001) and with oxygen being supplied (x2=4.81, p=.028). Conclusion Assessing the umbilical cord diameter and status in newborns is an important tool for evaluating neonatal health status after birth, and this point also underscores the importance of professionals' careful observations in the newborn nursery.

Ethnically specific data on genetic variation are crucial for understanding human biology and for clinical interpretation of variant pathogenicity. We analyzed data obtained by deep sequencing 1303 Korean whole exomes; the data were generated by three independent whole exome sequencing projects (named the KOEX study). The primary focus of this study was to comprehensively analyze the variant statistics, investigate secondary findings that may have clinical actionability, and identify loci that should be cautiously interpreted for pathogenicity. A total of 495 729 unique variants were identified at exonic regions, including 169 380 nonsynonymous variants and 4356 frameshift insertion/deletions. Among these, 76 607 were novel coding variants. On average, each individual had 7136 nonsynonymous single-nucleotide variants and 74 frameshift insertion/deletions. We classified 13 pathogenic and 13 likely pathogenic variants in 56 genes that may have clinical actionability according to the guidelines of the American College of Medical Genetics and Genomics, and the Association for Molecular Pathology. The carrier frequency of these 26 variants was 2.46% (95% confidence interval 1.73–3.46). To identify loci that require cautious interpretation in clinical sequencing, we identified 18 genes that are prone to sequencing errors, and 671 genes that are highly polymorphic and carry excess nonsynonymous variants. The catalog of identified variants, its annotation and frequency information are publicly available (https://koex.snu.ac.kr). These findings should be useful resources for investigating ethnically specific characteristics in human health and disease.
Biology ; Clinical Coding ; Exome ; Exons ; Genetic Variation ; Genetics, Medical ; Genomics ; High-Throughput Nucleotide Sequencing ; Humans ; Pathology, Molecular ; Virulence