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.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

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.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

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.