Objective: The aim of the study is to evaluate the efficacy of a karaoke-based digital therapeutic program (CogniSong) in patients with mild cognitive impairment (MCI). Methods: In a 12-week single-center, assessor-blinded randomized controlled trial, 36 patients with MCI were randomly assigned to either the CogniSong digital therapeutics (DTx) group (n=18) or standard treatment group (n=18). The intervention group underwent daily 30-minute karaoke game-based cognitive training sessions via a mobile application. The primary outcome was the change in Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score from baseline to week 12. Secondary outcomes included RBANS subdomain index scores, the Seoul-Instrumental Activities of Daily Living, 17-item Hamilton Depression Rating Scale, Neuropsychiatric Inventory, and Clinical Dementia Rating–Sum of Boxes. Results: The DTx group showed a significantly greater improvement in RBANS total score compared to standard treatment group (mean change +5.9 points vs. -0.2 points, p=0.041). However, there were no significant between-group differences in RBANS subdomain scores or secondary outcomes such as daily functioning, mood, neuropsychiatric symptoms, or global clinical status. Conclusion In this randomized controlled trial, a karaoke-based DTx appeared feasible and effective in enhancing global cognition in MCI. The findings support its potential as a complementary intervention for cognitive improvement in MCI patients.

Chronic respiratory diseases such as idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, and respiratory infections injure the alveoli; the damage evoked is mostly irreversible and occasionally leads to death. Achieving a detailed understanding of the pathogenesis of these fatal respiratory diseases has been hampered by limited access to human alveolar tissue and the differences between mice and humans. Thus, the development of human alveolar organoid (AO) models that mimic in vivo physiology and pathophysiology has gained tremendous attention over the last decade. In recent years, human pluripotent stem cells (hPSCs) have been successfully employed to generate several types of organoids representing different respiratory compartments, including alveolar regions. However, despite continued advances in three-dimensional culture techniques and single-cell genomics, there is still a profound need to improve the cellular heterogeneity and maturity of AOs to recapitulate the key histological and functional features of in vivo alveolar tissue. In particular, the incorporation of immune cells such as macrophages into hPSC-AO systems is crucial for disease modeling and subsequent drug screening. In this review, we summarize current methods for differentiating alveolar epithelial cells from hPSCs followed by AO generation and their applications in disease modeling, drug testing, and toxicity evaluation. In addition, we review how current hPSC-AOs closely resemble in vivo alveoli in terms of phenotype, cellular heterogeneity, and maturity.

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: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background and Objectives: It is unknown whether the presence of low body mass index (BMI) influences the incidence of laryngeal cancer. In a national population-based study, we investigated their relationship retrospectively.Subjects and Method Using the data of Korean Health Insurance claims database, we selected adults aged 20 years or older who underwent a national health examination from January 1, 2007 to December 31, 2008 and were followed up until 2015 for the occurrence of laryngeal cancer. The hazard ratio of laryngeal cancer according to BMI and smoking status in the subjects was analyzed and adjusted for factors such as age, sex, alcohol consumption, and exercise status. Results: Finally, a total of 13675470 subjects were included in the study, and we found that laryngeal cancer occurred in 3731 of those subjects. The risk of developing laryngeal cancer was significantly associated with underweight (BMI <18.5 kg/m2) even after adjustment (hazard ratio of 1.27; 95% confidence interval of 1.11-1.46). There was also a difference according to smoking status. Underweight was not associated with laryngeal cancer in never-smokers, but in ex-smokers and current smokers. Conclusion Being underweight can increase the risk of laryngeal cancer. In particular, this risk can increase if you drink and smoke at the same time.

Background and Objectives: It is unknown whether the presence of low body mass index (BMI) influences the incidence of laryngeal cancer. In a national population-based study, we investigated their relationship retrospectively.Subjects and Method Using the data of Korean Health Insurance claims database, we selected adults aged 20 years or older who underwent a national health examination from January 1, 2007 to December 31, 2008 and were followed up until 2015 for the occurrence of laryngeal cancer. The hazard ratio of laryngeal cancer according to BMI and smoking status in the subjects was analyzed and adjusted for factors such as age, sex, alcohol consumption, and exercise status. Results: Finally, a total of 13675470 subjects were included in the study, and we found that laryngeal cancer occurred in 3731 of those subjects. The risk of developing laryngeal cancer was significantly associated with underweight (BMI <18.5 kg/m2) even after adjustment (hazard ratio of 1.27; 95% confidence interval of 1.11-1.46). There was also a difference according to smoking status. Underweight was not associated with laryngeal cancer in never-smokers, but in ex-smokers and current smokers. Conclusion Being underweight can increase the risk of laryngeal cancer. In particular, this risk can increase if you drink and smoke at the same time.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background and Objectives: It is unknown whether the presence of low body mass index (BMI) influences the incidence of laryngeal cancer. In a national population-based study, we investigated their relationship retrospectively.Subjects and Method Using the data of Korean Health Insurance claims database, we selected adults aged 20 years or older who underwent a national health examination from January 1, 2007 to December 31, 2008 and were followed up until 2015 for the occurrence of laryngeal cancer. The hazard ratio of laryngeal cancer according to BMI and smoking status in the subjects was analyzed and adjusted for factors such as age, sex, alcohol consumption, and exercise status. Results: Finally, a total of 13675470 subjects were included in the study, and we found that laryngeal cancer occurred in 3731 of those subjects. The risk of developing laryngeal cancer was significantly associated with underweight (BMI <18.5 kg/m2) even after adjustment (hazard ratio of 1.27; 95% confidence interval of 1.11-1.46). There was also a difference according to smoking status. Underweight was not associated with laryngeal cancer in never-smokers, but in ex-smokers and current smokers. Conclusion Being underweight can increase the risk of laryngeal cancer. In particular, this risk can increase if you drink and smoke at the same time.

Background and Objectives: It is unknown whether the presence of low body mass index (BMI) influences the incidence of laryngeal cancer. In a national population-based study, we investigated their relationship retrospectively.Subjects and Method Using the data of Korean Health Insurance claims database, we selected adults aged 20 years or older who underwent a national health examination from January 1, 2007 to December 31, 2008 and were followed up until 2015 for the occurrence of laryngeal cancer. The hazard ratio of laryngeal cancer according to BMI and smoking status in the subjects was analyzed and adjusted for factors such as age, sex, alcohol consumption, and exercise status. Results: Finally, a total of 13675470 subjects were included in the study, and we found that laryngeal cancer occurred in 3731 of those subjects. The risk of developing laryngeal cancer was significantly associated with underweight (BMI <18.5 kg/m2) even after adjustment (hazard ratio of 1.27; 95% confidence interval of 1.11-1.46). There was also a difference according to smoking status. Underweight was not associated with laryngeal cancer in never-smokers, but in ex-smokers and current smokers. Conclusion Being underweight can increase the risk of laryngeal cancer. In particular, this risk can increase if you drink and smoke at the same time.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.