오재훈 교수

Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. It has recently been hypothesized that representation reuse, which makes little change in efficient representations, is the dominant factor in the performance of the meta-initialized model through MAML in contrast to repr

Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between them by analyzing Federated Averaging at the client level and determine that a better federated global model performance does not constantly improve personalization. To elucidate t

Previous studies have demonstrated the potential for using smartwatches with a built-in accelerometer as feedback devices for high-quality chest compression during cardiopulmonary resuscitation. However, to the best of our knowledge, no previous study has reported the effects of this feedback on chest compressions in action. A randomized, parallel controlled study of 40 senior medical students was conducted to examine the effect of chest compression feedback via a smartwatch during cardiopulmona

OBJECTIVE: Healthcare providers in emergency departments should wear respirators for infection protection. However, the wearer's vigorous movements during cardiopulmonary resuscitation may affect the protective performance of the respirator. Herein, we aimed to assess the effects of chest compressions (CCs) on the protective performance of respirators. METHODS: This crossover study evaluated 30 healthcare providers from 1 emergency department who performed CC with real-time feedback. The first,

Objective . There are many smartphone-based applications (apps) for cardiopulmonary resuscitation (CPR) training. We investigated the conformity and the learnability/usability of these apps for CPR training and real-life supports. Methods . We conducted a mixed-method, sequential explanatory study to assess CPR training apps downloaded on two apps stores in South Korea. Apps were collected with inclusion criteria as follows, Korean-language instruction, training features, and emergency supports

Continue CPR Attach monitoring/defibrillator Give 100% oxygen Shockable rhythm? VF/pVT Defibrillation CPR 2 min IV/IO access Epinephrine 1 mg every 3-5 min CPR 2 min IV/IO access Epinephrine 1 mg every 3-5 min Asystole

This study aimed to verify a deep convolutional neural network (CNN) algorithm to detect intussusception in children using a human-annotated data set of plain abdominal X-rays from affected children. From January 2005 to August 2019, 1449 images were collected from plain abdominal X-rays of patients ≤ 6 years old who were diagnosed with intussusception while 9935 images were collected from patients without intussusception from three tertiary academic hospitals (A, B, and C data sets). Single Sho

Abstract Background The effects of the body mass index (BMI) on outcomes of patients resuscitated from cardiac arrest are controversial. Therefore, the current study investigated the association between the BMI and the favourable neurologic outcomes and survival to discharge of patients resuscitated from out-of-hospital cardiac arrest (OHCA). Methods This multicentre, prospective, nationwide OHCA registry-based study was conducted using data from the Korean Cardiac Arrest Resuscitation Consortiu

Background This study aimed to investigate the relationship between body mass index ( BMI ) and sufficient chest compression depth (CCD) in obese patients by a mathematical model. Methods and Results This retrospective analysis was performed with chest computed tomography images conducted between 2006 and 2018. We classified the selected individuals into underweight (<18.5), normal weight (≥18.5, <25), overweight (≥25, <30), and obese (≥30) groups according to BMI (kg/m 2 ). We defined

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase th