Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

Objectives: To investigate whether machine learning (ML)-based center of pressure (COP) analysis for gait assessment, when used in conjunction with clinical information, offers additive benefits in predicting functional outcomes in patients with acute ischemic stroke.

Design: A prospective, single-center cohort study.

Setting: A tertiary hospital setting.

Longitudinal Changes in Maximal Forced Inspiratory Flow and Clinical Outcomes in Patients With COPD.

Background: COPD primarily impairs expiratory flow due to progressive airflow obstruction and reduced lung elasticity. Increasing evidence underlines the importance of inspiratory flow as a biomarker for selecting inhaler devices and providing ancillary aerodynamic information.

Research Question: Do the longitudinal changes in maximum forced inspiratory flow (FIFmax) influence acute exacerbations and lung function decline in patients with COPD?

Study Design And Methods: This longitudinal study evaluated FIFmax in patients with COPD over a 7-year period from 2004 to 2020.

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing electronic medical records and chest CT scans of 778 ICU patients with severe community-acquired pneumonia from January 2016 to December 2021, AI-enhanced 3D segmentation was used to assess thoracic muscle mass.

Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI.

Materials And Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.

Background: Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients.

Methods: Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization.

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring.

Prediction of Hearing Prognosis After Intact Canal Wall Mastoidectomy With Tympanoplasty Using Artificial Intelligence.

Objective: To evaluate the performance of a machine learning model and the effects of major prognostic factors on hearing outcomes following intact canal wall (ICW) mastoidectomy with tympanoplasty.

Study Design: Retrospective cross-sectional study.

Setting: Tertiary hospital.

Machine learning-based prediction of in-ICU mortality in pneumonia patients.

Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021.

Prediction of Mortality Among Patients With Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study.

Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries.

Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos.

According to the Korea Institute for Health and Social Affairs, in 2017, the elderly, aged 65 or older, had an average of 2.7 chronic diseases per person. The concern for the medical welfare of the elderly is increasing due to a low birth rate, an aging population, and the lack of medical personnel.

Prediction of hearing recovery in unilateral sudden sensorineural hearing loss using artificial intelligence.

Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.

Real-world oral anticoagulants for Asian patients with non-valvular atrial fibrillation: A PRISMA-compliant article.

Background And Purpose: This study aimed to evaluate the comparative efficacy and safety of 4 non-vitamin K antagonist oral anticoagulants (NOACs) and warfarin in Asians with non-valvular atrial fibrillation in real-world practice through a network meta-analysis of observational studies.

Methods: We searched multiple comprehensive databases (PubMed, Embase, and Cochrane library) for studies published until August 2020. Hazard ratios and 95% confidence intervals were used for the pooled estimates.

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning.

Objectives: While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therapy in acute stroke patients with and without active cancer.

Materials And Methods: A comprehensive literature search was conducted for studies comparing the effects of intravenous thrombolysis (IVT) or endovascular treatment (EVT) in ischemic stroke patients with and without active cancer.

Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal.

A Novel Method for Gait Analysis on Center of Pressure Excursion Based on a Pressure-Sensitive Mat.

Center of pressure (COP) during gait is a useful measure for assessing gait ability and has been investigated using platform or insole systems. However, these systems have inherent restrictions in repeated measure design or in obtaining true vertical force. This study proposes a novel method based on a pressure-sensitive mat system for COP measurement and presents normal reference values for the system.

Effects of therapeutic exercise on sea sand on pain, fatigue, and balance in patients with chronic ankle instability: a feasibility study.

Background: Chronic ankle instability (CAI) is a common disease following ankle sprain and appears balance and gait problems, pain, and fatigue. This study aimed to examine the effect of therapeutic exercise performed on sea sand on pain, fatigue, and balance ability in patients with CAI.

Methods: This study was designed as a randomized controlled trial.

The effects of spinal support device on pain and extensibility of the hamstrings in patients with non-specific low back pain.

[Purpose] The objective of this study was to investigate the effects of spinal support device (SSD) on pain and hamstring extensibility in patients with non-specific low back pain (NSLBP). [Subjects and Methods] 20 patients with NSLBP were recruited and randomly assigned to either the SSD group or the control group. In the SSD group, SSD was applied; in the control group, bed rest in supine position was performed.