Vibrotactile stimulation at 40 Hz inhibits Aβ-induced changes in SH-SY5Y, BV2 cells, and pericytes.

Alzheimer's disease (AD) poses a major societal challenge, yet no definitive cure exists. Noninvasive brain stimulation methods, such as transcranial magnetic stimulation and transcranial direct current stimulation, have shown promise in alleviating cognitive symptoms associated with neurodegenerative disorders. This study investigated the effects of 40 Hz vibrotactile stimulation on AD-related cellular responses using SH-SY5Y neuroblastoma cells, primary human brain pericytes, and BV2 microglia.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

Introduction: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images.

Design and validation of a wearable dynamometry system for knee extension-flexion torque measurement.

Muscle strength assessments are vital in rehabilitation, orthopedics, and sports medicine. However, current methods used in clinical settings, such as manual muscle testing and hand-held dynamometers, often lack reliability, and isokinetic dynamometers (IKD), while reliable, are not easily portable. The aim of this study was to design and validate a wearable dynamometry system with high accessibility, accuracy, and reliability, and to validate the device.

Sonic vibration ameliorates inflammatory diseases via the up-regulation of IL-10.

Sonic vibration (SV), or vibroacoustic therapy, is applied to enhance local and systemic blood circulation and alleviate pain using low-frequency sine wave vibrations. However, there is limited scientific data on the mechanisms through which the benefits are achieved. In this study, we investigated the impact of SV on inflammatory responses by assessing cytokine secretion in both and models.

A multimodal virtual vision platform as a next-generation vision system for a surgical robot.

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system.

Automated deep learning model for estimating intraoperative blood loss using gauze images.

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning.

A portable articulated dynamometer for ankle dorsiflexion and plantar flexion strength measurement: a design, validation, and user experience study.

Monitoring ankle strength is crucial for assessing daily activities, functional ability, and preventing lower extremity injuries. However, the current methods for measuring ankle strength are often unreliable or not easily portable to be used in clinical settings. Therefore, this study proposes a portable dynamometer with high reliability capable of measuring ankle dorsiflexion and plantar flexion.

Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope.

Objective: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images.

Measurement of level of consciousness by AVPU scale assessment system based on automated video and speech recognition technology.

Objective: To develop an alert/verbal/painful/unresponsive (AVPU) scale assessment system based on automated video and speech recognition technology (AVPU-AVSR) that can automatically assess a patient's level of consciousness and evaluate its performance through clinical simulation.

Methods: We developed an AVPU-AVSR system with a whole-body camera, face camera, and microphone. The AVPU-AVSR system automatically extracted essential audiovisual features to assess the AVPU score from the recorded video files.

Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly.

Background: Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment.

Objective: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics.

Development and validation of a portable articulated dynamometry system to assess knee extensor muscle strength.

Muscle strength assessment is important in predicting clinical and functional outcomes in many disorders. Manual muscle testing, although commonly used, offers suboptimal accuracy and reliability. Isokinetic dynamometers (IKDs) have excellent accuracy and reliability; but are bulky and expensive, offering limited accessibility.

Highly Sensitive and Selective Gas Sensors Based on Metal Iodates: Material Characterization and Sensor Performance Evaluation.

This study proposes the possibility of employing metal iodates as novel gas-sensing materials synthesized using a facile chemical precipitation method. An extensive survey of a library of metal iodates reveals that cobalt, nickel, and copper iodates are useful for gas sensor applications. Material analysis conducted using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, thermal gravity differential temperature analysis, and Raman spectroscopy enables us to understand the thermal behavior and optimize post-annealing conditions.

Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test.

Background: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-center visual Turing test.

Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul.

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates.

Review of Smart Hospital Services in Real Healthcare Environments.

Objective: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type.

Methods: A literature review was conducted regarding the background and technical characteristics of smart hospitals.

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement.

Background: Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful.

Ultra-Thin ReS Nanosheets Grown on Carbon Black for Advanced Lithium-Ion Battery Anodes.

ReS nanosheets are grown on the surface of carbon black (CB) via an efficient hydrothermal method. We confirmed the ultra-thin ReS nanosheets with ≈1-4 layers on the surface of the CB (ReS@CB) by using analytical techniques of field emission scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HRTEM). The ReS@CB nanocomposite showed high specific capacities of 760, 667, 600, 525, and 473 mAh/g at the current densities of 0.

A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information.

Purpose: The colonoscopy adenoma detection rate depends largely on physician experience and skill, and overlooked colorectal adenomas could develop into cancer. This study assessed a system that detects polyps and summarizes meaningful information from colonoscopy videos.

Methods: One hundred thirteen consecutive patients had colonoscopy videos prospectively recorded at the Seoul National University Hospital.

A Pilot Study on the Evaluation of Physicians' Laser Delivery Performance Using a Laser Beam Detection Kit.

Background And Objective: Many laser devices have been developed over the past decades for various skin conditions. However, variations in the technical skill of physicians for laser skin treatment delivery have not yet been evaluated. This study evaluates the differences in omission and overlap percentages during simulated laser hair removal treatments among physicians at two clinics.

Development of a Novel Automated Hair Counting System for the Quantitative Evaluation of Laser Hair Removal.

Objective: We aimed to develop and validate a novel computer-assisted automated hair counting system for the quantitative evaluation of laser hair removal (LHR).

Methods: We developed a computer-aided image processing system to count hairs on shaved skin and validated its performance through clinical trials. Five volunteers of Fitzpatrick skin type III-IV volunteered and were tested on both thighs.

Improvement in Laser-Irradiation Efficiency of Robot-Assisted Laser Hair Removal Through Pose Measurement of Skin Surface.

Objective: This study aims to improve the performance of an automatic laser hair removal (LHR) system by applying an algorithm that considers the curve and slant of the skin surface.

Background Data: In an earlier research, a robot-assisted LHR system has been developed and validated for an almost flat skin or a relatively smooth curved part of the skin. For practical clinical applications, the feature of the robot-assisted LHR system is extended for real curved skins.