Joo-eun Choi

Lateral cephalometry has been widely used for skeletal classification in orthodontic diagnosis and treatment planning. However, this conventional system, requiring manual tracing of individual landmarks, contains possible errors of inter- and intravariability and is highly time-consuming. This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a 1-step, end-to-end diagnostic system with lateral cephalograms. A multim

This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the max

BACKGROUND: Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation. OBJECTIVE: The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical fea

This paper presents a novel class of self-organizing multi-agent systems that form a swarm and learn a spatio- temporal process through noisy measurements from neighbors for various global goals. The physical spatio-temporal process of interest is modeled by a spatio-temporal Gaussian process. Each agent maintains its own posterior predictive statistics of the Gaussian process based on measurements from neighbors. A set of biologically inspired navigation strategies are identified from the poste

Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to compu

Autophagy is known to be regulated by the phosphoinositide-3 kinase (PI3K)-protein kinase B (AKT) and/or mitogen-activated protein kinase 1/2 (MEK1/2)-extracellular signal-regulated kinase 1/2 (ERK1/2) pathways, leading to activation of mammalian target of rapamycin (mTOR), a major negative regulator of autophagy. However, some reports have also suggested that autophagic regulation by the PI3K-AKT and/or MEK1/2-ERK1/2 pathways may not be mediated by mTOR activity, and there is no direct evidence

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in