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Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty

Park JH, Park I, Han K, Yoon J, Sim Y, Kim SJ, Won JY, Lee S, Kwon JH, Moon S, Kim GM, Kim Md

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty...
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Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging

Cho Y, Cho H, Shim J, Choi JI, Kim YH, Kim N, Oh YW, Hwang SH

Background: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). Methods: An active learning framework was...
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Exploring medical students’ perception of non-face-to-face theory and face-to-face laboratory classes during COVID-19 pandemic: focusing on anatomy course

Park HJ, Woo RS, Song DY, Yoo HI

Purpose: This study investigated students’ perceptions of non-face-to-face theory classes and face-to-face laboratory classes conducted in anatomy courses at medical schools during the coronavirus disease 2019 pandemic. Methods: This study utilized...
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Exploration of Potential Gut Microbiota-Derived Biomarkers to Predict the Success of Fecal Microbiota Transplantation in Ulcerative Colitis: A Prospective Cohort in Korea

Kang GU, Park S, Jung Y, Jee JJ, Kim MS, Lee S, Lee DW, Shin JH, Koh H

Background/Aims: Although fecal microbiota transplantation (FMT) has been proven as one of the promising treatments for patients with ulcerative colitis (UC), potential prognostic markers regarding the clinical outcomes of FMT...
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Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy

Yang CB, Kim SH, Lim YJ

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is...
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Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia

Marchi M, Galli G, Fiore G, Mackinnon A, Mattei G, Starace F, Galeazzi GM

Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms. Methods: In a cross-sectional design, a sample of community mental health service users (SUs;...
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Machine Learning on Early Diagnosis of Depression

Lee KS, Ham BJ

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science....
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Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm

Kong SH, Lee JW, Bae BU, Sung JK, Jung KH, Kim JH, Shin CS

Background: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. Methods: This study included 1,595...
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Unsupervised Machine Learning to Identify Depressive Subtypes

Kung B, Chiang M, Perera G, Pritchard M, Stewart R

Objectives: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. Methods: Data from 18,314 depressed patients...
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Development of a Frailty Detection Model Using Machine Learning with the Korean Frailty and Aging Cohort Study Data

Koo D, Lee AR, Lee E, Kim IK

Objectives: This paper aimed to use machine learning to identify a new group of factors predicting frailty in the elderly population by utilizing the existing frailty criteria as a basis,...
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Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

Amruthalingam L, Buerzle O, Gottfrois P, Jimenez AG, Roth A, Koller T, Pouly M, Navarini AA

Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules...
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Machine Learning Smart System for Parkinson Disease Classification Using the Voice as a Biomarker

Tougui I, Jilbab A, Mhamdi JE

Objectives: This study presents PD Predict, a machine learning system for Parkinson disease classification using voice as a biomarker. Methods: We first created an original set of recordings from the...
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Development and evaluation of a pediatric nursing competency-building program for nursing students in South Korea: a quasi-experimental study

Koo HY, Lee BR

Purpose: The present study aimed to develop and examine the effectiveness of a pediatric nursing competency-building program for nursing students. Methods: This was a quasi-experimental study with a nonequivalent control group...
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Ethics for Artificial Intelligence: Focus on the Use of Radiology Images

Park SH

The importance of ethics in research and the use of artificial intelligence (AI) is increasingly recognized not only in the field of healthcare but throughout society. This article intends to...
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Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Park TJ, Chang HJ, Choi BJ, Jung JA, Kang S, Yoon S, Kim M, Yoon D

Purpose: Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability....
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Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method

Hwang J, Yoon HM, Hwang JY, Kim PH, Bak B, Bae BU, Sung J, Kim HJ, Jung AY, Cho YA, Lee JS

Purpose: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods. Materials and Methods: We collected 485 hand...
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Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

Han JH, Yoon SJ, Lee HS, Park G, Lim J, Shin JE, Eun HS, Park MS, Lee SM

Purpose: The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants. Materials...
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Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study

Lee JH, Ha EJ, Lee DH, Han M, Park JH, Kim Jh

Objective: Preoperative differential diagnosis of follicular-patterned lesions is challenging. This multicenter cohort study investigated the clinicoradiological characteristics relevant to the differential diagnosis of such lesions. Materials and Methods: From June to...
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Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT

Son W, Kim M, Hwang JY, Kim YW, Park C, Choo KS, Kim TU, Jang JY

Objective: To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms. Materials and Methods: Post-contrast abdominopelvic CT...
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Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio

Song SH, Han JH, Kim KS, Cho YA, Youn HJ, Kim YI, Kweon J

Purpose: We investigated the feasibility of measuring the hydronephrosis area to renal parenchyma (HARP) ratio from ultrasound images using a deep-learning network. Materials and Methods: The coronal renal ultrasound images of...
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