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Radiomics and Deep Learning: Hepatic Applications

Park HJ, Park B, Lee SS

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging...
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Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives

Bang CS

Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution...
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Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Hong N, Park H, Rhee Y

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and...
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Effects of Reading a Free Electronic Book on Regional Anatomy with Schematics and Mnemonics on Student Learning

Chung BS, Koh KS, Oh CS, Park JS, Lee JH, Chung MS

BACKGROUND: To help medical students learn anatomy effectively in limited hours, a regional anatomy book enhancing students' memorization was developed. METHODS: Only anatomical terms essential for basic cadaver dissection are included...
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Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images

Kim YJ, Ganbold B, Kim KG

OBJECTIVES: Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is...
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Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

Symum H, Zayas-Castro JL

OBJECTIVES: The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five...
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Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department

Yu JY, Jeong GY, Jeong OS, Chang DK, Cha WC

OBJECTIVES: The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome. METHODS: The retrospective study included...
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Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography

Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, Huh J, Lee TY, Park T, Lee J, Kim KW

OBJECTIVE: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. MATERIALS AND METHODS: A fully...
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Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Do S, Song KD, Chung JW

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years....
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Feasibility of fully automated classification of whole slide images based on deep learning

Cho KO, Lee SH, Jang HJ

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners...
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Machine Learning: a New Opportunity for Risk Prediction

Kwon O, Na W, Kim YH

No abstract available.
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Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes

Cho IJ, Sung JM, Kim HC, Lee SE, Chae MH, Kavousi M, Rueda-Ochoa OL, Ikram MA, Franco OH, Min JK, Chang HJ

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD),...
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The Satisfaction of Team-Based Learning on Discussion in the Training Course of Emergency Medical Technician

Hwang HJ, Ko SH, Kwon OY

BACKGROUND: Team-based learning is known for its effective and satisfying education methods in the study of various medical schools. This study was prepared to confirm the satisfaction of applying this...
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Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

Khan O, Badhiwala , Wilson JR, Jiang F, Martin AR, Fehlings M

Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over...
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Deep Learning in Medical Imaging

Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the...
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Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

Staartjes , Stienen MN

No abstract available.
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Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Schwartz J, Gao M, Geng EA, Mody KS, Mikhail CM, Cho SK

Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly,...
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Possibility of predicting missing teeth using deep learning: a pilot study

Kim SJ, Rim D, Heo JU, Cho HJ

OBJECTIVES: The primary objective of this study was to determine if the number of missing teeth could be predicted by oral disease pathogens, and the secondary objective was to assess...
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Learning Curve for Robot-Assisted Percutaneous Pedicle Screw Placement in Thoracolumbar Surgery

Kam JK, Gan C, Dimou S, Awad M, Kavar B, Nair G, Morokoff A

STUDY DESIGN: Retrospective review of an initial cohort of consecutive patients undergoing robot-assisted pedicle screw placement. PURPOSE: We aimed to evaluate the learning curve, if any, of this new technology over...
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The Effects of Jigsaw Cooperation Learning on Communication Ability, Problem Solving Ability, Critical Thinking Disposition, Self-directed Learning Ability and Cooperation of Nursing Students

Kim MG, Kim HW

PURPOSE: This study was conducted to examine the effects of jigsaw cooperative learning on the communication ability, problem solving ability, critical thinking disposition, self-directed learning ability and cooperation of nursing...
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