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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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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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Decision-Making in Artificial Intelligence: Is It Always Correct?

Kim HS

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 Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System

Chung KJ, Kim J, Whangbo TK, Kim KH

In upcoming fourth industrial revolution era, it is inevitable to address smart healthcare as not only scientist but also clinician. We have the task to plan and realize this through...
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Epigenetics and Depression: An Update

Lin E, Tsai SJ

OBJECTIVE: Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics...
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Application of Artificial Intelligence in Lung Cancer Screening

Lee SM, Park CM

Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However,...
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Artificial intelligence in drug development: clinical pharmacologist perspective

Jang IJ

No abstract available.
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Current status and future direction of digital health in Korea

Shin SY

Recently, digital health has gained the attention of physicians, patients, and healthcare industries. Digital health, a broad umbrella term, can be defined as an emerging health area that uses brand...
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Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography

Nam KH, Seo I, Kim DH, Lee JI, Choi BK, Han IH

OBJECTIVE: Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides...
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Overview of Deep Learning in Gastrointestinal Endoscopy

Min JK, Kwak MS, Cha JM

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based...
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AI in Medicine: Need of Orchestration for High-Performance

Choi J

No abstract available.
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Application of machine learning in rheumatic disease research

Kim KJ, Tagkopoulos I

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing,...
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Can artificial Intelligence Prediction Algorithms Exceed Statistical Predictions?

Park J

No abstract available.
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Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study...
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Future Sleep Medicine: Mobile Health and Big Data

Kim J, Cho JW

Sleep is well known to be important to health and well-being, creativity, memory consolidation, and cognitive functions. However, sleep disorder patients sometimes had some limitation to get proper diagnosis and...
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Medical Image Analysis Using Artificial Intelligence

Yoon HJ, Jeong YJ, Kang H, Jeong JE, Kang DY

PURPOSE: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data....
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