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Recent developments in small bowel endoscopy: the “black box” is now open!

Alemanni LV, Fabbri S, Rondonotti E, Mussetto A

Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist’s toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn’s...
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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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Prediction of the composition of urinary stones using deep learning

Kim US, Kwon HS, Yang W, Lee W, Choi C, Kim JK, Lee SH, Rim D, Han JH

Purpose: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images. Materials and Methods: We classified 1,332 stones into 31 classes according to the stone...
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The usefulness of inner ear magnetic resonance imaging in patient with Ménière’s disease: A narrative review

Cho YS, Song B, Cho BH, Chung WH

Ménière’s disease (MD) is a multifactorial disorder with typical symptoms of recurrent vertigo, tinnitus, fluctuating hearing loss, and sensations of ear fullness. This disease greatly reduces the quality of life...
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A Machine-Learning-Based Risk Factor Analysis for Hypertension: Korea National Health and Nutrition Examination Survey 2016–2019

Oh T, Kim D, Won C, Kim S, Jeong E, Yang J, Yu J, Kim B, Lee J

Background: The purpose of this study was to use machine learning to identify risk factors (other than systolic and diastolic blood pressure) for hypertension. Methods: The study population comprised 23,170 adults...
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Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Nam KH, Kim DY, Kim DH, Lee JH, Lee JI, Kim MJ, Park JY, Hwang JH, Yun SS, Choi BK, Kim MG, Han IH

Objective: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance...
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Machine learning techniques for arrhythmic risk stratification: a review of the literature

 Chung C, Bazoukis G, Lee S, Liu Y,  Liu T, Letsas K,  Armoundas A, Tse G

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors....
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Artificial intelligence in perioperative medicine: a narrative review

Yoon HK, Yang HL, Jung CW, Lee HC

Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses,...
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Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network

Serindere G, Bilgili E, Yesil C, Ozveren N

Purpose This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods A CNN...
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Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Kim HS, Ha EG, Kim YH, Jeon KJ, Lee C, Han SS

Purpose This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods Periapical radiographs of implant fixtures obtained using the Superline...
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Recent trends of healthcare information and communication technologies in pediatrics: a systematic review

Jung Sy, Lee K, Hwang H

As information communication technology (ICT) has advanced, the healthcare industry has embraced it to reduce medical costs, improve health outcomes, and increase patient satisfaction. Healthcare ICT revolutionizes pediatric healthcare. This...
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Big data analysis and artificial intelligence in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting

Chung YG, Jeon Y, Yoo S, Kim H, Hwang H

There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses...
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Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status

Jeong SY, Suh CH, Park HY, Heo H, Shim WH, Kim SJ

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important...
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Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps? A meta-analysis

Sivananthan A, Nazarian S, Ayaru L, Patel K, Ashrafian H, Darzi A, Patel N

Background/Aims: Colonoscopy is the gold standard diagnostic method for colorectal neoplasia, allowing detection and resection of adenomatous polyps; however, significant proportions of adenomas are missed. Computer-aided detection (CADe) systems in...
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Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach

Siripoppohn V, Pittayanon R, Tiankanon K, Faknak N, Sanpavat A, Klaikaew N, Vateekul P, Rerknimitr R

Background/Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we...
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Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study

Lee JH, Kim KH, Lee EH, Ahn JS, Ryu JK, Park YM, Shin GW, Kim YJ, Choi HY

Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods: A commercial deep learning-based...
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Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry

Bang S, Tukhtaev S, Ko KJ, Han DH, Baek M, Jeon HG, Cho BH, Lee KS

Purpose: To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In...
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Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells

Lee D, Kim S

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their...
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Stakeholders’ Requirements for Artificial Intelligence for Healthcare in Korea

Yu JY, Hong S, Lee YC, Lee KH, Lee I, Seo Y, Kang M, Kim K, Cha WC, Shin SY

Objectives: The outlook of artificial intelligence for healthcare (AI4H) is promising. However, no studies have yet discussed the issues from the perspective of stakeholders in Korea. This research aimed to...
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Digital Pathology and Artificial Intelligence Applications in Pathology

Go H

Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological...
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