Skip Navigation
Skip to contents
Results by Year

View Wide

Filter

ARTICLE TYPE

more+
SELECT FILTER
 
Close

PUBLICATION DATE

176 results
Display

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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI

Park HJ, Yoon JS, Lee SS, Suk HI, Park B, Sung YS, Hong SB, Ryu H

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Machine Learning-Based Predictor for Treatment Outcomes of Patients With Salivary Gland Cancer After Operation

Jeong MC, Koh YW, Choi EC, Lim JY, Kim SH, Park YM

Background and Objectives The purpose of this study was to analyze the survival data of salivary gland cancer (SGCs) patients to construct machine learning and deep learning models that can...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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,...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
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...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives

Park JE

The artificial intelligence (AI) techniques, both deep learning end-to-end approaches and radiomics with machine learning, have been developed for various imaging-based tasks in neuro-oncology. In this brief review, use cases...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction

Park J, Shin J, Min IK, Bae H, Kim YE, Chung YE

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence

Choo MS, Ryu HY, Lee S

Purpose: To develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI). Methods: A prospectively collected 1,574 UFM results (1,031 males, 543...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Deep learning approach for classification of chondrocytes in rats

Kang JS

The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, there-by relieving of some burden...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs

Choi JW, Choi JW, Cho YJ, Ha JY, Lee YY, Koh SY, Seo JY, Choi YH, Cheon JE, Phi JH, Kim I, Yang J, Kim WS

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Ha SM, Kim HH, Kang E, Seo BK, Choi N, Kim TH, Ku YJ, Ye JC

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close
Classification of Mouse Lung Metastatic Tumor with Deep Learning

Lee HN, Seo HD, Kim EM, Han BS, Kang JS

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience;...
CITED
export Copy
Close
SHARE
Twitter Facebook
Close

Go to Top

Copyright © 2022 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr