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Jungheum Cho

Seoul National University · Medicine

About the Lab

Professor Jungheum Cho's research lab specializes in medical artificial intelligence and radiology, focusing on the development and validation of deep learning algorithms for medical image diagnosis. The lab investigates diagnostic accuracy, particularly in detecting conditions such as sinusitis, osteonecrosis, salivary gland malignancies, and brain metastases using imaging modalities like X-ray, CT, and MRI. A central theme is the comparison of AI performance with radiologists to enhance diagnostic efficiency and reliability across diverse clinical settings.

medical AIdeep learningradiologydiagnostic accuracymedical imaging

Research Overview

Papers
40
Total Citations
572
Papers (5y)
18
Primary Field
Medicine

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
18total
2022
2023
2024
2025
2026
Citations per year (5y)
60total
20222023202420252026

Selected Papers

15
1
Article|108 citations·2018
Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography
Youngjune Kim, Kyong Joon Lee, Leonard Sunwoo, Dongjun Choi, Chang-Mo Nam, Jungheum Cho, Jihyun Kim, Yun Jung Bae, Roh‐Eul Yoo, Byung Se Choi, Cheolkyu Jung, Jae Hyoung Kim
SJR Q1Investigative Radiology

OBJECTIVES: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs. MATERIALS AND METHODS: Among 80,475 Waters' view radiographs, examined between May 2003 and February 2017, 9000 randomly selected cases were classified as normal or maxillary sinusitis based on radiographic findings and divided into training (n = 8000) and validation (n = 1000) sets to develop a deep lear

OtorhinolaryngologyMedicine
2
Review|82 citations·2012
Comparison of emergency medical services systems in the pan‐Asian resuscitation outcomes study countries: Report from a literature review and survey
Marcus Eng Hock Ong, Jungheum Cho, Matthew Huei‐Ming, Hideharu Tanaka, Tatsuya Nishiuchi, Omer Al Sakaf, Sarah Abdul Karim, Nalinas Khunkhlai, Rıdvan Atilla, Chih‐Hao Lin, Nur Shahidah, Désirée Lie
SJR Q1Emergency Medicine Australasia

OBJECTIVE: Asia-Pacific countries have unique prehospital emergency care or emergency medical services (EMS) systems, which are different from European or Anglo-American models. We aimed to compare the EMS systems of eight Asia-Pacific countries/regions as part of the Pan Asian Resuscitation Outcomes Study (PAROS), to provide a basis for future comparative studies across systems of care. METHODS: In the first phase, a systematic literature review of EMS system within the eight PAROS countries/re

Emergency MedicineMedicine
3
Review|51 citations·2020
Comparison of core needle biopsy and fine‐needle aspiration in diagnosis of ma lignant salivary gland neoplasm: Systematic review and meta‐analysis
Jungheum Cho, Junghoon Kim, Ji Sung Lee, Choong Guen Chee, Youngjune Kim, Sang Il Choi
SJR Q1Head & Neck

BACKGROUND: In this meta-analysis, we compared the risk of obtaining nondiagnostic results and the diagnostic accuracy for detection of salivary gland malignancy between core needle biopsy (CNB) and fine-needle aspiration (FNA). METHODS: All published English-language studies comparing CNB and FNA diagnostic accuracy for salivary gland masses through December 2019 were searched. Pooled risk ratios (RRs) of nondiagnostic results, sensitivities, and specificities of CNB and FNA for salivary gland

SurgeryMedicine
4
Article|50 citations·2019
Performance of a Deep Learning Algorithm in Detecting Osteonecrosis of the Femoral Head on Digital Radiography: A Comparison With Assessments by Radiologists
Choong Guen Chee, Youngjune Kim, Yusuhn Kang, Kyong Joon Lee, Hee‐Dong Chae, Jungheum Cho, Chang-Mo Nam, Dongjun Choi, Eugene Lee, Joon Woo Lee, Sung Hwan Hong, Joong Mo Ahn
SJR Q1American Journal of Roentgenology

<b>OBJECTIVE.</b> The objective of our study was to compare the sensitivity of a deep learning (DL) algorithm with the assessments by radiologists in diagnosing osteonecrosis of the femoral head (ONFH) using digital radiography. <b>MATERIALS AND METHODS.</b> We performed a two-center, retrospective, noninferiority study of consecutive patients (≥ 16 years old) with a diagnosis of ONFH based on MR images. We investigated the following four datasets of unilaterally cropped hip anteroposterior radi

Orthopedics and Sports MedicineMedicine
5
Article|30 citations·2021
Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study
Hyunsu Choi, Won Chang, Jong Hyo Kim, Chulkyun Ahn, Heejin Lee, Hae Young Kim, Jungheum Cho, Yoon Jin Lee, Young Hoon Kim
SJR Q1European RadiologyOA
Radiology, Nuclear Medicine and ImagingMedicine
6
Review|28 citations·2021
Diagnostic accuracy and complication rate of image-guided percutaneous transthoracic needle lung biopsy for subsolid pulmonary nodules: a systematic review and meta-analysis
Junghoon Kim, Choong Guen Chee, Jungheum Cho, Youngjune Kim, Min A Yoon
SJR Q1British Journal of RadiologyOA

Objectives: To determine the diagnostic accuracy and complication rate of percutaneous transthoracic needle biopsy (PTNB) for subsolid pulmonary nodules and sources of heterogeneity among reported results. Methods: We searched PubMed, EMBASE, and Cochrane libraries (until November 7, 2020) for studies measuring the diagnostic accuracy of PTNB for subsolid pulmonary nodules. Pooled sensitivity and specificity of PTNB were calculated using a bivariate random-effects model. Bivariate meta-regressio

Pulmonary and Respiratory MedicineMedicine
7
Article|27 citations·2021
Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI
Jungheum Cho, Young Jae Kim, Leonard Sunwoo, Gi Pyo Lee, Toan Nguyen, Se Jin Cho, Sung Hyun Baik, Yun Jung Bae, Byung Se Choi, Cheolkyu Jung, Chul‐Ho Sohn, Jungho Han
SJR Q2Frontiers in OncologyOA

BACKGROUND: Although accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning. METHODS: We included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test da

Pulmonary and Respiratory MedicineMedicine
8
Article|26 citations·2020
Clinical validity of two different grading systems for lumbar central canal stenosis: Schizas and Lee classification systems
Yeon-jee Ko, Eugene Lee, Joon Woo Lee, Chi Young Park, Jungheum Cho, Yusuhn Kang, Joong Mo Ahn
SJR Q1PLoS ONEOA

Both Schizas and Lee MRI grading systems for LCCS are reliable grading systems, and can be used as a learnable method for both clinicians and radiologists.

Pathology and Forensic MedicineMedicine
9
Article|21 citations·2021
The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain
Se Woo Kim, Jung Hoon Kim, Suha Kwak, Minkyo Seo, Changhyun Ryoo, Cheong‐Il Shin, Siwon Jang, Jungheum Cho, Young-Hoon Kim, Kyutae Jeon
SJR Q1Scientific ReportsOA

Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorithm generating DL-SCE-CT using NECT with paired precontrast/postcontrast images. For clinical application, 353 patients from three institutions who visited the ED with AAP were included. Six reviewers (experienced radiologists, ER1-3; training radiolo

Radiology, Nuclear Medicine and ImagingMedicine
10
Article|20 citations·2019
Quantitative MRI evaluation of gastric motility in patients with Parkinson’s disease: Correlation of dyspeptic symptoms with volumetry and motility indices
Jungheum Cho, Yoon Jin Lee, Young Hoon Kim, Cheol Min Shin, Jong‐Min Kim, Won Ick Chang, Ji Hoon Park
SJR Q1PLoS ONEOA

Gastric motility can be quantitatively assessed by MRI, showing decreased GMI, delayed GE, and prolonged T1/2 in PD patients with early satiety or dyspepsia.

NeurologyMedicine
11
Article|17 citations·2020
Biparametric versus multiparametric magnetic resonance imaging of the prostate: detection of clinically significant cancer in a perfect match group
Jungheum Cho, Hyungwoo Ahn, Sung Il Hwang, Hak Jong Lee, Gheeyoung Choe, Seok‐Soo Byun, Sung Kyu Hong
SJR Q1Prostate InternationalOA

Diagnostic performance of bpMRI without dynamic contrast enhancement MRI is not significantly different from mpMRI with dynamic contrast enhancement MRI in the detection of csPCa.

Pulmonary and Respiratory MedicineMedicine
12
Article|13 citations·2018
Diagnostic accuracy of digital radiography for the diagnosis of osteonecrosis of the femoral head, revisited
Choong Guen Chee, Jungheum Cho, Yusuhn Kang, Youngjune Kim, Eugene Lee, Joon Woo Lee, Joong Mo Ahn, Heung Sik Kang
SJR Q3Acta Radiologica

Background The radiographic diagnosis of osteonecrosis of the femoral head (ONFH) is challenging for radiologists. Purpose To measure the sensitivity and specificity of digital radiography for diagnosing ONFH and to evaluate the diagnostic value of the frog-leg view. Material and Methods Patients diagnosed with ONFH by magnetic resonance imaging (MRI) (n = 132) and normal controls (n = 69) were included. Two radiologists independently graded the likelihood of ONFH and subchondral fracture on rad

Orthopedics and Sports MedicineMedicine
13
Article|11 citations·2024
Risk of hematologic malignant neoplasms from head CT radiation in children and adolescents presenting with minor head trauma: a nationwide population-based cohort study
Seung‐Jae Lee, Hae Young Kim, Kyung Hee Lee, Jungheum Cho, Choonsik Lee, Kwang Pyo Kim, Jinhee Hwang, Ji Hoon Park
SJR Q1European Radiology
Radiology, Nuclear Medicine and ImagingMedicine
14
Article|11 citations·2021
Adrenal Nodules Detected at Staging CT in Patients with Resectable Gastric Cancers Have a Low Incidence of Malignancy
Hae Young Kim, Won Chang, Yoon Jin Lee, Ji Hoon Park, Jungheum Cho, Hee Young Na, Hyungwoo Ahn, Sung Il Hwang, Hak Jong Lee, Young Hoon Kim, Kyoung Ho Lee
SJR Q1Radiology

Background Guidelines recommending additional imaging for adrenal nodules lack relevant epidemiologic evidence. Purpose To measure the prevalence of adrenal nodules detected at staging CT in patients with potentially resectable gastric cancer and the proportion of patients with malignant nodules among them. Materials and Methods This retrospective study included 10 250 consecutive patients (median age, 63 years; interquartile range, 53-71 years; 6884 men) who underwent staging CT and had potenti

SurgeryMedicine
15
Article|11 citations·2020
Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers
Jungheum Cho, Jihang Kim, Jihang Kim, Kyong Joon Lee, Chang Mo Nam, Sung Hyun Yoon, Hwayoung Song, Junghoon Kim, Junghoon Kim, Ye Ra Choi, Kyung Hee Lee, Kyung Won Lee
SJR Q1Journal of Clinical MedicineOA

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting &amp; data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detecti

Pulmonary and Respiratory MedicineMedicine

Research Areas

Radiology, Nuclear Medicine and ImagingEmergency MedicinePulmonary and Respiratory MedicineSurgeryOncologyOrthopedics and Sports Medicine

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