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Article: Response to letter to the editors "Re: Byung-Do Lee, Wan Lee, Kyung-Hwan Kwon, Moon-Ki Choi, Eun-Joo Choi and Jung-Hoon Yoon. Glandular odontogenic cyst mimicking ameloblastoma in a 78-year-old female: a case report. Imaging Science in Dentistry 2014; 44(3): 249-52.".

Lee, Byung-Do

2015 Volume 45, Issue 2, Page(s) 139–140

Language	English
Publishing date	2015-06-19
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2631801-5
ISSN	2233-7830 ; 2233-7822
ISSN (online)	2233-7830
ISSN	2233-7822
DOI	10.5624/isd.2015.45.2.139
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The mediating role of flow in the relationship between simulation design and simulation educational satisfaction in korean nursing students: a cross-sectional study.

Lee, Eun-Kyung / Ji, Eun-Joo

BMC nursing

2024 Volume 23, Issue 1, Page(s) 279

Abstract: Background: In Korea, there has been recent interest in nursing simulation education. In nursing, simulation education has many advantages, such as improving nursing students' problem-solving and judgment skills. Simulation education satisfaction is an ... ...

Abstract	Background: In Korea, there has been recent interest in nursing simulation education. In nursing, simulation education has many advantages, such as improving nursing students' problem-solving and judgment skills. Simulation education satisfaction is an indicator for evaluating educational performance from the learners' perspective and an important criterion for the development and progress of nursing education. Therefore, based on NLN/Jeffries simulation theory, this study aims to identify the relationship between simulation design and educational satisfaction and to confirm the mediating effect of flow. Methods: This cross-sectional study was conducted using 143 fourth-year nursing students who had participated in classes using simulations at three universities in Seoul, Daegu, and Jeonbuk. Data were collected from April 24 to May 3, 2023. Demographic data, simulation design scale (SDS), flow in simulation, and the educational satisfaction scale in simulation were collected via an online questionnaire. The collected data were analyzed through t-test, ANOVA, Scheffé test, and Pearson's correlation coefficient using SPSS 25.0. The mediating effect of flow was analyzed through the three-stage mediation effect procedure using hierarchical regression analysis and the Sobel test. Results: The simulation educational satisfaction had a statistically significant positive correlation with simulation design (r = .65, p < .001) and flow (r = .47, p < .001), and simulation design was positively correlated with the flow (r = .55, p < .001). The simulation design had a statistically significant effect on flow, which was the mediating variable (β = 0.55, p < .001). Additionally, simulation design had a statistically significant effect on simulation educational satisfaction (β = 0.56, p < .001). The significance of the mediating effect of flow on the relationship between simulation design and simulation educational satisfaction was investigated using the Sobel test, and the mediating effect of flow was found to be statistically significant (Z = 5.36, p < .001). Conclusion: The significance of the current study lies in its confirmation of the link between simulation design and simulation educational satisfaction, as well as the mediating function of flow. Nursing students can achieve simulation educational satisfaction through simulation-based education if simulation educators follow best practices that improve flow through well-organized simulation design.
Language	English
Publishing date	2024-04-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2091496-9
ISSN	1472-6955
ISSN	1472-6955
DOI	10.1186/s12912-024-01946-5
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Article ; Online: Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography

Si Eun Lee / Hanpyo Hong / Eun-Kyung Kim

European Journal of Radiology Open, Vol 12, Iss , Pp 100545- (2024)

2024

Abstract: Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This ... ...

Abstract	Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.
Keywords	Breast cancer ; Digital mammography ; Diagnosis ; Computer-assisted ; Artificial intelligence ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Subject code	610
Language	English
Publishing date	2024-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Erythrobacter oryzae

Lee, Hyo-Jin / Hwang, Ji-Soo / Lee, Eun-Kyung / Whang, Kyung-Sook

International journal of systematic and evolutionary microbiology

2024 Volume 74, Issue 3

Abstract: Two novel bacterial strains, designated as COR- ... ...

Abstract	Two novel bacterial strains, designated as COR-2
MeSH term(s)	Sphingomonadaceae ; Oryza ; Phylogeny ; RNA, Ribosomal, 16S/genetics ; Base Composition ; Fatty Acids/chemistry ; Sequence Analysis, DNA ; DNA, Bacterial/genetics ; Bacterial Typing Techniques
Chemical Substances	RNA, Ribosomal, 16S ; Fatty Acids ; DNA, Bacterial
Language	English
Publishing date	2024-03-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2002336-4
ISSN	1466-5034 ; 1466-5026
ISSN (online)	1466-5034
ISSN	1466-5026
DOI	10.1099/ijsem.0.006287
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Article: Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.

Lee, Si Eun / Hong, Hanpyo / Kim, Eun-Kyung

European journal of radiology open

2024 Volume 12, Page(s) 100545

Abstract	Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.
Language	English
Publishing date	2024-01-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2810314-2
ISSN	2352-0477
ISSN	2352-0477
DOI	10.1016/j.ejro.2023.100545
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Article ; Online: Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.

Lee, Si Eun / Hong, Hanpyo / Kim, Eun-Kyung

Korean journal of radiology

2024 Volume 25, Issue 4, Page(s) 343–350

Abstract: Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores ... ...

Abstract	Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.
MeSH term(s)	Female ; Humans ; Adult ; Middle Aged ; Mammography/methods ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Predictive Value of Tests ; Retrospective Studies ; Artificial Intelligence ; Radiographic Image Interpretation, Computer-Assisted/methods ; Early Detection of Cancer ; Computers
Language	English
Publishing date	2024-03-25
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2046981-0
ISSN	2005-8330 ; 1229-6929
ISSN (online)	2005-8330
ISSN	1229-6929
DOI	10.3348/kjr.2023.0907
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Zs.A 5591: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Park, Eun Kyung / Kwak, SooYoung / Lee, Weonsuk / Choi, Joon Suk / Kooi, Thijs / Kim, Eun-Kyung

Radiology. Artificial intelligence

2024 , Page(s) e230318

Abstract: Just Accepted" papers have undergone full peer review and have been accepted for publication ... ...

Abstract	"Just Accepted" papers have undergone full peer review and have been accepted for publication in
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Journal Article
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.230318
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Article ; Online: BayMDS: An R Package for Bayesian Multidimensional Scaling and Choice of Dimension.

Oh, Man-Suk / Lee, Eun-Kyung

Applied psychological measurement

2022 Volume 46, Issue 3, Page(s) 250–251

Language	English
Publishing date	2022-03-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2002941-X
ISSN	1552-3497 ; 0146-6216
ISSN (online)	1552-3497
ISSN	0146-6216
DOI	10.1177/01466216221084219
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Article ; Online: Factors Influencing the Educational Needs and Nursing Intention Regarding COVID-19 Patient Care among Undergraduate Nursing Students.

Ji, Eun-Joo / Lee, Eun-Kyung

International journal of environmental research and public health

2022 Volume 19, Issue 23

Abstract: Purpose: This study examines final-year undergraduate nursing students to determine the educational needs for Coronavirus disease 2019 (COVID-19), knowledge of COVID-19, attitude toward COVID-19 patient care, and nursing intention toward COVID-19 ... ...

Abstract	Purpose: This study examines final-year undergraduate nursing students to determine the educational needs for Coronavirus disease 2019 (COVID-19), knowledge of COVID-19, attitude toward COVID-19 patient care, and nursing intention toward COVID-19 patients. Methods: A structured questionnaire was used to collect data from 21 April to 6 May 2022. The participants included 144 final-year (4th year) undergraduate nursing students in Gangwon-do, Daegu-si, and Chungcheong-do. The SPSS/WIN 21.0 program was used to analyze the data; Pearson's correlation coefficients and multiple regression were further performed. Results: The attitude toward COVID-19 patient care (β = 0.38, Conclusions: To foster undergraduate nursing students' nursing intention toward patients with emerging infectious diseases (EIDs), a program focused on cultivating a positive attitude toward EID patient care should be developed and implemented. The curriculum should further include education on EID patient care.
MeSH term(s)	Humans ; Students, Nursing ; Education, Nursing, Baccalaureate ; COVID-19/epidemiology ; Intention ; Surveys and Questionnaires ; Patient Care
Language	English
Publishing date	2022-11-25
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph192315671
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Article ; Online: 3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net.

Oh, Kangrok / Lee, Si Eun / Kim, Eun-Kyung

Scientific reports

2023 Volume 13, Issue 1, Page(s) 22625

Abstract: Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for ... ...

Abstract	Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best.
MeSH term(s)	Female ; Humans ; Breast Neoplasms/diagnostic imaging ; Mammography/methods ; Breast Density ; Breast/diagnostic imaging ; Ultrasonography, Mammary/methods ; Neural Networks, Computer ; Early Detection of Cancer/methods
Language	English
Publishing date	2023-12-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-49794-8
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