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  1. Article ; Online: Caveats in Using Abnormality/Probability Scores from Artificial Intelligence Algorithms: Neither True Probability nor Level of Trustworthiness.

    Park, Seong Ho / Hwang, Eui Jin

    Korean journal of radiology

    2024  Volume 25, Issue 4, Page(s) 328–330

    MeSH term(s) Humans ; Artificial Intelligence ; Algorithms ; Probability
    Language English
    Publishing date 2024-03-25
    Publishing country Korea (South)
    Document type Editorial
    ZDB-ID 2046981-0
    ISSN 2005-8330 ; 1229-6929
    ISSN (online) 2005-8330
    ISSN 1229-6929
    DOI 10.3348/kjr.2024.0144
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    Zs.A 5591: Show issues Location:
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  2. Article ; Online: Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality.

    Park, Hyungin / Hwang, Eui Jin / Goo, Jin Mo

    Investigative radiology

    2023  Volume 59, Issue 3, Page(s) 278–286

    Abstract: Objectives: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.: Materials and methods: This retrospective ... ...

    Abstract Objectives: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.
    Materials and methods: This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models.
    Results: The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status.
    Conclusions: The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.
    MeSH term(s) Male ; Humans ; Pulmonary Emphysema/diagnostic imaging ; Retrospective Studies ; Deep Learning ; Lung/diagnostic imaging ; Tomography, X-Ray Computed/methods ; Emphysema/diagnostic imaging
    Language English
    Publishing date 2023-07-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80345-5
    ISSN 1536-0210 ; 0020-9996
    ISSN (online) 1536-0210
    ISSN 0020-9996
    DOI 10.1097/RLI.0000000000001003
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    Zs.A 734: Show issues Location:
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  3. Article ; Online: Measurement Variability of Same-Day CT Quantification of Interstitial Lung Disease: A Multicenter Prospective Study.

    Lee, Jong Hyuk / Chae, Kum Ju / Park, Jimyung / Choi, Sun Mi / Jang, Myoung-Jin / Hwang, Eui Jin / Jin, Gong Yong / Goo, Jin Mo

    Radiology. Cardiothoracic imaging

    2024  Volume 6, Issue 2, Page(s) e230287

    Abstract: Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March ... ...

    Abstract Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes. Deep learning-based texture analysis software was used to segment ILD features. Fibrosis extent was defined as the sum of reticular opacity and honeycombing cysts. Measurement variability between scans was assessed with Bland-Altman analyses for absolute and relative differences with 95% limits of agreement (LOA). The contribution of fibrosis extent to variability was analyzed using a multivariable linear mixed-effects model while adjusting for lung volume. Eight readers assessed ILD fibrosis stability with and without QCT information for 30 randomly selected samples. Results Sixty-five participants were enrolled in this study (mean age, 68.7 years ± 10 [SD]; 47 [72%] men, 18 [28%] women). Between two same-day CT scans, the 95% LOA for the mean absolute and relative differences of quantitative fibrosis extent were -0.9% to 1.0% and -14.8% to 16.1%, respectively. However, these variabilities increased to 95% LOA of -11.3% to 3.9% and -123.1% to 18.4% between CT scans with different reconstruction parameters. Multivariable analysis showed that absolute differences were not associated with the baseline extent of fibrosis (
    MeSH term(s) Aged ; Female ; Humans ; Male ; Linear Models ; Lung Diseases, Interstitial/diagnosis ; Prospective Studies ; Pulmonary Fibrosis ; Tomography, X-Ray Computed ; Middle Aged
    Language English
    Publishing date 2024-03-14
    Publishing country United States
    Document type Multicenter Study ; Journal Article
    ISSN 2638-6135
    ISSN (online) 2638-6135
    DOI 10.1148/ryct.230287
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  4. Article ; Online: Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia.

    Lee, Taehee / Hwang, Eui Jin / Park, Chang Min / Goo, Jin Mo

    Academic radiology

    2023  Volume 30, Issue 12, Page(s) 2844–2855

    Abstract: Rationale and objectives: The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative ...

    Abstract Rationale and objectives: The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves.
    Results: In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012).
    Conclusion: Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.
    MeSH term(s) Male ; Humans ; Middle Aged ; Retrospective Studies ; Deep Learning ; Radiography, Thoracic/methods ; Pneumonia/diagnostic imaging ; Pneumonia/etiology ; Radiography ; Disease Progression
    Language English
    Publishing date 2023-03-16
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1355509-1
    ISSN 1878-4046 ; 1076-6332
    ISSN (online) 1878-4046
    ISSN 1076-6332
    DOI 10.1016/j.acra.2023.02.016
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    Zs.A 4594: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  5. Article ; Online: Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.

    Hong, Wonju / Hwang, Eui Jin / Park, Chang Min / Goo, Jin Mo

    Korean journal of radiology

    2023  Volume 24, Issue 9, Page(s) 890–902

    Abstract: Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) ... ...

    Abstract Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT).
    Materials and methods: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit.
    Results: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33;
    Conclusion: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.
    MeSH term(s) Male ; Humans ; Aged ; Artificial Intelligence ; Pulmonary Medicine ; Tomography, X-Ray Computed ; Computers ; Ambulatory Care Facilities ; Referral and Consultation
    Language English
    Publishing date 2023-08-27
    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.2023.0255
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    Zs.A 5591: Show issues Location:
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  6. Article ; Online: AI for Detection of Tuberculosis: Implications for Global Health.

    Hwang, Eui Jin / Jeong, Won Gi / David, Pierre-Marie / Arentz, Matthew / Ruhwald, Morten / Yoon, Soon Ho

    Radiology. Artificial intelligence

    2024  Volume 6, Issue 2, Page(s) e230327

    Abstract: Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the ... ...

    Abstract Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity.
    MeSH term(s) Humans ; Artificial Intelligence ; Global Health ; Software ; Diagnosis, Computer-Assisted/methods ; Tuberculosis
    Language English
    Publishing date 2024-01-10
    Publishing country United States
    Document type Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.230327
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  7. Article ; Online: Penetration rates into the stratum corneum layer: A novel quantitative indicator for assessing skin barrier function.

    Yoo, Suji / Kim, Jongwook / Jeong, Eui Taek / Hwang, Seung Jin / Kang, Nae-Gyu / Lee, Jinyong

    Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)

    2024  Volume 30, Issue 3, Page(s) e13655

    Abstract: Background: The stratum corneum (SC), the outermost layer of the skin epidermis, acts as an effective bi-directional barrier, preventing water loss (inside-outside barrier) and entry of foreign substances (outside-inside barrier). Although ... ...

    Abstract Background: The stratum corneum (SC), the outermost layer of the skin epidermis, acts as an effective bi-directional barrier, preventing water loss (inside-outside barrier) and entry of foreign substances (outside-inside barrier). Although transepidermal water loss (TEWL) is a widely-used measure of barrier function, it represents only inside-outside protection. Therefore, we aimed to establish a non-invasive method for quantitative evaluation of the outside-inside barrier function and visually present a skin barrier model.
    Materials and methods: Skin barrier damage was induced by applying a closed patch of 1% sodium dodecyl sulfate to the forearms of eight participants; they were instructed to apply a barrier cream on a designated damaged area twice daily for 5 days. The SC barrier was evaluated by measuring TEWL and fluorescein sodium salt penetration rate before, immediately after, and 5 days after damage. The penetration rate was assessed using tape-stripping (TS) technique and fluorescence microscopy.
    Results: The rates of fluorescein sodium salt penetration into the lower layers of SC differed significantly based on the degree of skin barrier damage. The correlation between penetration rate and TEWL was weak after two rounds of TS and became stronger after subsequent rounds. Five days after skin barrier damage, the penetration rate of all layers differed significantly between areas with and without the barrier cream application.
    Conclusion: Our findings demonstrated that the penetration rate was dependent on skin barrier conditions. The penetration rate and corresponding fluorescence images are suitable quantitative indicators that can visually represent skin barrier conditions.
    MeSH term(s) Humans ; Fluorescein/metabolism ; Fluorescein/pharmacology ; Water Loss, Insensible ; Epidermis/metabolism ; Skin/metabolism ; Skin Diseases/metabolism ; Water/metabolism ; Emollients/pharmacology
    Chemical Substances Fluorescein (TPY09G7XIR) ; Water (059QF0KO0R) ; Emollients
    Language English
    Publishing date 2024-03-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 1229160-2
    ISSN 1600-0846 ; 0909-752X ; 1397-1344
    ISSN (online) 1600-0846
    ISSN 0909-752X ; 1397-1344
    DOI 10.1111/srt.13655
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    Zs.A 4278: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  8. Article ; Online: Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs.

    Huh, Jung Eun / Lee, Jong Hyuk / Hwang, Eui Jin / Park, Chang Min

    Korean journal of radiology

    2023  Volume 24, Issue 2, Page(s) 155–165

    Abstract: Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the ... ...

    Abstract Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model.
    Materials and methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the
    Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all
    Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.
    MeSH term(s) Humans ; Artificial Intelligence ; Radiography, Thoracic/methods ; Deep Learning ; Radiographic Image Interpretation, Computer-Assisted/methods ; Lung Neoplasms/diagnostic imaging ; Sensitivity and Specificity ; Lung ; Reference Standards ; Retrospective Studies
    Language English
    Publishing date 2023-02-01
    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.2022.0548
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  9. Article: Persistent pulmonary subsolid nodules: How long should they be observed until clinically relevant growth occurs?

    Hwang, Eui Jin / Park, Chang Min

    Journal of thoracic disease

    2019  Volume 11, Issue Suppl 9, Page(s) S1408–S1411

    Language English
    Publishing date 2019-06-05
    Publishing country China
    Document type Editorial ; Comment
    ZDB-ID 2573571-8
    ISSN 2077-6624 ; 2072-1439
    ISSN (online) 2077-6624
    ISSN 2072-1439
    DOI 10.21037/jtd.2019.03.08
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  10. Article ; Online: A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

    Kim, Changi / Hwang, Eui Jin / Choi, Ye Ra / Choi, Hyewon / Goo, Jin Mo / Kim, Yisak / Choi, Jinwook / Park, Chang Min

    AJR. American journal of roentgenology

    2023  Volume 221, Issue 5, Page(s) 586–598

    Abstract: BACKGROUND. ...

    Abstract BACKGROUND.
    Language English
    Publishing date 2023-06-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.23.29414
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