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  1. Article ; Online: Curtis P. Langlotz, MD, PhD, President, Radiological Society of North America, 2024.

    Eng, John

    Radiology

    2023  Volume 310, Issue 1, Page(s) e249001

    MeSH term(s) Humans ; Radiography ; Radiology ; North America
    Language English
    Publishing date 2023-12-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.249001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book: Radiologie compact

    Eng, John

    37 Tabellen

    (US-ART, US-American radiology toolbooks)

    1999  

    Author's details hrsg. von John Eng. Übers. von Kirsten Holsteg
    Series title US-ART, US-American radiology toolbooks
    Keywords Radiologische Diagnostik
    Subject Diagnostische Radiologie ; Strahlendiagnostik
    Language German
    Size XVI, 357 S. : Ill., graph. Darst.
    Publisher Thieme
    Publishing place Stuttgart u.a.
    Document type Book
    Note Aus d. Engl. übers.
    HBZ-ID HT010502929
    ISBN 3-13-117431-5 ; 978-3-13-117431-4
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    1999 A 4021 order for loan Magazin

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  3. Book: Manual of radiology

    Eng, John

    acute problems and essential procedures

    1997  

    Author's details ed. John Eng
    Keywords Radiography / handbooks ; Radiography / methods / handbooks
    Language English
    Size XV, 351 S. : Ill., graph. Darst.
    Publisher Lippincott-Raven
    Publishing place Philadelphia u.a.
    Publishing country United States
    Document type Book
    HBZ-ID HT007568939
    ISBN 0-397-51768-8 ; 978-0-397-51768-8
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    1997 A 5066 order for loan Magazin

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  4. Article ; Online: External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.

    Yu, Alice C / Mohajer, Bahram / Eng, John

    Radiology. Artificial intelligence

    2022  Volume 4, Issue 3, Page(s) e210064

    Abstract: Purpose: To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis.: Materials and methods: In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based ... ...

    Abstract Purpose: To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis.
    Materials and methods: In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics.
    Results: Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance.
    Conclusion: Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.
    Language English
    Publishing date 2022-05-04
    Publishing country United States
    Document type Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.210064
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

    Yu, Alice C / Eng, John

    Radiographics : a review publication of the Radiological Society of North America, Inc

    2020  Volume 40, Issue 7, Page(s) 1932–1937

    Abstract: Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or exceeding ...

    Abstract Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or exceeding that of radiologists, the results are less impressive when the algorithms are installed at new hospitals and are presented with new images. This phenomenon is potentially the result of selection bias in the data that were used to develop the ML algorithm. Selection bias has long been described by clinical epidemiologists as a key consideration when designing a clinical research study, but this concept has largely been unaddressed in the medical imaging ML literature. The authors discuss the importance of selection bias and its relevance to ML algorithm development to prepare the radiologist to critically evaluate ML literature for potential selection bias and understand how it might affect the applicability of ML algorithms in real clinical environments.
    Language English
    Publishing date 2020-09-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603172-9
    ISSN 1527-1323 ; 0271-5333
    ISSN (online) 1527-1323
    ISSN 0271-5333
    DOI 10.1148/rg.2020200040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1751: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Imaging Publications in the COVID-19 Pandemic: Applying New Research Results to Clinical Practice.

    Eng, John / Bluemke, David A

    Radiology

    2020  Volume 297, Issue 1, Page(s) E228–E231

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections/diagnostic imaging ; Epidemiologic Research Design ; Humans ; Pandemics ; Pneumonia, Viral/diagnostic imaging ; Predictive Value of Tests ; Publications/standards ; Radiology/organization & administration ; SARS-CoV-2 ; Tomography, X-Ray Computed
    Keywords covid19
    Language English
    Publishing date 2020-04-23
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020201724
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Receiver operating characteristic analysis: utility, reality, covariates, and the future.

    Eng, John

    Academic radiology

    2013  Volume 20, Issue 7, Page(s) 795–797

    MeSH term(s) Humans ; ROC Curve ; Radiology
    Language English
    Publishing date 2013-07
    Publishing country United States
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 1355509-1
    ISSN 1878-4046 ; 1076-6332
    ISSN (online) 1878-4046
    ISSN 1076-6332
    DOI 10.1016/j.acra.2013.05.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Sampling the latest work in receiver operating characteristic analysis: what does it mean?

    Eng, John

    Academic radiology

    2012  Volume 19, Issue 12, Page(s) 1449–1451

    MeSH term(s) History, 20th Century ; History, 21st Century ; Humans ; ROC Curve ; Radiology/history ; Radiology/methods ; Sensitivity and Specificity
    Language English
    Publishing date 2012-12
    Publishing country United States
    Document type Biography ; Editorial ; Historical Article ; Introductory Journal Article
    ZDB-ID 1355509-1
    ISSN 1878-4046 ; 1076-6332
    ISSN (online) 1878-4046
    ISSN 1076-6332
    DOI 10.1016/j.acra.2012.09.019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Teaching receiver operating characteristic analysis: an interactive laboratory exercise.

    Eng, John

    Academic radiology

    2012  Volume 19, Issue 12, Page(s) 1452–1456

    Abstract: Rationale and objectives: Despite its fundamental importance in the evaluation of diagnostic tests, receiver operating characteristic (ROC) analysis is not easily understood. The purpose of this project was to create a learning experience that resulted ... ...

    Abstract Rationale and objectives: Despite its fundamental importance in the evaluation of diagnostic tests, receiver operating characteristic (ROC) analysis is not easily understood. The purpose of this project was to create a learning experience that resulted in an intuitive understanding of the basic principles of ROC analysis.
    Materials and methods: An interactive laboratory exercise was developed for a class about radiology testing taught within a clinical epidemiology course between 2000 and 2009. The physician students in the course were clinical fellows from various medical specialties who were enrolled in a graduate degree program in clinical investigation. For the exercise, the class was divided into six groups. Each group interpreted radiographs from a set of 50 exams of the peripheral skeleton to determine the presence or absence of an acute fracture. Data from the class were pooled and given to each student. Students calculated the area under the ROC curve (AUC) corresponding to overall class performance. A binormal ROC curve was also fitted to the data from each class year.
    Results: The laboratory exercise was conducted for 8 years with approximately 20-30 students per year. The mean AUC over the eight laboratory classes was 0.72 with a standard deviation of 0.08 (range, 0.60-0.85).
    Conclusion: With some simplifications in design, an observer study can be conducted in a laboratory classroom setting. Participatory data collection promotes the intuitive understanding of ROC analysis principles.
    MeSH term(s) Area Under Curve ; Humans ; Internship and Residency ; ROC Curve ; Radiographic Image Interpretation, Computer-Assisted ; Radiology/education ; Sensitivity and Specificity ; Teaching/methods
    Language English
    Publishing date 2012-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1355509-1
    ISSN 1878-4046 ; 1076-6332
    ISSN (online) 1878-4046
    ISSN 1076-6332
    DOI 10.1016/j.acra.2012.09.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Generating "virtual physicals on demand" patient videos with standardized "range of motion protocols" in plastic surgery.

    Eng, John S

    Plastic and reconstructive surgery

    2014  Volume 134, Issue 4 Suppl 1, Page(s) 49

    Language English
    Publishing date 2014-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208012-6
    ISSN 1529-4242 ; 0032-1052 ; 0096-8501
    ISSN (online) 1529-4242
    ISSN 0032-1052 ; 0096-8501
    DOI 10.1097/01.prs.0000455387.82142.be
    Database MEDical Literature Analysis and Retrieval System OnLINE

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