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  1. AU="Murata, Chiori"
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  1. Article ; Online: Childhood fixed solar urticaria induced by visible light.

    Uetsu, Naoko / Murata, Chiori / Okamoto, Hiroyuki

    Photodermatology, photoimmunology & photomedicine

    2023  Volume 39, Issue 2, Page(s) 159–161

    MeSH term(s) Child ; Humans ; Urticaria, Solar ; Light ; Sunlight ; Urticaria ; Photosensitivity Disorders
    Language English
    Publishing date 2023-01-19
    Publishing country England
    Document type Letter
    ZDB-ID 1028855-7
    ISSN 1600-0781 ; 0108-9684 ; 0905-4383
    ISSN (online) 1600-0781
    ISSN 0108-9684 ; 0905-4383
    DOI 10.1111/phpp.12850
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness.

    Niiya, Akifumi / Murakami, Kouzou / Kobayashi, Rei / Sekimoto, Atsuhito / Saeki, Miho / Toyofuku, Kosuke / Kato, Masako / Shinjo, Hidenori / Ito, Yoshinori / Takei, Mizuki / Murata, Chiori / Ohgiya, Yoshimitsu

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 8363

    Abstract: Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In ...

    Abstract Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In this study, the clinical effectiveness of this algorithm was evaluated. A total of 56 cases were retrospectively examined, including 46 rib fractures and 10 control cases from our hospital, between January and June 2019. Two radiologists annotated the fracture lesions (complete or incomplete) for each CT image, which is considered the "ground truth." Thereafter, the algorithm's diagnostic results for all cases were compared with the ground truth, and the sensitivity and number of false positive (FP) results per case were assessed. The radiologists identified 199 images with a fracture. The sensitivity of the algorithm was 89.8%, and the number of FPs per case was 2.5. After additional learning, the sensitivity increased to 93.5%, and the number of FPs was 1.9 per case. FP results were found in the trabecular bone with the appearance of fracture, vascular grooves, and artifacts. The sensitivity of the algorithm used in this study was sufficient to aid the rapid detection of rib fractures within the evaluated validation set of CT images.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Humans ; Retrospective Studies ; Rib Fractures/diagnostic imaging ; Technology ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-05-19
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-12453-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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