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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.
Afiliación
  • Niiya A; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan. aniiya@med.showa-u.ac.jp.
  • Murakami K; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Kobayashi R; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Sekimoto A; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Saeki M; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Toyofuku K; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Kato M; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Shinjo H; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Ito Y; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
  • Takei M; Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan.
  • Murata C; Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan.
  • Ohgiya Y; Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
Sci Rep ; 12(1): 8363, 2022 05 19.
Article en En | MEDLINE | ID: mdl-35589847
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fracturas de las Costillas Tipo de estudio: Diagnostic_studies / Observational_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fracturas de las Costillas Tipo de estudio: Diagnostic_studies / Observational_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido