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アイテム

  1. 福島医学会
  2. Fukushima Journal of Medical Science
  3. Vol.69 (2023)

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

https://fmu.repo.nii.ac.jp/records/2002083
https://fmu.repo.nii.ac.jp/records/2002083
d5d03da9-7384-4c2b-b82d-aa590d69b45c
名前 / ファイル ライセンス アクション
FksmJMedSci_69_p177.pdf FksmJMedSci_69_p177.pdf (1.7 MB)
Item type デフォルトアイテムタイプ(フル)fmu(1)
公開日 2023-11-22
タイトル
タイトル Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography
言語 en
作成者 Higuchi, Mitsunori

× Higuchi, Mitsunori

en Higuchi, Mitsunori

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Nagata, Takeshi

× Nagata, Takeshi

en Nagata, Takeshi

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Iwabuchi, Kohei

× Iwabuchi, Kohei

en Iwabuchi, Kohei

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Sano, Akira

× Sano, Akira

en Sano, Akira

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Maekawa, Hidemasa

× Maekawa, Hidemasa

en Maekawa, Hidemasa

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Idaka, Takayuki

× Idaka, Takayuki

en Idaka, Takayuki

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Yamasaki, Manabu

× Yamasaki, Manabu

en Yamasaki, Manabu

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Seko, Chihiro

× Seko, Chihiro

en Seko, Chihiro

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Sato, Atsushi

× Sato, Atsushi

en Sato, Atsushi

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Suzuki, Junzo

× Suzuki, Junzo

en Suzuki, Junzo

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Anzai, Yoshiyuki

× Anzai, Yoshiyuki

en Anzai, Yoshiyuki

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Yabuki, Takashi

× Yabuki, Takashi

en Yabuki, Takashi

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Saito, Takuro

× Saito, Takuro

en Saito, Takuro

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Suzuki, Hiroyuki

× Suzuki, Hiroyuki

en Suzuki, Hiroyuki

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権利情報
権利情報Resource https://creativecommons.org/licenses/by-nc-sa/4.0/
権利情報 © 2023 The Fukushima Society of Medical Science. This article is licensed under a Creative Commons [Attribution-NonCommercial-ShareAlike 4.0 International] license.
内容記述
内容記述タイプ Abstract
内容記述 Background: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. Methods: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. Results: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. Conclusions: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.
出版者
出版者 The Fukushima Society of Medical Science
言語
言語 eng
書誌情報 en : Fukushima Journal of Medical Science

巻 69, 号 3, p. 177-183, 発行日 2023
関連情報
関連タイプ isIdenticalTo
識別子タイプ DOI
関連識別子 https://doi.org/10.5387/fms.2023-14
関連情報
識別子タイプ PMID
関連識別子 37853640
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
収録物識別子
収録物識別子タイプ PISSN
収録物識別子 0016-2590
収録物識別子
収録物識別子タイプ EISSN
収録物識別子 2185-4610
収録物識別子
収録物識別子タイプ NCID
収録物識別子 AA0065246X
主題
主題Scheme Other
主題 artificial intelligence (AI)
主題
主題Scheme Other
主題 deep learning
主題
主題Scheme Other
主題 chest radiography
主題
主題Scheme Other
主題 lung cancer
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