| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-08-26 |
| タイトル |
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タイトル |
Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Nakagawa, Shota
小野, 直亮
Hakamata, Yukichika
Ishii, Takashi
Saito, Akira
Yanagimoto, Shintaro
金谷, 重彦
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
The purpose of this study is to demonstrate the use of a deep learning model in quantitatively evaluating clinical findings typically subject to uncertain evaluations by physicians, using binary test results based on routine protocols. A chest X-ray is the most commonly used diagnostic tool for the detection of a wide range of diseases and is generally performed as a part of regular medical checkups. However, when it comes to findings that can be classified as within the normal range but are not considered disease-related, the thresholds of physicians’ findings can vary to some extent, therefore it is necessary to define a new evaluation method and quantify it. The implementation of such methods is difficult and expensive in terms of time and labor. In this study, a total of 83,005 chest X-ray images were used to diagnose the common findings of pleural thickening and scoliosis. A novel method for quantitatively evaluating the probability that a physician would judge the images to have these findings was established. The proposed method successfully quantified the variation in physicians’ findings using a deep learning model trained only on binary annotation data. It was also demonstrated that the developed method could be applied to both transfer learning using convolutional neural networks for general image analysis and a newly learned deep learning model based on vector quantization variational autoencoders with high correlations ranging from 0.89 to 0.97. |
| 書誌情報 |
en : PLOS Digital Health
巻 3,
号 3,
ページ数 17,
発行日 2024-03-15
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| 出版者 |
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出版者 |
Public Library of Science |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2767-3170 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1371/journal.pdig.0000460 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000460 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2024 Nakagawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
21K12111 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K12111/ |
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研究課題名 |
分散学習ネットワークモデルを用いた病理組織画像の特徴抽出の最適化 |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
21KK0183 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21KK0183/ |
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研究課題名 |
PETを用いた代謝疾患の動態解析のための統計モデルの開発 |
