|
|
福島県立医科大学学術成果リポジトリ = Fukushima Medical University Repository >
福島医学会 = The Fukushima Society of Medical Science >
Fukushima Journal of Medical Science >
Vol.69 (2023) >
このアイテムの引用には次の識別子を使用してください:
http://ir.fmu.ac.jp/dspace/handle/123456789/2199
|
| タイトル: | Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography |
| 著者: | Higuchi, Mitsunori
Nagata, Takeshi
Iwabuchi, Kohei
Sano, Akira
Maekawa, Hidemasa
Idaka, Takayuki
Yamasaki, Manabu
Seko, Chihiro
Sato, Atsushi
Suzuki, Junzo
Anzai, Yoshiyuki
Yabuki, Takashi
Saito, Takuro
Suzuki, Hiroyuki |
| 学内所属: | 会津医療センター外科学講座
呼吸器外科学講座 |
| 誌名/書名: | Fukushima Journal of Medical Science |
| 巻: | 69 |
| 号: | 3 |
| 開始ページ: | 177 |
| 終了ページ: | 183 |
| 発行日: | 2023年 |
| 抄録: | 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: | The Fukushima Society of Medical Science |
| 出版者（異表記）: | 福島医学会 |
| 本文の言語: | eng |
| このページのURI: | http://ir.fmu.ac.jp/dspace/handle/123456789/2199 |
| 本文URL: | http://ir.fmu.ac.jp/dspace/bitstream/123456789/2199/1/FksmJMedSci_69_p177.pdf |
| ISSN: | 0016-2590
2185-4610 |
| DOI: | 10.5387/fms.2023-14 |
| PubMed番号: | 37853640 |
| 関連ページ: | https://doi.org/10.5387/fms.2023-14 |
| 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. |
| 権利情報: | https://creativecommons.org/licenses/by-nc-sa/4.0/ |
| 出現コレクション: | Vol.69 (2023)
|
このリポジトリに保管されているアイテムは、他に指定されている場合を除き、著作権により保護されています。
|
