| 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 |
Explainable deep-learning-based ischemia detection using hybrid O-15 H2O perfusion positron emission tomography and computed tomography imaging with clinical data |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
CAD |
| キーワード |
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主題Scheme |
Other |
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主題 |
PET/CT |
| キーワード |
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主題Scheme |
Other |
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主題 |
Myocardial perfusion |
| キーワード |
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主題Scheme |
Other |
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主題 |
Deep learning |
| キーワード |
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主題Scheme |
Other |
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主題 |
Explainability |
| 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 |
Teuho, Jarmo
Schultz, Jussi
Klén, Riku
Juarez-Orozco, Luis Eduardo
Knuuti, Juhani
Saraste, Antti
小野, 直亮
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 |
Background We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H2O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings. Methods A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image—and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test. Results The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading. Conclusions The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner. |
| 書誌情報 |
en : Journal of Nuclear Cardiology
巻 38,
ページ数 15,
発行日 2024-08-01
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| 出版者 |
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出版者 |
Elsevier |
| 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 |
1532-6551 |
| 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/10.1016/j.nuclcard.2024.101889 |
| 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://www.sciencedirect.com/science/article/pii/S1071358124005439 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2024 The Author(s). Published by Elsevier Inc. on behalf of American Society of Nuclear Cardiology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| 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 |
P19748 |
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研究課題名 |
Postdoctoral Fellowships for Research in Japan (Standard) |
| 助成情報 |
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助成機関名 |
Academy of Finland |
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研究課題番号 |
322019 |
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研究課題名 |
Academy of Finland mobility funding |
| 助成情報 |
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助成機関名 |
Finnish Cultural Foundation |
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研究課題名 |
Maire and Aimo Mäkinen Fund of the Finnish Cultural Foundation |
| 助成情報 |
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助成機関名 |
Academy of Finland |
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研究課題番号 |
351482 |
| 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 |
| 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 |
Turku University Foundation |
