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Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.
http://hdl.handle.net/10422/00012819
http://hdl.handle.net/10422/00012819e3c2d553-51ff-459c-9c9d-152c0a9a2618
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-10-20 | |||||
| タイトル | ||||||
| タイトル | Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection. | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Computed tomography | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Deep learning | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Diagnosis | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Efficacy | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Intracranial haemorrhage | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Retrospective | |||||
| 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 | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
WATANABE, Yoshiyuki
× WATANABE, Yoshiyuki× TANAKA, Takahiro× NISHIDA, Atsushi× TAKAHASHI, Hiroto× FUJIWARA, Masahiro× FUJIWARA, Takuya× ARISAWA, Atsuko× YANO, Hiroki× TOMIYAMA, Noriyuki× NAKAMURA, Hajime× TODO, Kenichi× YOSHIYA, Kazuhisa |
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| 著者別名 |
渡邉, 嘉之
× 渡邉, 嘉之 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Purpose: To elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Methods: A total of 40 head CT datasets (normal, 16; haemorrhagic, 24) were evaluated by 15 physicians (5 board-certificated radiologists, 5 radiology residents, and 5 medical interns). The physicians attended 2 reading sessions without and with CAD. All physicians annotated the haemorrhagic regions with a degree of confidence, and the reading time was recorded in each case. Our CAD system was developed using 433 patients' head CT images (normal, 203; haemorrhagic, 230), and haemorrhage rates were displayed as corresponding probability heat maps using U-Net and a machine learning-based false-positive removal method. Sensitivity, specificity, accuracy, and figure of merit (FOM) were calculated based on the annotations and confidence levels. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Results: In patient-based evaluation, the mean accuracy of all physicians significantly increased from 83.7 to 89.7% (p < 0.001) after using CAD. Additionally, accuracies of board-certificated radiologists, radiology residents, and interns were 92.5, 82.5, and 76.0% without CAD and 97.5, 90.5, and 81.0% with CAD, respectively. The mean FOM of all physicians increased from 0.78 to 0.82 (p = 0.004) after using CAD. The reading time was significantly lower when CAD (43 s) was used than when it was not (68 s, p < 0.001) for all physicians. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Conclusion: The CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage. |
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| 書誌情報 |
en : Neuroradiology 発行日 2020-10-06 |
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| 出版者 | ||||||
| 出版者 | Springer Nature | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1432-1920 | |||||
| PMID | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 33025044 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1007/s00234-020-02566-x | |||||
| 関連名称 | 10.1007/s00234-020-02566-x | |||||
| 権利 | ||||||
| 権利情報 | © Springer-Verlag GmbH Germany, part of Springer Nature 2020 | |||||
| 資源タイプ | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Journal Article | |||||
