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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 | |||||
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Cite as
WATANABE, Yoshiyuki, TANAKA, Takahiro, NISHIDA, Atsushi, TAKAHASHI, Hiroto, FUJIWARA, Masahiro, FUJIWARA, Takuya, ARISAWA, Atsuko, YANO, Hiroki, TOMIYAMA, Noriyuki, NAKAMURA, Hajime, TODO, Kenichi, YOSHIYA, Kazuhisa, 2020, Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.: Springer Nature.
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