Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning–based computer-assisted detection.
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. Methods: A total of 40 head CT datasets (normal, 16; haemorrhagic, 24) were evaluated by 15 physicians (5 board-c...
| Publicado en: | Neuroradiology Vol. 63; no. 5; pp. 713 - 721 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149789423&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149789423 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: May2021 vid: 63 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149789423 146294829 149789423 149789423 10.1007/s00234-020-02566-x 149789423 ppf: 713 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning–based computer-assisted detection. aug: au: Watanabe, Yoshiyuki Tanaka, Takahiro Nishida, Atsushi Takahashi, Hiroto Fujiwara, Masahiro Fujiwara, Takuya Arisawa, Atsuko Yano, Hiroki Tomiyama, Noriyuki Nakamura, Hajime Todo, Kenichi Yoshiya, Kazuhisa affil: Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine, Osaka, Japan sug: subj: Intracranial Hemorrhage Radiography Tomography, X-Ray Computed Diagnosis, Computer Assisted Image Interpretation, Computer Assisted Deep Learning Radiologists Interns and Residents Clinical Competence Evaluation Competency Assessment Human Decision Support Systems, Clinical Systems Development Confidence Machine Learning Sensitivity and Specificity Descriptive Statistics ab: 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. 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. 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. 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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