{"_buckets": {"deposit": "21e45438-3d92-48d4-a25d-03174d223c98"}, "_deposit": {"created_by": 11, "id": "3706", "owners": [11], "pid": {"revision_id": 0, "type": "depid", "value": "3706"}, "status": "published"}, "_oai": {"id": "oai:shiga-med.repo.nii.ac.jp:00003706", "sets": ["262"]}, "author_link": ["8208", "7911", "8216", "8214", "8209", "8210", "8212", "8213", "8217", "8207", "8211", "8215"], "item_4_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2020-10-06", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}, {"bibliographic_title": "Neuroradiology", "bibliographic_titleLang": "en"}]}]}, "item_4_creator_3": {"attribute_name": "著者別名", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "渡邉, 嘉之"}], "nameIdentifiers": [{"nameIdentifier": "7911", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "20362733", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=20362733"}, {"nameIdentifier": "0000-0003-3906-3730", "nameIdentifierScheme": "ORCID", "nameIdentifierURI": "https://orcid.org/0000-0003-3906-3730"}]}]}, "item_4_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Purpose:\nTo elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT. ", "subitem_description_type": "Abstract"}, {"subitem_description": "Methods:\nA 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\u0027 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. ", "subitem_description_type": "Abstract"}, {"subitem_description": "Results:\nIn patient-based evaluation, the mean accuracy of all physicians significantly increased from 83.7 to 89.7% (p \u003c 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 \u003c 0.001) for all physicians. ", "subitem_description_type": "Abstract"}, {"subitem_description": "Conclusion:\nThe CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage. ", "subitem_description_type": "Abstract"}]}, "item_4_description_42": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"subitem_description": "Journal Article", "subitem_description_type": "Other"}]}, "item_4_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Springer Nature"}]}, "item_4_relation_10": {"attribute_name": "PubMed番号", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "33025044", "subitem_relation_type_select": "PMID"}}]}, "item_4_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "10.1007/s00234-020-02566-x"}], "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1007/s00234-020-02566-x", "subitem_relation_type_select": "DOI"}}]}, "item_4_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "© Springer-Verlag GmbH Germany, part of Springer Nature 2020"}]}, "item_4_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1432-1920", "subitem_source_identifier_type": "ISSN"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "metadata only access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_14cb"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "WATANABE, Yoshiyuki"}], "nameIdentifiers": [{"nameIdentifier": "7911", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "20362733", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=20362733"}, {"nameIdentifier": "0000-0003-3906-3730", "nameIdentifierScheme": "ORCID", "nameIdentifierURI": "https://orcid.org/0000-0003-3906-3730"}]}, {"creatorNames": [{"creatorName": "TANAKA, Takahiro"}], "nameIdentifiers": [{"nameIdentifier": "8207", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "NISHIDA, Atsushi"}], "nameIdentifiers": [{"nameIdentifier": "8208", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "TAKAHASHI, Hiroto"}], "nameIdentifiers": [{"nameIdentifier": "8209", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "FUJIWARA, Masahiro"}], "nameIdentifiers": [{"nameIdentifier": "8210", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "FUJIWARA, Takuya"}], "nameIdentifiers": [{"nameIdentifier": "8211", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "ARISAWA, Atsuko"}], "nameIdentifiers": [{"nameIdentifier": "8212", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "YANO, Hiroki"}], "nameIdentifiers": [{"nameIdentifier": "8213", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "TOMIYAMA, Noriyuki"}], "nameIdentifiers": [{"nameIdentifier": "8214", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "NAKAMURA, Hajime"}], "nameIdentifiers": [{"nameIdentifier": "8215", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "TODO, Kenichi"}], "nameIdentifiers": [{"nameIdentifier": "8216", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "YOSHIYA, Kazuhisa"}], "nameIdentifiers": [{"nameIdentifier": "8217", "nameIdentifierScheme": "WEKO"}]}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "Computed tomography", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Deep learning", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Diagnosis", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Efficacy", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Intracranial haemorrhage", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Retrospective", "subitem_subject_language": "en", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection."}]}, "item_type_id": "4", "owner": "11", "path": ["262"], "permalink_uri": "http://hdl.handle.net/10422/00012819", "pubdate": {"attribute_name": "公開日", "attribute_value": "2020-10-20"}, "publish_date": "2020-10-20", "publish_status": "0", "recid": "3706", "relation": {}, "relation_version_is_last": true, "title": ["Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection."], "weko_shared_id": -1}