{"created":"2024-12-03T02:28:17.474822+00:00","id":2002083,"links":{},"metadata":{"_buckets":{"deposit":"f8f05371-e34d-426f-95ae-76549a0c8ce9"},"_deposit":{"created_by":8,"id":"2002083","owners":[8],"pid":{"revision_id":0,"type":"depid","value":"2002083"},"status":"published"},"_oai":{"id":"oai:fmu.repo.nii.ac.jp:02002083","sets":["1732778801467:1732778962699:1733191882419"]},"author_link":[],"item_1617186331708":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_title":"Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography","subitem_title_language":"en"}]},"item_1617186419668":{"attribute_name":"Creator","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Higuchi, Mitsunori","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nagata, Takeshi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Iwabuchi, Kohei","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Sano, Akira","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Maekawa, Hidemasa","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Idaka, Takayuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Yamasaki, Manabu","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Seko, Chihiro","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Sato, Atsushi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Suzuki, Junzo","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Anzai, Yoshiyuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Yabuki, Takashi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Saito, Takuro","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Suzuki, Hiroyuki","creatorNameLang":"en"}]}]},"item_1617186499011":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_rights":"© 2023 The Fukushima Society of Medical Science. This article is licensed under a Creative Commons [Attribution-NonCommercial-ShareAlike 4.0 International] license.","subitem_rights_language":"en","subitem_rights_resource":"https://creativecommons.org/licenses/by-nc-sa/4.0/"}]},"item_1617186609386":{"attribute_name":"Subject","attribute_value_mlt":[{"subitem_subject":"artificial intelligence (AI)","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"deep learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"chest radiography","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"lung cancer","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_1617186626617":{"attribute_name":"Description","attribute_value_mlt":[{"subitem_description":"Background: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. Methods: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. Results: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. Conclusions: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_1617186643794":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_publisher":"The Fukushima Society of Medical Science","subitem_publisher_language":"en"}]},"item_1617186702042":{"attribute_name":"Language","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_1617186920753":{"attribute_name":"Source Identifier","attribute_value_mlt":[{"subitem_source_identifier":"0016-2590","subitem_source_identifier_type":"PISSN"},{"subitem_source_identifier":"2185-4610","subitem_source_identifier_type":"EISSN"},{"subitem_source_identifier":"AA0065246X","subitem_source_identifier_type":"NCID"}]},"item_1617187056579":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2023","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"183","bibliographicPageStart":"177","bibliographicVolumeNumber":"69","bibliographic_titles":[{"bibliographic_title":"Fukushima Journal of Medical Science","bibliographic_titleLang":"en"}]}]},"item_1617258105262":{"attribute_name":"Resource Type","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_1617265215918":{"attribute_name":"Version Type","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1617353299429":{"attribute_name":"Relation","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.5387/fms.2023-14","subitem_relation_type_select":"DOI"}},{"subitem_relation_type_id":{"subitem_relation_type_id_text":"37853640","subitem_relation_type_select":"PMID"}}]},"item_1617605131499":{"attribute_name":"File","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2023-11-22"}],"displaytype":"detail","filename":"FksmJMedSci_69_p177.pdf","filesize":[{"value":"1.7 MB"}],"format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://fmu.repo.nii.ac.jp/record/2002083/files/FksmJMedSci_69_p177.pdf"},"version_id":"4cfe77b0-7412-4f7b-b11e-5819c06270ec"}]},"item_title":"Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography","item_type_id":"40002","owner":"8","path":["1733191882419"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-11-22"},"publish_date":"2023-11-22","publish_status":"0","recid":"2002083","relation_version_is_last":true,"title":["Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography"],"weko_creator_id":"8","weko_shared_id":-1},"updated":"2024-12-03T02:34:38.939620+00:00"}