{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T10:10:41Z","timestamp":1786270241242,"version":"build-2736575974"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJSP2138"],"award-info":[{"award-number":["JPMJSP2138"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004206","name":"Osaka University","doi-asserted-by":"publisher","award":["hp230031"],"award-info":[{"award-number":["hp230031"]}],"id":[{"id":"10.13039\/501100004206","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["eBioMedicine"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.ebiom.2024.105463","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T18:33:26Z","timestamp":1732818806000},"page":"105463","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study"],"prefix":"10.1016","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1206-2036","authenticated-orcid":false,"given":"Junya","family":"Sato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kento","family":"Sugimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1224-6601","authenticated-orcid":false,"given":"Tomohiro","family":"Wataya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-3348","authenticated-orcid":false,"given":"Kosuke","family":"Kita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7006-3408","authenticated-orcid":false,"given":"Daiki","family":"Nishigaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miyuki","family":"Tomiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Hiraoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Hori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshihiro","family":"Takeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoji","family":"Kido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Tomiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ebiom.2024.105463_bib1","series-title":"Health care use","year":"2017"},{"key":"10.1016\/j.ebiom.2024.105463_bib2","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.acra.2015.05.007","article-title":"The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload","volume":"22","author":"McDonald","year":"2015","journal-title":"Acad Radiol"},{"key":"10.1016\/j.ebiom.2024.105463_bib3","article-title":"Radiologist shortage leaves patient care at risk, warns royal college","volume":"359","author":"Rimmer","year":"2017","journal-title":"BMJ"},{"key":"10.1016\/j.ebiom.2024.105463_bib4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1148\/radiol.2017170555","article-title":"Effect of Shift, Schedule, and volume on interpretive accuracy: a retrospective analysis of 2.9 million radiologic examinations","volume":"287","author":"Hanna","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.ebiom.2024.105463_bib5","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s41746-023-00829-4","article-title":"The impact of artificial intelligence on the reading times of radiologists for chest radiographs","volume":"6","author":"Shin","year":"2023","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.ebiom.2024.105463_bib6","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1038\/s41467-023-39631-x","article-title":"Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs","volume":"14","author":"Pyrros","year":"2023","journal-title":"Nat Commun"},{"key":"10.1016\/j.ebiom.2024.105463_bib7","first-page":"590","article-title":"CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison","volume":"33","author":"Irvin","year":"2019","journal-title":"Proc Conf AAAI Artif Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib8","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat Med"},{"key":"10.1016\/j.ebiom.2024.105463_bib9","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s12911-016-0332-1","article-title":"Feasibility of extracting data from electronic medical records for research: an international comparative study","volume":"16","author":"van Velthoven","year":"2016","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.ebiom.2024.105463_bib10","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41551-022-00936-9","article-title":"Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning","volume":"6","author":"Tiu","year":"2022","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.ebiom.2024.105463_bib11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s42256-021-00425-9","article-title":"Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports","volume":"4","author":"Zhou","year":"2022","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib12","series-title":"Proceedings of the 7th Machine Learning for Healthcare Conference","first-page":"2","article-title":"2020. Contrastive learning of medical visual representations from paired images and text","author":"Zhang","year":"2022"},{"key":"10.1016\/j.ebiom.2024.105463_bib13","article-title":"Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports","volume":"4","author":"Liu","year":"2023","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.ebiom.2024.105463_bib14","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib15","article-title":"Performance of the winning algorithms of the RSNA 2022 cervical spine fracture detection challenge","volume":"6","author":"Lee","year":"2024","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib16","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.ebiom.2024.105463_bib17","article-title":"Large batch and patch size training for medical image segmentation","author":"Sato","year":"2022","journal-title":"arXiv [eess.IV]"},{"key":"10.1016\/j.ebiom.2024.105463_bib18","article-title":"AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation","author":"Ji","year":"2022","journal-title":"arXiv [eess.IV"},{"key":"10.1016\/j.ebiom.2024.105463_bib19","first-page":"300","article-title":"Semiautomated organ segmentation using 3-dimensional medical imagery through sparse representation","volume":"51","author":"Watanabe","year":"2013","journal-title":"Trans Jpn Soc Med Biol Eng"},{"key":"10.1016\/j.ebiom.2024.105463_bib20","doi-asserted-by":"crossref","DOI":"10.2196\/49041","article-title":"Extracting clinical information from Japanese radiology reports using a 2-stage deep learning approach: algorithm development and validation","volume":"11","author":"Sugimoto","year":"2023","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.ebiom.2024.105463_bib21","first-page":"569","article-title":"Classification of diagnostic certainty in radiology reports with deep learning","volume":"310","author":"Sugimoto","year":"2024","journal-title":"Stud Health Technol Inf"},{"key":"10.1016\/j.ebiom.2024.105463_bib22","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A\u00a0brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl Sci Rev"},{"key":"10.1016\/j.ebiom.2024.105463_bib23","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"16133","article-title":"ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders","author":"Woo","year":"2023"},{"key":"10.1016\/j.ebiom.2024.105463_bib24","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.210026","article-title":"Classification of multiple diseases on body ct scans using weakly supervised deep learning","volume":"4","author":"Tushar","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib25","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.230024","article-title":"TotalSegmentator: robust segmentation of 104 anatomic structures in CT images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.ebiom.2024.105463_bib26","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1038\/s41467-021-22018-1","article-title":"Multi-task weak supervision enables anatomically resolved abnormality detection in whole-body FDG-PET\/CT","volume":"12","author":"Eyuboglu","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.ebiom.2024.105463_bib27","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.ebiom.2024.105463_bib28","article-title":"NegBio: a high-performance tool for negation and uncertainty detection in radiology reports","author":"Peng","year":"2018","journal-title":"arXiv [csCL]"},{"key":"10.1016\/j.ebiom.2024.105463_bib29","doi-asserted-by":"crossref","first-page":"e232","DOI":"10.1016\/S2589-7500(19)30108-6","article-title":"Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study","volume":"1","author":"Faes","year":"2019","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.ebiom.2024.105463_bib30","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","article-title":"MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports","volume":"6","author":"Johnson","year":"2019","journal-title":"Sci Data"},{"key":"10.1016\/j.ebiom.2024.105463_bib31","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1007\/s00330-021-07992-w","article-title":"Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study","volume":"31","author":"van Winkel","year":"2021","journal-title":"Eur Radiol"}],"container-title":["eBioMedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396424004997?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396424004997?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T23:53:44Z","timestamp":1733961224000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352396424004997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":31,"alternative-id":["S2352396424004997"],"URL":"https:\/\/doi.org\/10.1016\/j.ebiom.2024.105463","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.06.10.24308633","asserted-by":"object"}]},"ISSN":["2352-3964"],"issn-type":[{"value":"2352-3964","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study","name":"articletitle","label":"Article Title"},{"value":"eBioMedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ebiom.2024.105463","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"105463"}}