{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:54:14Z","timestamp":1785538454757,"version":"3.56.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1159","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Nature Science Foundation of Hubei Province","award":["2022CFB853"],"award-info":[{"award-number":["2022CFB853"]}]},{"name":"Young and Middle aged Talent Project of Education Department of Hubei Province","award":["Q202220110"],"award-info":[{"award-number":["Q202220110"]}]},{"name":"Advantages Discipline Group (Medicine) Project in Higher Education of Hubei Province","award":["2022XKQT1"],"award-info":[{"award-number":["2022XKQT1"]}]},{"name":"Advantages Discipline Group (Medicine) Project in Higher Education of Hubei Province","award":["2022XKQT5"],"award-info":[{"award-number":["2022XKQT5"]}]},{"name":"Innovation Training Program of Education Department of Hubei Province","award":["S202210929023"],"award-info":[{"award-number":["S202210929023"]}]},{"name":"Innovation Training Program of Education Department of Hubei Province","award":["X202110929019"],"award-info":[{"award-number":["X202110929019"]}]},{"name":"Innovation Training Program of Education Department of Hubei Province","award":["X202213249007"],"award-info":[{"award-number":["X202213249007"]}]},{"name":"Health Commission of Hubei Province Scientific Research Project","award":["WJ2021M047"],"award-info":[{"award-number":["WJ2021M047"]}]},{"name":"Shiyan City Scientific Research Project","award":["21Y31"],"award-info":[{"award-number":["21Y31"]}]},{"DOI":"10.13039\/100007452","name":"Wu Jieping Medical Foundation","doi-asserted-by":"publisher","award":["320.6750.2020-08-6"],"award-info":[{"award-number":["320.6750.2020-08-6"]}],"id":[{"id":"10.13039\/100007452","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Doctoral Start-up Fund","award":["2021EB0ZX01"],"award-info":[{"award-number":["2021EB0ZX01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,18]]},"abstract":"Abstract<\/jats:title>\n \n Objectives<\/jats:title>\n To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 and September 2022. Patients were divided into training (N\u2009=\u2009688), validation (N\u2009=\u2009145), testing dataset [N\u2009=\u2009291; N\u2009=\u2009104 for normal pancreas, N\u2009=\u200998 for AP, N\u2009=\u200989 for AP complicated with PDAC (AP&PDAC)]. A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using dice similarity coefficient (DSC) and intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumour and oedema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve (AUC) in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions.<\/jats:p>\n <\/jats:sec>\n \n Advances in knowledge<\/jats:title>\n This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bjr\/tqae091","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T04:29:25Z","timestamp":1715401765000},"page":"1268-1277","source":"Crossref","is-referenced-by-count":10,"title":["Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis"],"prefix":"10.1093","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-9990","authenticated-orcid":false,"given":"Xuhang","family":"Pan","sequence":"first","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijian","family":"Jiao","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linshuang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yige","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejun","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suping","family":"Chen","sequence":"additional","affiliation":[{"name":"Advanced Application Team, GE Healthcare , Shanghai 200135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1462-1639","authenticated-orcid":false,"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine , Shiyan 442000, China"},{"name":"School of Biomedical Engineering, Hubei University of Medicine , Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"issue":"45","key":"2024061918464461800_tqae091-B1","doi-asserted-by":"crossref","first-page":"5120","DOI":"10.3748\/wjg.v24.i45.5120","article-title":"Glucocorticoid receptor regulates expression of microRNA-22 and down-stream signaling pathway in apoptosis of pancreatic acinar cells","volume":"24","author":"Fu","year":"2018","journal-title":"World J Gastroenterol"},{"issue":"9","key":"2024061918464461800_tqae091-B2","doi-asserted-by":"crossref","first-page":"922299","DOI":"10.3389\/fmed.2022.922299","article-title":"Radiomics and its applications and progress in pancreatitis: a current state of the art review","volume":"23","author":"Yan","year":"2022","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"2024061918464461800_tqae091-B3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1136\/gutjnl-2012-302779","article-title":"Classification of acute pancreatitis\u20142012: revision of the Atlanta classification and definitions by international consensus","volume":"62","author":"Banks","year":"2013","journal-title":"Gut"},{"issue":"11S","key":"2024061918464461800_tqae091-B4","doi-asserted-by":"crossref","first-page":"S316","DOI":"10.1016\/j.jacr.2019.05.017","article-title":"ACR appropriateness criteria\u00ae acute pancreatitis","volume":"16","author":"Porter","year":"2019","journal-title":"J Am Coll Radiol"},{"key":"2024061918464461800_tqae091-B5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13054-016-1208-6","article-title":"36th international symposium on intensive care and emergency medicine: Brussels, Belgium. 15-18 March 2016","volume":"20(Suppl 2)","author":"Bateman","year":"2016","journal-title":"Crit Care"},{"issue":"1","key":"2024061918464461800_tqae091-B6","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s40644-020-00324-z","article-title":"Mass-forming pancreatitis versus pancreatic ductal adenocarcinoma: CT and MR imaging for differentiation","volume":"20","author":"Schima","year":"2020","journal-title":"Cancer Imaging"},{"issue":"1","key":"2024061918464461800_tqae091-B7","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s13045-021-01167-2","article-title":"Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data","volume":"14","author":"Gao","year":"2020","journal-title":"J Hematol Oncol"},{"key":"2024061918464461800_tqae091-B8","doi-asserted-by":"crossref","first-page":"101958","DOI":"10.1016\/j.media.2021.101958","article-title":"Pancreas segmentation using a dual-input v-mesh network","volume":"69","author":"Wang","year":"2021","journal-title":"Med Image Anal"},{"issue":"3","key":"2024061918464461800_tqae091-B9","first-page":"992","article-title":"Differentiation between pancreatic cancer and normal pancreas on computed tomography with artificial intelligence","volume":"44","author":"Liao","year":"2019","journal-title":"Gastroenterology"},{"key":"2024061918464461800_tqae091-B10","first-page":"222","author":"Zhou","year":"2017"},{"issue":"9","key":"2024061918464461800_tqae091-B11","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TMI.2013.2263388","article-title":"Optimal co-segmentation of tumor in PET-CT images with context information","volume":"32","author":"Song","year":"2013","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"2024061918464461800_tqae091-B12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41574-021-00573-3","article-title":"Metabolic implications of pancreatic fat accumulation","volume":"18","author":"Wagner","year":"2022","journal-title":"Nat Rev Endocrinol"},{"issue":"76","key":"2024061918464461800_tqae091-B13","doi-asserted-by":"crossref","first-page":"e2439","DOI":"10.6061\/clinics\/2021\/e2439","article-title":"Fatty pancreas: disease or finding?","volume":"76","author":"Silva","year":"2021","journal-title":"Clinics (Sao Paulo)"},{"issue":"4","key":"2024061918464461800_tqae091-B14","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.7150\/thno.52508","article-title":"Fully end-to-end deep-learning-based diagnosis of pancreatic tumors","volume":"11","author":"Si","year":"2021","journal-title":"Theranostics"},{"issue":"Pt 2","key":"2024061918464461800_tqae091-B15","first-page":"165","article-title":"Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images","volume":"16","author":"Chu","year":"2013","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"2024061918464461800_tqae091-B16","first-page":"234","author":"Ronneberger","year":"2015"},{"issue":"1","key":"2024061918464461800_tqae091-B17","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1148\/radiol.2018181353","article-title":"Reporting standards for chronic pancreatitis by using CT, MRI, and MR cholangiopancreatography: the consortium for the study of chronic pancreatitis, diabetes, and pancreatic cancer","volume":"290","author":"Tirkes","year":"2019","journal-title":"Radio"},{"issue":"6","key":"2024061918464461800_tqae091-B18","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3348\/kjr.2017.18.6.936","article-title":"Post-ischemic bowel stricture: CT features in eight cases","volume":"18","author":"Kim","year":"2017","journal-title":"Korean J Radiol"},{"issue":"16","key":"2024061918464461800_tqae091-B19","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.3748\/wjg.v23.i16.2826","article-title":"Transition of early-phase treatment for acute pancreatitis: an analysis of nationwide epidemiological survey","volume":"23","author":"Hamada","year":"2017","journal-title":"World J Gastroenterol"},{"issue":"3","key":"2024061918464461800_tqae091-B20","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1148\/radiol.2233010680","article-title":"Acute pancreatitis: assessment of severity with clinical and CT evaluation","volume":"223","author":"Balthazar","year":"2002","journal-title":"Radiology"},{"issue":"10","key":"2024061918464461800_tqae091-B21","doi-asserted-by":"crossref","first-page":"4736","DOI":"10.1007\/s00261-021-03152-2","article-title":"Evaluation of the contour of the pancreas: types and frequencies","volume":"46","author":"Dilek","year":"2021","journal-title":"Abdom Radiol (NY)"},{"issue":"3","key":"2024061918464461800_tqae091-B22","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1148\/radiol.11110947","article-title":"The revised Atlanta classification of acute pancreatitis: its importance for the radiologist and its effect on treatment","volume":"262","author":"Thoeni","year":"2012","journal-title":"Radiology"},{"issue":"6","key":"2024061918464461800_tqae091-B23","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1007\/s11604-017-0635-x","article-title":"Contour variations of the body and tail of the pancreas: evaluation with MDCT","volume":"35","author":"Omeri","year":"2017","journal-title":"Jpn J Radiol"},{"issue":"1","key":"2024061918464461800_tqae091-B24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12880-019-0355-z","article-title":"Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children","volume":"19","author":"Wang","year":"2019","journal-title":"BMC Med Imaging"},{"key":"2024061918464461800_tqae091-B25","author":"Gu","year":"2019"},{"issue":"4","key":"2024061918464461800_tqae091-B26","first-page":"1982","article-title":"Fatty pancreas: should we be concerned?","volume":"11","author":"Majumder","year":"2021","journal-title":"Radiology"},{"issue":"9","key":"2024061918464461800_tqae091-B27","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1210\/jc.2017-00828","article-title":"Pancreas fat and cell mass in humans with and without diabetes: an analysis in the Japanese population","volume":"102","author":"Murakami","year":"2017","journal-title":"J Clin Endocrinol Metab"},{"issue":"5","key":"2024061918464461800_tqae091-B28","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s00592-014-0581-3","article-title":"Differences in pancreatic volume, fat content, and fat density measured by multidetector-row computed tomography according to the duration of diabetes","volume":"51","author":"Lim","year":"2014","journal-title":"Acta Diabetol"},{"issue":"5","key":"2024061918464461800_tqae091-B29","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1007\/s00261-019-02292-w","article-title":"Pancreatitis and PDAC: association and differentiation","volume":"45","author":"Elsherif","year":"2020","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"2024061918464461800_tqae091-B30","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1016\/j.pan.2020.09.020","article-title":"Focal parenchymal atrophy of pancreas: an important sign of underlying high-grade pancreatic intraepithelial neoplasia without invasive crcarcinoma","volume":"20","author":"Nakahodo","year":"2017","journal-title":"Pancreatology"},{"issue":"2","key":"2024061918464461800_tqae091-B31","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00330-016-4442-z","article-title":"Decreased stage migration rate of early gastric cancer with a new reconstruction algorithm using dual-energy CT images: a preliminary study","volume":"27","author":"Shi","year":"2017","journal-title":"Eur Radiol"},{"issue":"2","key":"2024061918464461800_tqae091-B32","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1148\/radiol.2017162464","article-title":"When imaging becomes clinically relevant: multikinase inhibitor-related pancreatic insufficiency and pancreatic atrophy","volume":"282","author":"Tselikas","year":"2017","journal-title":"Radiology"},{"issue":"2","key":"2024061918464461800_tqae091-B33","doi-asserted-by":"crossref","first-page":"e200029","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"}],"container-title":["British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjr\/advance-article-pdf\/doi\/10.1093\/bjr\/tqae091\/57861232\/tqae091.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/97\/1159\/1268\/58280422\/tqae091.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/97\/1159\/1268\/58280422\/tqae091.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T18:48:08Z","timestamp":1718822888000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/97\/1159\/1268\/7668441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":33,"journal-issue":{"issue":"1159","published-online":{"date-parts":[[2024,5,10]]},"published-print":{"date-parts":[[2024,6,18]]}},"URL":"https:\/\/doi.org\/10.1093\/bjr\/tqae091","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,5,10]]}}}