{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T12:44:24Z","timestamp":1794314664843,"version":"4.9.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1122","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,1]]},"abstract":"<jats:sec>\n                  <jats:title>Objectives:<\/jats:title>\n                  <jats:p>Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are frequently used as a first-line test but the availability of immediate expert radiological review is variable. The aim was to investigate the feasibility of using a deep learning model for automated identification of small bowel obstruction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>A total of 990 plain abdominal radiographs were collected, 445 with normal findings and 445 demonstrating small bowel obstruction. The images were labelled using the radiology reports, subsequent CT scans, surgical operation notes and enhanced radiological review. The data were used to develop a predictive model comprising an ensemble of five convolutional neural networks trained using transfer learning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>The performance of the model was excellent with an area under the receiver operator curve (AUC) of 0.961, corresponding to sensitivity and specificity of 91 and 93% respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>Deep learning can be used to identify small bowel obstruction on plain radiographs with a high degree of accuracy. A system such as this could be used to alert clinicians to the presence of urgent findings with the potential for expedited clinical review and improved patient outcomes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>This paper describes a novel labelling method using composite clinical follow-up and demonstrates that ensemble models can be used effectively in medical imaging tasks. It also provides evidence that deep learning methods can be used to identify small bowel obstruction with high accuracy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjr.20201407","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T12:58:07Z","timestamp":1619528287000},"source":"Crossref","is-referenced-by-count":35,"title":["An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs"],"prefix":"10.1093","volume":"94","author":[{"given":"DH","family":"Kim","sequence":"first","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H","family":"Wit","sequence":"additional","affiliation":[{"name":"The Medical Imaging Department, University Hospitals Plymouth NHS Trust , Plymouth , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M","family":"Thurston","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M","family":"Long","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"},{"name":"The Medical Imaging Department, University Hospitals Plymouth NHS Trust , Plymouth , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"GF","family":"Maskell","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MJ","family":"Strugnell","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D","family":"Shetty","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"IM","family":"Smith","sequence":"additional","affiliation":[{"name":"The Department of General Surgery, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"NP","family":"Hollings","sequence":"additional","affiliation":[{"name":"The Department of Clinical Imaging, The Royal Cornwall Hospitals NHS Trust , Truro , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"2024050215471793700_b1","first-page":"623","article-title":"Current management of small bowel obstruction in the UK: results from the National audit of small bowel obstruction clinical practice survey","volume":"7","author":"Leem","year":"2018","journal-title":"Colorectal dis"},{"key":"2024050215471793700_b2","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/j.rcl.2015.06.008","article-title":"Bowel obstruction","volume":"53","author":"Gore","year":"2015","journal-title":"Radiol Clin North Am"},{"key":"2024050215471793700_b3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s13017-018-0208-z","article-title":"Small bowel obstruction in the elderly: a plea for comprehensive acute geriatric care","volume":"13","author":"Ozturk","year":"2018","journal-title":"World J Emerg Surg"},{"key":"2024050215471793700_b4","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1148\/radiol.15131519","article-title":"Review of small-bowel obstruction: the diagnosis and when to worry","volume":"275","author":"Paulson","year":"2015","journal-title":"Radiology"},{"key":"2024050215471793700_b5","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1097\/00000658-200004000-00012","article-title":"Complications and death after surgical treatment of small bowel obstruction: a 35-year institutional experience","volume":"231","author":"Fevang","year":"2000","journal-title":"Ann Surg"},{"key":"2024050215471793700_b6","first-page":"565","article-title":"The importance of early diagnosis of small bowel obstruction","volume":"54","author":"Cheadle","year":"1988","journal-title":"Am Surg"},{"key":"2024050215471793700_b7","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1503\/cjs.029513","article-title":"Management and outcomes of small bowel obstruction in older adult patients: a prospective cohort study","volume":"57","author":"Springer","year":"2014","journal-title":"Can J Surg"},{"key":"2024050215471793700_b8","unstructured":"American College of Radiology.\n          \n\u00a0ACR appropriateness criteria. Suspected small-bowel obstruction. American College of Radiology\n.\n1996\n. Available from:\nhttps:\/\/acsearch.acr.org\/docs\/69476\/Narrative\/\n."},{"key":"2024050215471793700_b9","unstructured":"Royal College of Radiologists.\n          \n\u00a0iRefer: Making the best use of clinical radiology, 8th edition. Guideline G11 Acute small bowel obstruction: confirmation and assessment of level\n.\n2019\n. Available from:\nhttps:\/\/www.irefer.org.uk\/guidelines\n."},{"key":"2024050215471793700_b10","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1148\/rg.292085514","article-title":"Small bowel obstruction: what to look for","volume":"29","author":"Silva","year":"2009","journal-title":"Radiographics"},{"key":"2024050215471793700_b11","unstructured":"The Royal College of Radiologist.\n          \n\u00a0Clinical radiology UK workforce census\n.\n2020\n. Available from:\nhttps:\/\/www.rcr.ac.uk\/clinical-radiology\/service-delivery\/rcr-radiology-workforce-census\n."},{"key":"2024050215471793700_b12","doi-asserted-by":"crossref","first-page":"W233","DOI":"10.2214\/AJR.06.0817","article-title":"Accuracy of abdominal radiography in acute small-bowel obstruction: does reviewer experience matter?","volume":"188","author":"Thompson","year":"2007","journal-title":"AJR Am J Roentgenol"},{"key":"2024050215471793700_b13","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12194-017-0406-5","article-title":"Overview of deep learning in medical imaging","volume":"10","author":"Suzuki","year":"2017","journal-title":"Radiol Phys Technol"},{"key":"2024050215471793700_b14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"2024050215471793700_b15","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2024050215471793700_b16","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2024050215471793700_b17","doi-asserted-by":"crossref","first-page":"213","DOI":"10.32614\/RJ-2016-042","article-title":"easyROC: an interactive web-tool for ROC curve analysis using R language environment","volume":"8","author":"Goksuluk","year":"2016","journal-title":"R J"},{"key":"2024050215471793700_b18","unstructured":"Chollet \u00a0F\n          \n.\nKeras: the Python deep learning library\n.\n2015\n. Available from:\nhttps:\/\/keras.io\n."},{"key":"2024050215471793700_b19","unstructured":"Abadi \u00a0M\n\u00a0Agarwal \u00a0A\n\u00a0Brevdo \u00a0P\n.\nTensorflow: large-scale machine learning on heterogenous systems\n.\n2015\n. Available from:\nhttps:\/\/www.tensorflow.org\/\n."},{"key":"2024050215471793700_b20","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"2024050215471793700_b21","first-page":"arXiv:1409.1556","article-title":"Very deep Convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"2024050215471793700_b22","first-page":"arXiv:1608.06993","article-title":"Densely connected Convolutional networks","author":"Huang","year":"2016"},{"key":"2024050215471793700_b23","first-page":"arXiv:1707.07012","article-title":"Learning transferable architectures for scalable image recognition","author":"Zoph","year":"2017"},{"key":"2024050215471793700_b24","first-page":"arXiv:1512.00567","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2015"},{"key":"2024050215471793700_b25","first-page":"arXiv:1610.02357","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2016"},{"key":"2024050215471793700_b26","doi-asserted-by":"crossref","unstructured":"Dong W, Socher R, Li LJ, Li K, Fei-Fei L.\n          \n\u00a0ImageNet: a large-scale hierarchical image database. 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