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dc.contributor.authorLitvin, Andrey
dc.contributor.authorKorenev, Sergey
dc.contributor.authorRumovskaya, Sophiya
dc.contributor.authorSartelli, Massimo
dc.contributor.authorBaiocchi, Gianluca
dc.contributor.authorBiffl, Walter L
dc.contributor.authorCoccolini, Federico
dc.contributor.authorDi Saverio, Salomone
dc.contributor.authorKelly, Michael Denis
dc.contributor.authorKluger, Yoram
dc.contributor.authorLeppäniemi, Ari
dc.contributor.authorSugrue, Michael
dc.contributor.authorCatena, Fausto
dc.date.accessioned2021-10-29T02:12:39Z
dc.date.available2021-10-29T02:12:39Z
dc.date.issued2021-09-26
dc.identifier.issn1749-7922
dc.identifier.otherPMC8474926
dc.identifier.other34565420
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330020
dc.descriptionFunder: Università degli Studi di Brescia
dc.description.abstractThe article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 1749-7922
dc.sourcenlmid: 101266603
dc.subjectArtificial neural networks
dc.subjectDecision support system
dc.subjectacute pancreatitis
dc.subjectBowel Obstruction
dc.subjectacute cholecystitis
dc.subjectPeptic Ulcer Bleeding
dc.subjectAcute Appendicitis
dc.subjectEmergency Surgery
dc.subjectPerforated Gastroduodenal Ulcers
dc.subjectStrangulated Hernias
dc.titleWSES project on decision support systems based on artificial neural networks in emergency surgery.
dc.typeArticle
dc.date.updated2021-10-29T02:12:38Z
prism.issueIdentifier1
prism.publicationNameWorld J Emerg Surg
prism.volume16
dc.identifier.doi10.17863/CAM.77464
dcterms.dateAccepted2021-09-13
rioxxterms.versionofrecord10.1186/s13017-021-00394-9
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidLitvin, Andrey [0000-0002-9330-6513]
dc.identifier.eissn1749-7922
cam.issuedOnline2021-09-26


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International