An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting

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dc.contributor.authorAydoseli, Aydin
dc.contributor.authorUnal, Tugrul Cem
dc.contributor.authorKardes, Onur
dc.contributor.authorDoguc, Ozge
dc.contributor.authorDolas, Ilyas
dc.contributor.authorAdiyaman, Ali Ekrem
dc.contributor.authorOrtahisar, Emircan
dc.date.accessioned2024-03-13T10:33:23Z
dc.date.available2024-03-13T10:33:23Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractAIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting. MATERIAL and METHODS: A total of 150 sets of cranial computed tomography (CT) scans were used in this study with a total of 11,025 images. Of the CTs, 75 were labeled as EAH, the remaining 75 were normal. A random forest algorithm was utilized for the detection of EAHs. The CTs were randomized into two groups: 100 samples for training of the algorithm (split evenly between EAH and normal cases), and 50 samples for testing. In the training phase, the algorithm scanned every CT slice separately for image features such as entropy, moment, and variance. If the algorithm determined an EAH on two or more images in a CT set, then the workflow produced an alert in the form of an email. RESULTS: Data from 50 patients (25 EAH and 25 controls) were used for testing the EWS. For all CTs with an EAH, an alert was produced, with a 0% false-negative rate. For 16% of the cases, the practitioner received an email from the EWS that the patient might have an EAH despite having a normal CT scan. Positive and negative predictive values were 86% and 100%, respectively. CONCLUSION: An EWS based on a machine learning algorithm is an efficient and inexpensive way of facilitating the work of emergency practitioners such as emergency physicians, neuroradiologists, and neurosurgeons.en_US
dc.identifier.doi10.5137/1019-5149.JTN.35996-21.1
dc.identifier.endpage465en_US
dc.identifier.issn1019-5149
dc.identifier.issue3en_US
dc.identifier.pmid35179731en_US
dc.identifier.scopus2-s2.0-85130477428
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage459en_US
dc.identifier.urihttps://doi.org/10.5137/1019-5149.JTN.35996-21.1
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3932
dc.identifier.volume32en_US
dc.identifier.wosWOS:000804648400016
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherTurkish Neurosurgical Socen_US
dc.relation.ispartofTurkish Neurosurgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectEpidural hematomaen_US
dc.subjectSubdural hematomaen_US
dc.subjectTraumaen_US
dc.titleAn Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Settingen_US
dc.typeArticleen_US

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