An early warning system using machine learning for the detection of intracranial hematomas in the emergency trauma setting

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dc.authorid0000-0002-5971-9218
dc.authorid0000-0001-8863-8348
dc.contributor.authorAydoseli, Aydın
dc.contributor.authorÜnal, Tuğrul Cem
dc.contributor.authorKardeş, Onur
dc.contributor.authorDoğuç, Özge
dc.contributor.authorDolaş, İlyas
dc.contributor.authorAdıyaman, Ali Ekrem
dc.contributor.authorOrtahisar, Emircan
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorAras, Yavuz
dc.contributor.authorSabancı, Pulat Akın
dc.contributor.authorSencer, Serra
dc.contributor.authorSencer, Altay
dc.date.accessioned2022-06-06T07:04:03Z
dc.date.available2022-06-06T07:04:03Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
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.
dc.identifier.citationAydoseli, A., Ünal, T. C., Kardeş, O., Doğuç, Ö., Dolaş, İ., Adıyaman, A. E. ... Sencer, A. (2022). An early warning system using machine learning for the detection of intracranial hematomas in the emergency trauma setting. Turkish Neurosurgery, 32(3), 459-465. https://doi.org/10.5137/1019-5149.JTN.35996-21.1
dc.identifier.doi10.5137/1019-5149.JTN.35996-21.1
dc.identifier.endpage465
dc.identifier.issn1019-5149
dc.identifier.issue3
dc.identifier.pmid35179731
dc.identifier.scopus2-s2.0-85130477428
dc.identifier.scopusqualityQ3
dc.identifier.startpage459
dc.identifier.urihttps://doi.org/10.5137/1019-5149.JTN.35996-21.1
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9504
dc.identifier.volume32
dc.identifier.wos000804648400016en_US
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorDoğuç, Özge
dc.institutionauthorSilahtaroğlu, Gökhan
dc.language.isoen
dc.publisherTurkish Neurosurgical Society
dc.relation.ispartofTurkish Neurosurgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Intelligence
dc.subjectEpidural Hematoma
dc.subjectMachine Learning
dc.subjectSubdural Hematoma
dc.subjectTrauma
dc.titleAn early warning system using machine learning for the detection of intracranial hematomas in the emergency trauma setting
dc.typeArticle

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