Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10442
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dc.contributor.authorÖzkaya, Emre-
dc.contributor.authorTopal, Fatih Esad-
dc.contributor.authorBulut, Tuğrul-
dc.contributor.authorGürsoy, Merve-
dc.contributor.authorÖzuysal, Mustafa-
dc.contributor.authorKarakaya, Zeynep-
dc.date.accessioned2021-01-24T18:43:11Z-
dc.date.available2021-01-24T18:43:11Z-
dc.date.issued2020-
dc.identifier.issn1863-9933-
dc.identifier.issn1863-9941-
dc.identifier.urihttps://doi.org/10.1007/s00068-020-01468-0-
dc.identifier.urihttps://hdl.handle.net/10442-
dc.descriptionPubMed: 32862314en_US
dc.description.abstractPurpose The aim of this study is to determine the diagnostic performance of artificial intelligence with the use of convolutional neural networks (CNN) for detecting scaphoid fractures on anteroposterior wrist radiographs. The performance of the deep learning algorithm was also compared with that of the emergency department (ED) physician and two orthopaedic specialists (less experienced and experienced in the hand surgery). Methods A total 390 patients with AP wrist radiographs were included in the study. The presence/absence of the fracture on radiographs was confirmed via CT. The diagnostic performance of the CNN, ED physician and two orthopaedic specialists (less experienced and experienced) as measured by AUC, sensitivity, specificity, F-Score and Youden index, to detect scaphoid fractures was evaluated and compared between the groups. Results The CNN had 76% sensitivity and 92% specificity, 0.840 AUC, 0.680 Youden index and 0.826Fscore values in identifying scaphoid fractures. The experienced orthopaedic specialist had the best diagnostic performance according to AUC. While CNN's performance was similar to a less experienced orthopaedic specialist, it was better than the ED physician. Conclusion The deep learning algorithm has the potential to be used for diagnosing scaphoid fractures on radiographs. Artificial intelligence can be useful for scaphoid fracture diagnosis particularly in the absence of an experienced orthopedist or hand surgeon.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofEuropean Journal of Trauma and Emergency Surgeryen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectScaphoiden_US
dc.subjectFractureen_US
dc.subjectDeep learningen_US
dc.subjectArtificial intelligenceen_US
dc.subjectRadiographyen_US
dc.titleEvaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiographyen_US
dc.typeArticleen_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000564157200001-
dc.identifier.scopusSCOPUS:2-s2.0-85089963570-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s00068-020-01468-0-
dc.relation.doi10.1007/s00068-020-01468-0en_US
dc.coverage.doi10.1007/s00068-020-01468-0en_US
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20240101-
crisitem.author.deptDepartment of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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