Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10442
Title: Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography
Authors: Özkaya, Emre
Topal, Fatih Esad
Bulut, Tuğrul
Gürsoy, Merve
Özuysal, Mustafa
Karakaya, Zeynep
Özuysal, Mustafa
Keywords: Scaphoid
Fracture
Deep learning
Artificial intelligence
Radiography
Issue Date: 2020
Publisher: Springer Verlag
Abstract: Purpose 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.
Description: PubMed: 32862314
URI: https://doi.org/10.1007/s00068-020-01468-0
https://hdl.handle.net/10442
ISSN: 1863-9933
1863-9941
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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