Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14193
Title: Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography
Authors: Ozkaya, E.
Topal, F.E.
Bulut, T.
Gursoy, M.
Ozuysal, M.
Karakaya, Z.
Keywords: Artificial intelligence
Deep learning
Fracture
Radiography
Scaphoid
artificial intelligence
diagnostic imaging
fracture
human
radiography
scaphoid bone
sensitivity and specificity
Artificial Intelligence
Fractures, Bone
Humans
Radiography
Scaphoid Bone
Sensitivity and Specificity
Publisher: Springer Science and Business Media Deutschland GmbH
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 0.826 F score 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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
URI: https://doi.org/10.1007/s00068-020-01468-0
https://hdl.handle.net/11147/14193
ISSN: 1863-9933
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record



CORE Recommender

SCOPUSTM   
Citations

24
checked on Apr 5, 2024

Page view(s)

46
checked on Apr 8, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.