Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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  • Article
    Citation - WoS: 55
    Citation - Scopus: 56
    Evaluation of an Artificial Intelligence System for Diagnosing Scaphoid Fracture on Direct Radiography
    (Springer Verlag, 2020) Özkaya, Emre; Topal, Fatih Esad; Bulut, Tuğrul; Gürsoy, Merve; Özuysal, Mustafa; Karakaya, Zeynep
    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.
  • Article
    Citation - WoS: 30
    Citation - Scopus: 34
    Gddom: an Online Tool for Calculation of Dominant Marker Gene Diversity
    (Springer Verlag, 2017) Abuzayed, Mazen; El-Dabba, Nourhan; Frary, Anne; Doğanlar, Sami
    Gene diversity (GD), also called polymorphism information content, is a commonly used measure of molecular marker polymorphism. Calculation of GD for dominant markers such as AFLP, RAPD, and multilocus SSRs is valuable for researchers. To meet this need, we developed a free online computer program, GDdom, which provides easy, quick, and accurate calculation of dominant marker GD with a commonly used formula. Results are presented in tabular form for quick interpretation. © 2016, Springer Science+Business Media New York.