An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography.

Journal: International journal of medical informatics
PMID: 39626596

Abstract

OBJECTIVE: This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic radiography (PR). The proposed architecture seeks to enhance the accuracy of early detection and improve clinical decision-making and treatment planning in dentistry.

Authors

  • İsmail Kayadibi
    Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Isparta, Turkey; Department of Management Information Systems, Faculty of Economic and Administrative Sciences, Afyon Kocatepe University, Afyonkarahisar, Turkey. Electronic address: ikayadibi@aku.edu.tr.
  • Utku Köse
    Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey.
  • Gür Emre Güraksın
    Department of Computer Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey. Electronic address: emreguraksin@aku.edu.tr.
  • Bilgün Çetin
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, Konya, Turkey. Electronic address: bcetin@selcuk.edu.tr.

Keywords

External Resources

Popular Topics
Recent Journals