Application of deep learning and feature selection technique on external root resorption identification on CBCT images.

Journal: BMC oral health
PMID: 38373931

Abstract

BACKGROUND: Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification.

Authors

  • Nor Hidayah Reduwan
    Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
  • Azwatee Abdul Aziz
    Department of Restorative Dentistry, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
  • Roziana Mohd Razi
    Department of Pediatric Dentistry and Orthodontic, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
  • Erma Rahayu Mohd Faizal Abdullah
    Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. erma@um.edu.my.
  • Seyed Matin Mazloom Nezhad
    Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
  • Meghna Gohain
    Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
  • Norliza Ibrahim
    Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. norlizaibrahim@um.edu.my.

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