A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography.

Journal: Journal of endodontics
PMID: 34563507

Abstract

INTRODUCTION: The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular second molars from cone-beam computed tomographic (CBCT) volumes and to compare the performance of 3 different architectures.

Authors

  • Adithya A Sherwood
    Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India.
  • Anand I Sherwood
    Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India. Electronic address: anand.sherwood@gmail.com.
  • Frank C Setzer
    Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: fsetzer@upenn.edu.
  • Sheela Devi K
    Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India.
  • Jasmin V Shamili
    Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India.
  • Caroline John
    Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, Florida.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.

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