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Towards a Smart Dental Healthcare: An Automated Assessment of Orthodontic Treatment Need

Authors:
Seiya Murata
Kobo Ishigaki
Chonho Lee
Chihiro Tanikawa
Susumu Date
Takashi Yoshikawa

Keywords: Orthodontic treatment; Diagnostic imaging; Deep learning.

Abstract:
With increasing demands for dental healthcare becoming one of the regular life health factors, this work focuses on the automation of diagnostic imaging in the field of orthodontics. The automated diagnostic imaging of oral images can evaluate the severity of malocclusion and jaw abnormality, and it is beneficial for both doctors reducing their workload and patients periodically performing self-assessment without visiting clinics. In this paper, we propose a deep learning-based model that assesses oral images and gives the severity of orthodontic treatment need. Unlike a traditional image classification model, the proposed model successfully deals with the case that one class label (e.g., the severity score) is assigned to a set of images (e.g., oral images of a patient). The experimental results show that the proposed model improves the classification accuracy by 11% (18% in the best) compared to other conventional models.

Pages: 35 to 39

Copyright: Copyright (c) IARIA, 2017

Publication date: October 8, 2017

Published in: conference

ISSN: 2519-8491

ISBN: 978-1-61208-597-5

Location: Athens, Greece

Dates: from October 8, 2017 to October 12, 2017