Use of neural network algorithms for the automated arrangement of cephalometric markers on lateral cefalograms.

Muraev A.A., Kibardin I.A., Oborotistov N.Yu., Ivanov S.S.

The lateral cephalogram analysis is an important method of a research in orthodontics and maxillofacial surgery that allows to obtain necessary diagnostic information on a structure of brain and facial skull parts for treatment planning. There are numerous techniques of the analysis based on processing of cephalometric points (markers) on the lateral cephalogram. Such approaches take a considerable time for the doctor to arrange cephalometric points. Modern digital technologies with use of artificial intelligence allow to improve this method of a research and to significantly simplify doctor’s work. Purpose. In this paper, we propose a neural network and training strategy that ena-bles to place cephalometric points on the lateral cephalogram with high precision. Materials and methods. The research used 80 lateral cephalograms of the head. Results. The developed method handles the cephalograms regardless of the source of the image, while error percentage below 2% of the size of the images. The offered approach demands 2-3 times less time than a traditional "manual" method of arrangement of cepha-lometric points, depending on quantity of points and complexity of the cephalometric analy-sis.
1 - The Peoples’ Friendship University of Russia, 2 – Moscow Physics and Technical University 3 – A.I. Evdokimov Moscow State Medical Dentistry University 4 – I.M. Sechenov Moscow State Medical University



 

Keywords: cephalometric analysis; artificial neural network; cephalogram.

 

Corresponding author:Aleksandr A. Muraev; E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

 

For citation: For citation: Muraev A.A., Kibardin I.A., Oborotistov N.Yu., Ivanov S.S. Use of neural network algorithms for the automated arrangement of cephalometric markers on lateral ce-falograms. REJR 2018; 8(4):16-22. DOI:10.21569/2222-7415-2018-8-2-16-22.

Received:13.10.18Accepted:11.11.18