Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis.

Yangdong Lin,Miao He,Balakrishnan Nagaraj

doi:10.1155/2021/4676316

Abstract

In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.

Highlights

With the rapid development of information technology, a large amount of data has been accumulated in various fields
We studied the deep learning-based diagnosis of neck and facial diseases in order to meet the challenges of 3D cone beam computed tomography (CBCT) data
On the basis of current research, we studied the deep learning-based diagnosis of neck and facial diseases to meet the challenges of 3D cone beam computed tomography (CBCT) data

Summary

Introduction

With the rapid development of information technology, a large amount of data has been accumulated in various fields. Most of the current medical image studies are focused on two-dimensional images and most of the parts studied are concentrated on human viscera and eye areas [2] Digital technologies such as CAD/CAM, rapid prototyping, and “3D printing” are all based on CT and CBCT image data for reference in the aspects of intelligent processing of dental prostheses and the production of accurate dental implant guide plates. E results on a real data set containing 100 oral CBCT images show that the proposed method achieves better tooth segmentation accuracy than existing methods [6]. On the basis of current research, we studied the deep learning-based diagnosis of neck and facial diseases to meet the challenges of 3D cone beam computed tomography (CBCT) data. As far as we know, this is the first time to study the diagnosis of diseases in oral neck and facial surgery based on deep learning [10]

Methods

Experimental Analysis

Findings

Method Number of labeled data