Development of a software complex for the diagnosis of dentoalveolar anomalies using neural networks

TL;DR

This paper presents the development of a software system that utilizes convolutional neural networks to assist in diagnosing dentoalveolar anomalies from teleradiographic images, aiming to improve treatment planning accuracy.

Contribution

It introduces a novel software complex architecture integrating neural network algorithms for decoding radiographic images in dental diagnostics.

Findings

Effective neural network algorithms for image decoding

Improved accuracy in diagnosing dentoalveolar anomalies

A modular software architecture for dental treatment planning

Abstract

This article describes the goals and objectives of developing a software complex for planning the treatment of dentoalveolar anomalies, the architecture of the software complex as interacting components for treatment planning, as well as the principle of using algorithms using convolutional neural networks within the software complex for a component that solves the problem of decoding a teleradiographic image.