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An automatic orthodontic difficulty coefficient evaluation method

A technology of difficulty coefficient and parameter evaluation, applied in neural learning methods, image data processing, image analysis, etc., to improve accuracy and facilitate large-scale promotion and application

Active Publication Date: 2022-06-17
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in terms of orthodontic difficulty evaluation, there are no reports on the automatic orthodontic difficulty coefficient evaluation system at home and abroad.

Method used

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  • An automatic orthodontic difficulty coefficient evaluation method
  • An automatic orthodontic difficulty coefficient evaluation method
  • An automatic orthodontic difficulty coefficient evaluation method

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Embodiment Construction

[0071] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. The drawings are only for the purpose of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented in accordance with some of the embodiments of this application. It should be understood that the operations of the flowcharts may be performed out of order and that steps without logical context may be performed in reverse order or concurrently. In addition, those skilled in the art can add one or more other operations to the flowchart, and ca...

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Abstract

The invention discloses an automatic orthodontic difficulty coefficient evaluation method, comprising: obtaining a tooth model of the oral cavity to be evaluated, and optimizing the data of the tooth model to generate first tooth data; extracting details from the first tooth data The data features of the granularity are used as the second tooth data; the second tooth data is segmented and smoothed to form the third tooth data; a plurality of orthodontic difficulty evaluation parameters are generated according to the third tooth data to evaluate the orthodontic difficulty. The automatic orthodontic difficulty coefficient evaluation method of the present invention fully digitally and automatically processes the orthodontic difficulty coefficient evaluation, effectively improves the accuracy of the orthodontic difficulty evaluation, does not rely on the experience judgment of doctors, and is conducive to large-scale popularization and application.

Description

technical field [0001] The invention relates to the field of computer application technology and computer vision, in particular to an automatic orthodontic difficulty coefficient evaluation method. Background technique [0002] Malocclusion is one of the most common oral diseases in modern humans. In malocclusion, objective and quantitative assessment of the difficulty of individual orthodontic treatment is the cornerstone of clinical diagnosis and treatment. The promotion of orthodontic difficulty assessment is conducive to the planning and decision-making of hierarchical diagnosis and treatment, optimizes the utilization of medical resources, and can provide guidance for clinical audit activities. [0003] At present, the commonly used orthodontic difficulty assessment indices in the world include the Discrepancy Index (DI), the peer assessment grade index, and the treatment complexity index. The DI is a reliable index developed by the American Board of Orthodontics to a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/80G06T5/00G06K9/62G06N3/04G06N3/08G06V10/77
CPCG06T7/0012G06T7/11G06T7/80G06T5/002G06N3/08G06T2207/30036G06N3/045G06F18/2135
Inventor 赵启军吴晓刘钧韦明颖
Owner SICHUAN UNIV
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