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Automated orthodontic treatment planning using deep learning

A deep neural network and computer technology, applied in the field of computer program products and training deep neural networks, can solve frequent updates and other problems

Active Publication Date: 2021-04-09
PROMATON HLDG BV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] A disadvantage of the method disclosed in patent document US 2017 / 0100212A1 is that the orthodontic treatment plan usually needs to be updated at least once during treatment, and possibly even more frequently

Method used

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  • Automated orthodontic treatment planning using deep learning
  • Automated orthodontic treatment planning using deep learning
  • Automated orthodontic treatment planning using deep learning

Examples

Experimental program
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Embodiment Construction

[0071] figure 1 A first embodiment of the method for training a deep neural network of the present invention is shown. Step 101 comprises obtaining a plurality of training dental computed tomography scans 111 reflecting moments preceding a corresponding successful orthodontic treatment. For example, the training dental computed tomography scan 111 may be a scan originally produced by a (CB)CT scanner or a voxel representation derived therefrom. Step 103 includes identifying individual teeth and jaws in each of the training dental computed tomography scans 111 . This identification is included in the training input data 113 . The training input data 113 also includes data representing all teeth and the entire alveolar process, which data may be the training dental computed tomography 111 , parts thereof or 3D datasets derived therefrom. Step 105 includes training the deep neural network using the training data 113 and target training data 115 for each CT scan.

[0072] fi...

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Abstract

A method of the invention comprises obtaining training dental CTscans, identifying individual teeth and jaw bone in each of these CTscans, and training a deep neural network with training input data obtained from these CTscans and training target data.A further method of the invention comprises obtaining(203) a patient dental CTscan, identifying(205) individual teeth and jaw bone in this CTscan and using(207) the trained deep learning network to determine a desired final position from input data obtained from this CTscan. The (training) input data represents all teeth and the entire alveolar process and identifies the individual teeth and the jaw bone.The determined desired final positions are used to determine a sequence of desired intermediate positions per tooth and the intermediate and final positions and attachment types are used to create three-dimensional representations of teeth and / or aligners.

Description

technical field [0001] The invention relates to an automated system for determining an orthodontic treatment plan. [0002] The invention also relates to an automated method of determining an orthodontic treatment plan and a method of training a deep neural network. [0003] The invention also relates to a computer program product enabling a computer system to perform such a method. Background technique [0004] Orthodontic treatment moves a patient's teeth from an initial position (ie, the position before treatment began) to a desired position in order to move the teeth into proper alignment. Traditionally, orthodontic treatment is performed using braces consisting of arch wires and metal brackets. Braces need to be adjusted several times by the orthodontist. Using a range of aligners, ie a range of templates, is now a popular choice due to their aesthetics and comfort. [0005] For the purposes of this disclosure, 'tooth' means the entire tooth including the crown and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61C7/00G06T1/00G06K9/46G16H50/20G16H20/40G16H50/70G06V10/764
CPCG16H20/40G16H50/20G16H50/70A61C7/002A61C7/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30036G06T7/0012G06V10/454G06V2201/033G06V10/82G06V10/764G06F18/2414A61C9/0053A61B5/0088G06N20/00G06N3/08G16H30/00G16H30/40G16H50/50G16H20/30G16H20/10A61B2018/20353G16H10/60G06T7/168G06N3/082
Inventor D·安萨里莫因F·T·C·克莱森
Owner PROMATON HLDG BV
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