AUTOMATIC PLACEMENT OF PRECISION CUTS
    22.
    发明申请
    AUTOMATIC PLACEMENT OF PRECISION CUTS 审中-公开
    自动放置精密CUTS

    公开(公告)号:US20140120490A1

    公开(公告)日:2014-05-01

    申请号:US14148453

    申请日:2014-01-06

    Abstract: An orthodontic positioning device and methods for making an orthodontic positioning device including a first patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in one of an upper jaw and a lower jaw. The first appliance includes a hook configured to receive an orthodontic elastic band. The orthodontic positioning device also includes a second patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in the other of the upper jaw and the lower jaw. The second appliance includes a cutout operable to expose an orthodontic elastic band receiving member.

    Abstract translation: 一种正畸定位装置和用于制造正畸定位装置的方法,所述矫正定位装置包括第一患者可移除矫正牙齿定位装置,其具有接收空腔的牙齿接收空腔,所述牙齿接收空腔被成形为接收并且向设置在上颌和下颚之一中的一个中的患者牙齿施加弹性定位力。 第一器具包括构造成接收正畸弹性带的钩。 正畸定位装置还包括第二患者可移除的正畸牙定位装置,其具有接收空腔的牙齿接收空腔,该空腔成形为接收并向设置在上颌和下颌中的另一个中的患者牙齿施加弹性定位力。 第二器具包括可操作以暴露正畸弹性带接收构件的切口。

    Method and apparatus for excessive materials removal from intraoral scans

    公开(公告)号:US11455727B2

    公开(公告)日:2022-09-27

    申请号:US16837960

    申请日:2020-04-01

    Abstract: A method includes determining the following for each point in a second intraoral scan based on a comparison of a first intraoral scan to the second intraoral scan: whether a surface normal at the point is co-directional with a viewing direction associated with the first intraoral scan, wherein points in the second intraoral scan that have a surface normal that is co-directional with the first viewing direction are back-face points; and whether the point is behind a corresponding point in the first intraoral scan, wherein points in the second intraoral scan that are not behind corresponding points in the first intraoral scan are uncovered points. The method further includes determining a region comprising a plurality of points in the second intraoral scan that are uncovered back-face points, determining that the region satisfies one or more removal criteria, and removing the region from the second intraoral.

    Excess material removal using machine learning

    公开(公告)号:US11238586B2

    公开(公告)日:2022-02-01

    申请号:US16865162

    申请日:2020-05-01

    Abstract: A method includes processing an input comprising data from an intraoral image using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the intraoral image, a first probability that the pixel belongs to a first dental class and a second probability that the pixel belongs to a second dental class, wherein the first dental class represents excess material, the excess material comprising material other than teeth or gums. The method further includes determining, based on the probability map, one or more pixels in the intraoral image that are classified as excess material. The method further includes hiding or removing from the intraoral image data for the one or more pixels that are classified as excess material.

    Identification of intraoral areas of interest

    公开(公告)号:US10898077B2

    公开(公告)日:2021-01-26

    申请号:US16923990

    申请日:2020-07-08

    Abstract: In embodiments, a first 3D model of a patient's teeth based on first intraoral scan data taken at a first time is processed to recognize the teeth in the first 3D model. A second 3D model of the patient's teeth based on second intraoral scan data taken at a second time is processed to recognize the teeth in the second 3D model. The first 3D model is compared to the second 3D model and a plurality of AOIs in the second virtual 3D model representative of at least one of tooth wear, tooth breakage, tooth movement, gingival recession or gingival swelling are determined based on the comparison. The teeth recognized in the first 3D model are compared to the teeth recognized in the second 3D model, and one or more AOIs in the second virtual 3D model representative of tooth wear or tooth breakage are determined based on the comparison.

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