Visualizations for dental diagnostics

Patent metadata

Field	Value
Publication number	US-2025099061-A1
Application number	US-202418895013-A
Country	US
Kind code	A1
Filing date	Sep 24, 2024
Priority date	Sep 25, 2023
Publication date	Mar 27, 2025
Grant date	—

Abstract

A method includes receiving image data of a current state of a dental site of a patient and processing the image data using a segmentation pipeline to generate an output comprising segmentation information for one or more teeth in the image data and at least one of identifications or locations of one or more oral conditions observed in the image data, wherein each of the one or more oral conditions is associated with a tooth of the one or more teeth. The method includes generating a visual overlay comprising visualizations for each of the one or more oral conditions, outputting the image data to a display, and outputting the visual overlay to the display over the image data.

First claim

What is claimed is:

1 . A method comprising:
receiving image data of a current state of a dental site of a patient; processing the image data using a segmentation pipeline to generate an output comprising segmentation information for one or more teeth in the image data and at least one of identifications or locations of one or more oral conditions observed in the image data, wherein each of the one or more oral conditions is associated with a tooth of the one or more teeth; generating a visual overlay comprising visualizations for each of the one or more oral conditions; outputting the image data to a display; and outputting the visual overlay to the display over the image data.

2 . The method of claim 1 , wherein the image data comprises a radiograph.

3 . The method of claim 2 , wherein processing the image data using the segmentation pipeline comprises:
processing the image data using one or more first trained machine learning models to generate a first output comprising the segmentation information for the one or more teeth in the image data; and processing the image data using one or more additional trained machine learning models to generate a second output comprising at least one of the identifications or the locations of the one or more oral conditions.

4 . The method of claim 3 , wherein for an oral condition of the one or more oral conditions an additional trained machine learning model of the one or more additional trained machine learning models outputs a bounding box for an instance of the oral condition, the method further comprising:
determining a tooth associated with the bounding box; determining an intersection of data from the bounding box and a segmentation mask for the tooth from the segmentation information; and determining a pixel-level mask for the instance of the oral condition based at least in part on the intersection of the data from the bounding box and the segmentation mask.

5 . The method of claim 4 , wherein the oral condition comprises a caries or a restoration, the method further comprising:
subtracting the data from the bounding box that does not intersect with the segmentation mask; wherein the pixel-level mask is provided as a layer of the visual overlay representing the oral condition within the tooth.

6 . The method of claim 4 , wherein the oral condition comprises calculus, the method further comprising:
drawing an ellipse within the bounding box, wherein the intersection of the data from the bounding box and the segmentation mask comprises an intersection of the ellipse and the segmentation mask; and subtracting the data from the bounding box that intersects with the segmentation mask; wherein the pixel-level mask is provided as a layer of the visual overlay representing the calculus around the tooth.

7 . The method of claim 3 , wherein for an oral condition of the one or more oral conditions an additional trained machine learning model of the one or more additional trained machine learning models outputs a plurality of bounding boxes for an instance of the oral condition, the method further comprising:
determining that a first bounding box of the plurality of bounding boxes encapsulates one or more additional bounding boxes of the plurality of bounding boxes; and removing the one or more additional bounding boxes.

8 . The method of claim 3 , wherein for an oral condition of the one or more oral conditions an additional trained machine learning model of the one or more additional trained machine learning models outputs a bounding box for an instance of the oral condition, the method further comprising:
determining a tooth associated with the bounding box; determining an overlap between the bounding box and a segmentation mask for the tooth from the segmentation information; and determining a location of the oral condition on the tooth based at least in part on the overlap.

9 . The method of claim 8 , wherein determining the location comprises:
performing principal component analysis of the segmentation mask or a bounding box for the tooth from the segmentation information to determine at least a first principal component; determining a first line between a tooth occlusal surface and a tooth root apex based on the first principal component; and determining a first portion of the bounding box that is on a mesial side of the first line and a second portion of the bounding box that is on a distal side of the first line; and determining whether the oral condition is on the mesial side of the tooth or the distal side of the tooth based on the first portion and the second portion.

10 . The method of claim 2 , herein each instance of one or more oral conditions is provided as a distinct layer of the visual overlay, the method further comprising:
generating a dental chart for the patient; populating the dental chart based on data for the one or more oral conditions; and outputting the dental chart to the display.

11 . The method of claim 10 , wherein each instance of one or more oral conditions is provided as a distinct layer of the visual overlay, the method further comprising:
receiving a selection of a tooth based on user interaction with at least one of the tooth in the dental chart or the tooth in the image data; outputting detailed information for instances of each of the one or more oral conditions identified for the selected tooth; receiving an instruction to remove an instance of an oral condition of the tooth; and marking the tooth as not having the instance of the oral condition.

12 . The method of claim 1 , wherein the one or more oral conditions comprise one or more instances of caries, the method further comprising:
for each instance of caries, determining whether the instance of the caries is an enamel caries or a dentin caries; marking instances of caries identified as enamel caries using a first visualization; and marking instances of caries identified as dentin caries using a second visualization.

13 . The method of claim 1 , further comprising:
determining dental codes associated with the one or more oral conditions; assigning the dental codes to the one or more oral conditions; determining a treatment that was performed on the patient; and automatically generating an insurance claim for the treatment, the insurance claim comprising the image data, at least a portion of the visual overlay comprising the one or more oral conditions that were treated, and the dental codes associated with the one or more oral conditions.

14 . The method of claim 1 , wherein the at least one of the identifications or the locations of the one or more oral conditions comprises a probability map indicating, for each pixel of the image data, a probability of the pixel corresponding to at least one oral condition of the one or more oral conditions, the method further comprising:
determining, for the at least one oral condition and for a first tooth, a pixel-level mask indicating pixels having a probability that exceeds a first threshold, wherein the visual overlay comprises the pixel-level mask for the first tooth.

15 . The method of claim 14 , further comprising:
receiving an instruction to activate a high sensitivity mode for oral condition detection; activating the high sensitivity mode, wherein activating the high sensitivity mode comprises replacing the first threshold with a second threshold that is lower than the first threshold; and determining, for the at least one oral condition and for the first tooth, a new pixel-level mask indicating pixels having a probability that exceeds the second t

Assignees

• Align Technology Inc

Inventors

• Kolli Sreelakshmi
• Patki Ema
• Cramer Christopher E
• Krois Joachim
• Dreher Martin
• Schwendicke Falk

Classifications

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T7/11

  Region-based segmentation · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title

• A61B6/5229

  combining image data of a patient, e.g. combining a functional image with an anatomical image · CPC title