The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 26, 2022

Filed:

Jun. 25, 2020
Applicant:

Retrace Labs, San Francisco, CA (US);

Inventors:

Vasant Kearney, San Francisco, CA (US);

Ali Sadat, San Francisco, CA (US);

Assignee:

Retrace Labs, San Francisco, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); A61B 5/00 (2006.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01); G16H 50/70 (2018.01); G16H 70/20 (2018.01); G16H 20/30 (2018.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06K 9/62 (2022.01); A61B 1/24 (2006.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 6/14 (2006.01); A61B 6/00 (2006.01); A61B 1/00 (2006.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 1/00009 (2013.01); A61B 1/24 (2013.01); A61B 5/0088 (2013.01); A61B 5/055 (2013.01); A61B 6/032 (2013.01); A61B 6/14 (2013.01); A61B 6/4085 (2013.01); A61B 6/5217 (2013.01); G06K 9/6257 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G16H 20/30 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/20 (2018.01); G16H 70/60 (2018.01); G06K 2209/05 (2013.01);
Abstract

Training a generator includes processing a dental image using the generator to obtain a synthetic pathology label, such has a pixel mask indicating portions of the dental image representing caries. The synthetic pathology label is compared to a target pathology label for the dental image and the generator is updated according to the comparison. The synthetic pathology may be evaluated by a discriminator along with a real pathology label to obtain a realism estimate. The discriminator and generator may be updated according to accuracy of the realism estimate. Inputs to the generator may further include tooth labels and/or labels of restorations. Machine learning models may be trained to label restorations and defects in restorations. A machine learning model may be trained to identify the surface of a tooth having a pathology thereon.


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