US 12,268,574 B2
Diagnostic intraoral methods and apparatuses
Michael Sabina, Campbell, CA (US); Avi Kopelman, Palo Alto, CA (US); Eric Kuo, San Jose, CA (US); Gilad Elbaz, Tel Aviv (IL); Assaf Weiss, Yavne (IL); Doron Malka, Tel Aviv (IL); Ofer Saphier, Rehovot (IL); Eliahou Franklin Nizard, Jerusalem (IL); Ido Tishel, Kfar Bilu (IL); Shai Ayal, Shoham (IL); and Maayan Moshe, Ra'anana (IL)
Assigned to Align Technology, Inc., San Jose, CA (US)
Filed by Align Technology, Inc., San Jose, CA (US)
Filed on Aug. 3, 2023, as Appl. No. 18/365,201.
Application 18/365,201 is a continuation of application No. 17/209,166, filed on Mar. 22, 2021, granted, now 11,759,295.
Application 17/209,166 is a continuation of application No. 16/258,527, filed on Jan. 25, 2019, granted, now 11,013,581, issued on May 25, 2021.
Claims priority of provisional application 62/758,503, filed on Nov. 9, 2018.
Claims priority of provisional application 62/622,798, filed on Jan. 26, 2018.
Prior Publication US 2023/0372069 A1, Nov. 23, 2023
Int. Cl. A61C 9/00 (2006.01); A61B 1/00 (2006.01); A61B 1/06 (2006.01); A61B 1/24 (2006.01); A61B 5/00 (2006.01); A61B 6/51 (2024.01); A61C 7/00 (2006.01); A61C 13/00 (2006.01); G06T 19/00 (2011.01); A61C 13/34 (2006.01)
CPC A61C 9/0053 (2013.01) [A61B 1/000094 (2022.02); A61B 1/000096 (2022.02); A61B 1/00172 (2013.01); A61B 1/00186 (2013.01); A61B 1/00194 (2022.02); A61B 1/0638 (2013.01); A61B 1/0646 (2013.01); A61B 1/24 (2013.01); A61B 5/0086 (2013.01); A61B 5/0088 (2013.01); A61B 6/512 (2024.01); A61C 7/002 (2013.01); A61C 13/0004 (2013.01); G06T 19/00 (2013.01); A61B 5/0062 (2013.01); A61B 5/0066 (2013.01); A61B 5/4547 (2013.01); A61C 13/0019 (2013.01); A61C 13/34 (2013.01); G06T 2200/24 (2013.01); G06T 2207/30036 (2013.01); G06T 2210/41 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An intraoral scanning system, the system comprising:
an imaging sensor; and
one or more processors, the one or more processors having a memory configured to store computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising:
engaging a trained machine learning network to identify one or more dental features in one or more regions of a patient's dentition, wherein each of the one or more dental features is present in at least one of a plurality of records, wherein each of the plurality of records comprises a plurality of images of the patient's dentition, and wherein each of the plurality of records is taken with a different imaging modality;
determining or adjusting a confidence score for the one or more dental features using a three-dimensional (3D) model of the patient's dentition, wherein each of the plurality of records is correlated to the 3D model of the patient's dentition, further wherein the confidence score is determined or adjusted based on the region of the patient's dentition within each of the plurality of records;
displaying an indicator of each dental feature of the one or more dental features that has a confidence score that is above a threshold on the 3D model of the patient's dentition; and
modifying the display as a user adjusts the threshold.