US 11,883,219 B2
Artificial intelligence intra-operative surgical guidance system and method of use
Richard Boddington, Salt Lake City, UT (US); Edouard Saget, Boise, ID (US); Joshua Cates, Salt Lake City, UT (US); Hind Oulhaj, Strasbourg (FR); and Erik Noble Kubiak, Las Vegas, NV (US)
Assigned to Orthogrid Systems Holdings, LLC, Salt Lake City, UT (US)
Filed by Orthogrid Systems Holdings, LLC, Salt Lake City, UT (US)
Filed on Aug. 25, 2022, as Appl. No. 17/895,948.
Application 17/895,948 is a division of application No. 17/668,319, filed on Feb. 9, 2022, granted, now 11,540,794.
Application 17/668,319 is a continuation of application No. 16/916,876, granted, now 11,589,928, previously published as PCT/US2019/050745, filed on Sep. 12, 2019.
Claims priority of provisional application 62/730,112, filed on Sep. 12, 2018.
Prior Publication US 2023/0000451 A1, Jan. 5, 2023
Int. Cl. G06T 7/30 (2017.01); A61B 6/00 (2006.01); A61B 6/12 (2006.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 11/00 (2006.01); G06T 7/60 (2017.01)
CPC A61B 6/463 (2013.01) [A61B 6/12 (2013.01); A61B 6/487 (2013.01); A61B 6/505 (2013.01); G06T 7/30 (2017.01); G06T 7/60 (2013.01); G06T 11/00 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2200/24 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/10116 (2013.01); G06V 2201/033 (2022.01); G06V 2201/034 (2022.01)] 8 Claims
OG exemplary drawing
 
1. An artificial intelligence assisted total hip arthroplasty comprising:
providing a computing platform comprised of an at least one image processing algorithm for the classification of a plurality of intra-operative medical images, the computing platform configured to execute one or more automated artificial intelligence models, wherein the one or more automated artificial intelligence models comprises a neural network model, wherein the one or more automated artificial intelligence models are trained on data from a data layer to identify a plurality of anatomical structures;
receiving a preoperative radiographic image of a subject;
detecting the plurality of anatomical structures in the preoperative radiographic image of the subject;
generating a subject specific functional pelvis grid from the plurality of anatomical structures detected in the preoperative radiographic image of the subject,
receiving an intraoperative anteroposterior pelvis radiographic image of the subject;
identifying an at least one anatomical landmark in the intraoperative anteroposterior pelvis radiographic image, whereby the computing platform performs the step of: selecting a situation specific grid selected from the group consisting of: functional pelvis grid, spinopelvic grid, level pelvis grid, reference grid, neck cut grid, reamer depth grid, center of rotation grid, cup grid, leg length and offset grid, and femur abduction grid; and registering the selected grid to the anatomical landmark in the intraoperative anteroposterior pelvis radiographic image.