US 12,251,313 B2
Systems and methods for orthopedic implants
Niall Patrick Casey, Carlsbad, CA (US); and Michael J Cordonnier, Carlsbad, CA (US)
Assigned to Carlsmed, Inc., Carlsbad, CA (US)
Filed by Carlsmed, Inc., Carlsbad, CA (US)
Filed on Jul. 23, 2024, as Appl. No. 18/782,016.
Application 18/782,016 is a continuation of application No. 18/213,244, filed on Jun. 22, 2023.
Application 18/213,244 is a continuation of application No. 18/071,555, filed on Nov. 29, 2022, granted, now 11,717,412, issued on Aug. 8, 2023.
Application 18/071,555 is a continuation of application No. 16/569,494, filed on Sep. 12, 2019, granted, now 11,696,833, issued on Jul. 11, 2023.
Claims priority of provisional application 62/730,336, filed on Sep. 12, 2018.
Prior Publication US 2024/0374389 A1, Nov. 14, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. A61F 2/30 (2006.01); A61B 34/10 (2016.01); A61F 2/44 (2006.01); B33Y 50/00 (2015.01); B33Y 80/00 (2015.01); G05B 19/4099 (2006.01)
CPC A61F 2/30942 (2013.01) [A61B 34/10 (2016.02); A61F 2/4455 (2013.01); B33Y 50/00 (2014.12); B33Y 80/00 (2014.12); G05B 19/4099 (2013.01); A61F 2002/30943 (2013.01); A61F 2002/30948 (2013.01); A61F 2002/30952 (2013.01); A61F 2002/3096 (2013.01); A61F 2002/30985 (2013.01); G05B 2219/35134 (2013.01); G05B 2219/49007 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing surgical assistance, the method comprising:
receiving one or more pre-operative images of a patient;
determining a patient-specific treatment plan for the patient, wherein the patient-specific treatment plan is determined at least in part by using at least one trained machine learning model, and wherein the patient-specific treatment plan includes:
a virtual model of a corrected spine of the patient based on the one or more pre-operative images of the patient, and
a patient-specific implant design for the patient-specific treatment plan;
after the patient-specific implant is implanted in the patient,
receiving one or more post-operative images of the patient,
determining a treatment outcome based on the received one or more post-operative images and the planned treatment, and
determining whether to retrain the at least one trained machine learning model based on the treatment outcome;
in response to determining to retrain the at least one trained machine learning model, selecting post-operative data of the patient as one or more training items;
retraining the at least one trained machine learning model using the one or more training items; and
determining, at least in part using the retrained at least one trained machine learning model, at least one patient-specific treatment plan for another patient.