US 12,343,085 B2
Methods for improved surgical planning using machine learning and devices thereof
Shawn P. Mcguan, San Clemente, CA (US); Scott Kennedy Laster, Memphis, TN (US); Zachary C. Wilkinson, Germantown, TN (US); Sied W. Janna, Memphis, TN (US); and Daniel Farley, Memphis, TN (US)
Assigned to SMITH & NEPHEW, INC., Memphis, TN (US); SMITH & NEPHEW ORTHOPAEDICS AG, Zug (CH); and SMITH & NEPHEW ASIA PACIFIC PTE. LIMITED, Singapore (SG)
Appl. No. 17/427,771
Filed by Smith & Nephew, Inc., Memphis, TN (US); Smith & Nephew Asia Pacific Pte. Limited, Singapore (SG); and Smith & Nephew Orthopaedics AG, Zug (CH)
PCT Filed Feb. 4, 2020, PCT No. PCT/US2020/016569
§ 371(c)(1), (2) Date Aug. 2, 2021,
PCT Pub. No. WO2020/163324, PCT Pub. Date Aug. 13, 2020.
Claims priority of provisional application 62/939,946, filed on Nov. 25, 2019.
Claims priority of provisional application 62/885,673, filed on Aug. 12, 2019.
Claims priority of provisional application 62/864,663, filed on Jun. 21, 2019.
Claims priority of provisional application 62/801,245, filed on Feb. 5, 2019.
Claims priority of provisional application 62/801,257, filed on Feb. 5, 2019.
Prior Publication US 2022/0125515 A1, Apr. 28, 2022
Int. Cl. G16H 20/40 (2018.01); A61B 17/17 (2006.01); A61B 34/00 (2016.01); A61B 34/10 (2016.01); A61B 90/00 (2016.01); A61F 2/46 (2006.01); G02B 27/01 (2006.01); G06N 3/08 (2023.01); G06N 5/046 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G09B 5/02 (2006.01); G09B 19/00 (2006.01); G09B 19/24 (2006.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 17/00 (2006.01); A61B 17/15 (2006.01); A61B 34/20 (2016.01); A61B 34/30 (2016.01); A61B 90/50 (2016.01); A61B 90/96 (2016.01); A61F 2/30 (2006.01)
CPC A61B 34/10 (2016.02) [A61B 17/1764 (2013.01); A61B 34/25 (2016.02); A61B 90/361 (2016.02); A61B 90/37 (2016.02); A61B 90/39 (2016.02); A61F 2/461 (2013.01); G02B 27/0172 (2013.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G09B 5/02 (2013.01); G09B 19/003 (2013.01); G09B 19/24 (2013.01); G16H 10/60 (2018.01); G16H 20/40 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 2017/00199 (2013.01); A61B 2017/00526 (2013.01); A61B 17/15 (2013.01); A61B 17/17 (2013.01); A61B 2034/102 (2016.02); A61B 2034/104 (2016.02); A61B 2034/105 (2016.02); A61B 2034/107 (2016.02); A61B 2034/108 (2016.02); A61B 34/20 (2016.02); A61B 2034/2048 (2016.02); A61B 2034/2057 (2016.02); A61B 2034/2063 (2016.02); A61B 2034/2065 (2016.02); A61B 2034/2068 (2016.02); A61B 2034/2072 (2016.02); A61B 2034/2074 (2016.02); A61B 2034/252 (2016.02); A61B 2034/254 (2016.02); A61B 2034/256 (2016.02); A61B 2034/258 (2016.02); A61B 34/30 (2016.02); A61B 2090/363 (2016.02); A61B 2090/365 (2016.02); A61B 2090/371 (2016.02); A61B 2090/376 (2016.02); A61B 2090/3916 (2016.02); A61B 2090/502 (2016.02); A61B 90/96 (2016.02); A61F 2002/30952 (2013.01); A61F 2002/4633 (2013.01); G02B 2027/0138 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for improved surgical planning, the method comprising:
training at least one neural network based on historical case log data sets comprising historical outcome data correlated with one or more of historical patient data, historical implant data, or historical healthcare professional data associated with a plurality of instances of a surgical procedure;
applying, by a processor, the at least one neural network to current patient data for a current patient to generate a predictor equation, wherein the current patient data comprises at least anatomy data for an anatomy of the current patient, and wherein the predictor equation functionally relates a size, position, and orientation of an implant to an estimated response for the anatomy of the current patient;
optimizing, by the processor, the predictor equation to generate the size, position, and orientation of the implant, and one or more resection parameters for one or more resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient as part of a surgical plan for the current patient undergoing the surgical procedure;
controlling, by the processor, actuation of a surgical tool to implement a resection of the surgical procedure according to the surgical plan, based on the size, position, and orientation of the implant; and
updating the at least one neural network based on the current patient data and current outcome data generated for the current patient following execution of the surgical procedure according to the surgical plan,
wherein the surgical procedure is an orthopedic procedure.