Computer-assisted arthroplasty system
A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.
1. A system for creating an activity-optimized cutting guide for a surgical procedure, the system comprising:
one or more processors; and
a non-transitory, computer-readable medium storing instructions that, when executed, cause the one or more processors to:
determine, based on pre-operative image data, one or more patient-specific anatomical measurements associated with an anatomical joint of a patient,
apply a statistical model of joint performance to the one or more patient-specific anatomical measurements, wherein the statistical model uses one or more transfer functions,
identify one or more cut angles for performing a surgical procedure based on the applied statistical model,
generate a model of a patient-specific cutting guide based on the one or more cut angles, and
output the model to a computer-readable storage device.
2. The system of claim 1 , wherein the one or more patient-specific anatomical measurements comprise a distal condyle radius and an anterior condyle radius.
3. The system of claim 1 , wherein the instructions that cause the one or more processors to identify one or more cut angles comprise instructions that, when executed, cause the one or more processors to select a set of cut angles that substantially balance condylar gaps of the anatomical joint throughout a range of motion associated with one or more physical activities.
4. The system of claim 1 , where the instructions that cause the one or more processors to determine one or more patient-specific anatomical measurements comprise instructions that, when executed, cause the one or more processors to:
receive one or more pre-operative images depicting the anatomical joint;
create a three-dimensional anatomical model of the anatomical joint based on the one or more pre-operative images; and
determine the one or more patient-specific anatomical measurements based on the three-dimensional anatomical model.
5. The system of claim 1 , wherein the instructions that cause the one or more processors to apply a statistical model of joint performance comprise instructions that, when executed, cause the one or more processors to apply a Monte Carlo method to iteratively evaluate a plurality of possible cut angles.
6. The system of claim 5 , wherein each iteration of the Monte Carlo method comprises applying the one or more transfer functions with a distinct set of parameters.
7. The system of claim 1 , wherein the statistical model of joint performance is a machine learning model.
8. The system of claim 1 , wherein the instructions that cause the one or more processors to generate a model of a patient-specific cutting guide comprise instructions that, when executed, cause the one or more processors to modify a model of a cutting guide blank to include the one or more apertures based on the one or more cut angles.
9. The system of claim 1 , wherein the computer-readable storage device is in operable communication with a manufacturing device configured to manufacture the patient-specific cutting guide.
10. A system for creating an activity-optimized cutting guide for a surgical procedure, the system comprising:
one or more processors; and
a non-transitory, computer-readable medium storing instructions that, when executed, cause the one or more processors to:
determine, based on pre-operative image data, one or more patient-specific anatomical measurements associated with an anatomical joint of a patient,
apply a statistical model of joint performance to the one or more patient-specific anatomical measurements, wherein the statistical model is based on simulation data for a plurality of joint geometries associated with a plurality of implants,
identify one or more cut angles for performing a surgical procedure based on the applied statistical model,
generate a model of a patient-specific cutting guide based on the one or more cut angles, and
output the model to a computer-readable storage device.
11. The system of claim 10 , wherein the one or more patient-specific anatomical measurements comprise a distal condyle radius and an anterior condyle radius.
12. The system of claim 10 , wherein the instructions that cause the one or more processors to identify one or more cut angles comprise instructions that, when executed, cause the one or more processors to select a set of cut angles that substantially balance condylar gaps of the anatomical joint throughout a range of motion associated with one or more physical activities.
13. The system of claim 10 , where the instructions that cause the one or more processors to determine one or more patient-specific anatomical measurements comprise instructions that, when executed, cause the one or more processors to:
receive one or more pre-operative images depicting the anatomical joint;
create a three-dimensional anatomical model of the anatomical joint based on the one or more pre-operative images; and
determine the one or more patient-specific anatomical measurements based on the three-dimensional anatomical model.
14. The system of claim 10 , wherein the instructions that cause the one or more processors to apply a statistical model of joint performance comprise instructions that, when executed, cause the one or more processors to apply a Monte Carlo method to iteratively evaluate a plurality of possible cut angles.
15. The system of claim 14 , wherein each iteration of the Monte Carlo method comprises applying one or more transfer functions with a distinct set of parameters.
16. The system of claim 10 , wherein the statistical model of joint performance is a machine learning model.
17. The system of claim 10 , wherein the instructions that cause the one or more processors to generate a model of a patient-specific cutting guide comprise instructions that, when executed, cause the one or more processors to modify a model of a cutting guide blank to include the one or more apertures based on the one or more cut angles.
18. The system of claim 10 , wherein the computer-readable storage device is in operable communication with a manufacturing device configured to manufacture the patient-specific cutting guide.
19. The system of claim 10 , wherein each implant of the plurality of implants is defined by a distinct set of implantation features comprising one or more of an implant position, an implant orientation, and an implant type.
20. The system of claim 10 , wherein the instructions, when executed, further cause the one or more processors to generate the statistical model by:
selecting the plurality of implants;
populating a database with the simulation data by simulating a motion of the plurality of joint geometries while performing one or more physical activities and an expected stress on the plurality of implants resulting from the motion; and
generating the statistical model of joint performance based on the simulation data.