ALGORITHM-BASED OPTIMIZATION FOR KNEE ARTHROPLASTY PROCEDURES

1 - 20 . (canceled)

21 . A method for optimizing a surgical procedure, the method comprising:

receiving pre-operative data comprising anatomical measurements of a patient and implant parameters identifying an implant to be used in the surgical procedure;

during the surgical procedure:

collecting intraoperative data from one or more surgical tools of a computer-assisted surgical system,

determining a plurality of patient-specific kinetic and kinematic response values by optimizing and solving at least one equation using the intraoperative data and the pre-operative data, and

generating, on a user interface, a recommendation for aligning and/or positioning the implant based on the plurality of patient-specific kinetic and kinematic response values.

22 . The method of claim 21 , wherein the plurality of patient-specific kinetic and kinematic response values comprise subsets of kinetic and kinematic response values for a plurality of joint flexion values.

23 . The method of claim 22 , further comprising receiving a user-selection of individual subsets of the kinetic and kinematic response values based on a user-selected joint flexion value.

24 . The method of claim 21 , further comprising comparing the patient-specific kinetic and kinematic values with a specified goal, wherein optimizing the at least one equation comprises reducing differences between the patient-specific kinetic and kinematic values and a corresponding target value associated with the specified goal.

25 . The method of claim 24 , further comprising displaying an indication of the differences between the patient-specific kinetic and kinematic response value and the corresponding target value associated with the specified goal.

26 . The method of claim 25 , wherein each patient-specific kinetic and kinematic response value and the corresponding target value is depicted on a slider scale.

27 . The method of claim 26 , wherein the slider scale further includes one or more indicators of a 510(K) limit associated with the patient-specific kinetic and kinematic response value depicted on the slider scale.

28 . The method of claim 21 , wherein a distinct weight value is applied to each patient-specific kinetic and kinematic response value during the solving of the at least one equation.

29 . The method of claim 28 , wherein the distinct weight value applied to each patient-specific kinetic and kinematic response value is determined though a Group Decision Making (GDM) process.

30 . The method of claim 21 , wherein the at least one equation is comprised in an equation set selected from a plurality of pre-generated equation sets based on the pre-operative data.

31 . The method of claim 30 , wherein the equation set is solved using a Goal Programming (GP) algorithm.

32 . A method for optimizing a surgical procedure, the method comprising:

receiving pre-operative data comprising: anatomical measurements of a patient and implant parameters identifying an implant to be used in the surgical procedure;

during the surgical procedure:

collecting intraoperative data from one or more surgical tools of a computer-assisted surgical system,

applying a machine learning model to the pre-operative data and the intraoperative data, wherein the machine learning model is configured to transform the pre-operative data to a plurality of patient-specific kinetic and kinematic response values, and

generating, on a user interface, a recommendation for aligning and/or positioning the implant based on the plurality of patient-specific kinetic and kinematic response values.

33 . The method of claim 32 , wherein the plurality of patient-specific kinetic and kinematic response values comprise kinetic and kinematic response values for a plurality of joint flexion values.

34 . The method of claim 33 , further comprising allowing user-selection of individual subsets of the kinetic and kinematic response values based on a user-selected joint flexion value.

35 . The method of claim 32 , further comprising comparing the patient-specific kinetic and kinematic values with a specified goal.

36 . The method of claim 35 , further comprising displaying an indication of differences between the patient-specific kinetic and kinematic response value and a corresponding target value associated with the specified goal.

37 . The method of claim 36 , wherein each patient-specific kinetic and kinematic response value and the corresponding target value is depicted on a slider scale.

38 . The method of claim 37 , wherein the slider scale further includes one or more indicators of a 510(K) limit associated with the patient-specific kinetic and kinematic response value depicted on the slider scale.

39 . The method of claim 32 , wherein the machine learning model is a neural network trained using a database of information collected from previous surgical procedures.

40 . A computer-assisted surgical system comprising:

one or more surgical tools generating intraoperative data during a surgical procedure;

a database comprising a plurality of pre-generated equation sets;

at least one processor configured, individually or in combination, to:

receive pre-operative data comprising: anatomical measurements of a patient and implant parameters identifying an implant to be used in the surgical procedure;

during the surgical procedure:

determine a plurality of patient-specific kinetic and kinematic response values by optimizing and solving at least one equation using the intraoperative data and the pre-operative data;

generate a recommendation for aligning and/or positioning the implant based on the plurality of patient-specific kinetic and kinematic response values.

a graphical user interface providing the recommendation.