# US 12,594,134 B2 · Kinematically aligned robotic total knee arthroplasty

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- Number: US 12,594,134 B2
- Document ID: US12594134B2
- Status: Granted
- Kind: B2
- Application number: 18/236,130
- Filed: 2023-08-21
- Published: 2023-12-07
- Granted: 2026-04-07
- Earliest claimed: 2018-09-14
- Assignees: Zimmer, Inc.
- Applicants: Zimmer, Inc.
- Inventors: Joseph Maratt
- Web page: https://patentgrep.com/patent/US12594134B2

## CPC

- A61B 34/30: Surgical robots
- A61B 17/1675: {for the knee}
- A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations
- A61F 2/461: {of knees}
- G06N 3/008: based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- A61B 17/154: {for preparing bone for knee prosthesis}
- A61B 17/155: {Cutting femur}
- A61B 17/157: {Cutting tibia}
- A61B 2034/104: {Modelling the effect of the tool, e.g. the effect of an implanted prosthesis or for predicting the effect of ablation or burring}
- A61B 2034/105: {Modelling of the patient, e.g. for ligaments or bones}
- A61B 2034/107: {Visualisation of planned trajectories or target regions}
- A61B 2034/2048: {using an accelerometer or inertia sensor}
- A61B 2034/2055: {Optical tracking systems}
- A61B 2034/252: {indicating steps of a surgical procedure}
- A61B 2090/367: {creating a 3D dataset from 2D images using position information}
- A61F 2002/4633: {for selection of endoprosthetic joints or for pre-operative planning}
- A61F 2002/4658: {for measuring dimensions, e.g. length}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G16H 50/50: for simulation or modelling of medical disorders
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61F: FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS (dental prosthetics A61C)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A system and method may use a robotic arm to perform a range of motion test for use in generating information related to kinematic alignment in a total or partial knee arthroplasty. A method may include controlling a robotic arm to move a tibia throughout a range of motion for a knee. A series of measurements of gap distance for the knee may be used to determine a tibial resection location for the tibia. The method may include updating a plan (e.g., 3D, preoperative) for an orthopedic procedure on the knee with the tibial resection location.

## Claims

### Claim 1

A non-transitory machine-readable medium including instructions, which when executed by processing circuitry, cause the processing circuitry to perform operations to:
transmit control signals to a robotic arm of a robotic surgical system, the control signals configured to control the robotic arm to move a tibia throughout a range of motion for a knee;
obtain, from a sensor operably coupled to the robotic arm, at least one measurement of a gap distance for the knee at a location in the range of motion;
determine, based on a model of the knee and the at least one measurement, a change to an implant size;
update a plan for an orthopedic procedure on the knee with the change to the implant size; and
control the robotic arm to move based on the updated plan.

### Claim 2 (depends on 1)

The non-transitory machine-readable medium of claim 1, further comprising instructions to cause the processing circuitry to preoperatively generate the plan for an orthopedic procedure on the knee before controlling the robotic arm to move the tibia throughout the range of motion.

### Claim 3 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein the instructions to determine the change to the implant size include instructions that cause the processing circuitry to use the at least one measurement as an input to a machine learning engine, trained using prior sizing measurements and outcome information, and receiving the change to the implant size as an output of the machine learning engine.

### Claim 4

A robotic surgical system comprising:
a robotic controller to control a robotic arm to move a tibia throughout a range of motion for a knee;
a sensor to obtain at least one measurement of a gap distance for the knee at a location in the range of motion;
processing circuitry; and
a machine-readable medium including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations to:
transmit control signals to the robotic arm of the robotic surgical system, the control signals configured to control the robotic arm via the robotic controller to move the tibia throughout the range of motion for the knee;
obtain, from the sensor operably coupled to the robotic arm, the at least one measurement of the gap distance for the knee at the location in the range of motion from the sensor;
determine, based on a model of the knee and the at least one measurement, a change to an implant size;
update a plan for an orthopedic procedure on the knee with the change to the implant size; and
control the robotic arm to move based on the updated plan.

### Claim 5 (depends on 4)

The robotic surgical system of claim 4, wherein the processing circuitry is further to preoperatively generate the plan for an orthopedic procedure on the knee before controlling the robotic arm to move the tibia throughout the range of motion.

### Claim 6 (depends on 4)

The robotic surgical system of claim 4, wherein the robotic controller is further to cause the robotic arm to remove osteophytes from the tibia of the knee before controlling the robotic arm to move the tibia throughout the range of motion.

### Claim 7 (depends on 4)

The robotic surgical system of claim 4, wherein the processing circuitry is further to generate information indicative of an axis through a femur of the knee.

### Claim 8 (depends on 4)

The robotic surgical system of claim 4, wherein to update the plan, the processing circuitry is further to generate a range of motion gap distance model based on the at least one measurement, including a medial gap distance and a lateral gap distance at a location along the range of motion.

### Claim 9 (depends on 4)

The robotic surgical system of claim 4, wherein to update the plan, the processing circuitry is further to generate a range of motion gap distance model based on the at least one measurement, including medial gap distances and lateral gap distances at every degree throughout the range of motion.

### Claim 10 (depends on 4)

The robotic surgical system of claim 4, wherein a planned laxity is adjusted based on the change to the implant size.

### Claim 11 (depends on 4)

The robotic surgical system of claim 4, wherein to update the plan, the processing circuitry is further to update a tibial resection location based on the at least one measurement.

### Claim 12 (depends on 4)

The robotic surgical system of claim 4, wherein to update the plan with the change to the implant size, the processing circuitry is further to determine a minimum error from an ideal rectangle or trapezoid to the gap distance across the range of motion.

### Claim 13 (depends on 4)

The robotic surgical system of claim 4, wherein to determine the change to the implant size, the processing circuitry is further to use the at least one measurement as an input to a machine learning engine, the machine learning engine trained using prior sizing measurements and outcome information, and receive the change to the implant size as an output of the machine learning engine.

### Claim 14 (depends on 4)

The robotic surgical system of claim 4, wherein to obtain the at least one measurement, the sensor is configured to determine position data for the robotic arm, and wherein the processing circuitry is to use the position data to evaluate the gap distance for the knee throughout the range of motion.

### Claim 15 (depends on 4)

The robotic surgical system of claim 4, wherein to control the robotic arm to move the tibia throughout the range of motion, the robotic controller is further to activate a force assist for the robotic arm.

### Claim 16 (depends on 4)

The robotic surgical system of claim 4, wherein to control the robotic arm to move the tibia throughout the range of motion, the robotic controller is to cause the robotic arm to move the tibia automatically and without surgeon interaction.

### Claim 17

A method comprising:
transmitting, using processing circuitry of a robotic surgical system, control signals to a robotic arm of the robotic surgical system, the control signals configured to control the robotic arm to move a tibia throughout a range of motion for a knee;
obtaining, from a sensor operably coupled to the robotic arm and using the processing circuitry of the robotic surgical system, at least one measurement of a gap distance for the knee at a location in the range of motion;
determining, using the processing circuitry of the robotic surgical system, based on a model of the knee and the at least one measurement, a change to an implant size;
updating, using the processing circuitry of the robotic surgical system, a plan for an orthopedic procedure on the knee with the change to the implant size; and
controlling the robotic arm to move based on the updated plan.

### Claim 18 (depends on 17)

The method of claim 17, further comprising preoperatively generating, using the processing circuitry of the robotic surgical system, a plan for an orthopedic procedure on the knee before controlling the robotic arm to move the tibia throughout the range of motion.

### Claim 19 (depends on 17)

The method of claim 17, wherein updating the plan includes generating, using the processing circuitry of the robotic surgical system, a range of motion gap distance model based on the at least one measurement, including a medial gap distance and a lateral gap distance at three or more locations along the range of motion.

### Claim 20 (depends on 17)

The method of claim 17, further comprising adjusting, using the processing circuitry of the robotic surgical system, a planned laxity based on the change to the implant size.