Implant design analysis suite

• US 10,172,675 B2
• Filed: 04/14/2016
• Issued: 01/08/2019
• Est. Priority Date: 08/17/2007
• Status: Active Grant

• Current Assignee: Zimmer Incorporated (Zimmer Biomet Holdings, Inc.)
• Original Assignee: Zimmer Incorporated (Zimmer Biomet Holdings, Inc.)
• Inventors: Mahfouz, Mohamed Rashwan
• Primary Examiner(s): Strege, John
• Application Number:
  US15/098,379
• Publication Number:
  US 20160220312A1
• Time in Patent Office: 999 Days
• CPC Class Codes: 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
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 2034/107 Visualisation of planned trajectories or target regions
• CPC Class Codes: A61B 2034/108 Computer aided selection or customisation of medical implants or cutting guides
• CPC Class Codes: A61B 2034/256 having a database of accessory information, e.g. including context sensitive help or scientific articles
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61F 2/3094 Designing or manufacturing processes
• CPC Class Codes: A61F 2/4657 Measuring instruments used for implanting artificial joints for surgical instruments A61B90/06
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/53 Querying
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 2207/30052 Implant; Prosthesis
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G06T 7/35 : using statistical methods
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

A method for anatomical analysis and joint implant design. Embodiments provide users with the ability to anatomically analyze a single bone or a series of bones that exist in a database, evaluate surgical landmarks and axes, identify differences among specific characteristics of a given population, and modify existing implants or create new implant designs based on anatomical analyzes.

Claims

1. A method of designing an orthopaedic implant, comprising the steps of:

(a) obtaining three dimensional data corresponding to a multi-piece existing or prototype orthopaedic implant;

(b) obtaining three dimensional data sets for a first bone and a second bone cooperating to form a joint;

(c) associating the three dimensional data corresponding to the multi-piece existing or prototype orthopaedic implant with a three dimensional data set for the first bone and the second bone to comprise a virtual joint replacement;

(d) taking the virtual joint replacement through a virtual range of motion and recording kinematic data; and

(e) responsive to the recorded kinematic data, modifying the existing or prototype orthopaedic implant.

2. The method of claim 1, further comprising the step of associating, separately, the three dimensional data corresponding to the multi-piece existing or prototype orthopaedic implant with the remainder of the three dimensional data sets for the first bone and the second bone to comprise multiple joint replacements.

3. The method of claim 2, further comprising the step of taking the multiple joint replacements through a virtual range of motion and recording kinematic data.

4. A method of designing an orthopaedic implant, comprising the steps of:

(a) obtaining three dimensional data sets for a first bone and a second bone cooperating to form a joint of a mammal;

(b) moving the first bone with respect to the second bone through a virtual range of motion of the joint to establish natural kinematic data;

(c) repeating steps (a) and (b) for different animals of the same mammal;

(d) mathematically modeling the natural kinematic data; and

(e) designing a multi-piece existing or prototype orthopaedic implant using the mathematically modeled natural kinematic data.

5. A method of designing an orthopaedic implant, comprising the steps of:

(a) obtaining three dimensional data sets for a first bone cooperating with a second bone to form a joint of a mammal, where the three dimensional data sets include an individual data set for multiple samples of the first bone;

(b) propagating a standard reference point across each individual data set;

(c) obtaining a three dimensional data set for an orthopaedic component adapted to be mounted to the first bone;

(d) virtually mounting the orthopaedic component to the multiple samples of the first bone using the standard reference point to standardize the orthopaedic component across the multiple samples;

(e) moving the first bone with respect to the second bone through a virtual range of motion of the joint to generate kinematic data; and

(f) modifying the design of the orthopaedic implant using the generated kinematic data.

6. The method of claim 5, wherein the three dimensional data sets for the first bone were taken from actual bones of humans grouped on the basis of ethnicity.

7. The method of claim 5, wherein the three dimensional data sets for the first bone were taken from actual bones of humans grouped on the basis of age.

8. The method of claim 5, wherein the three dimensional data sets for the first bone were taken from actual bones of humans grouped on the basis of gender.

9. The method of claim 5, wherein the three dimensional data sets for the first bone were taken from actual bones of humans grouped on the basis of degenerative joint condition.

10. The method of claim 5, wherein the three dimensional data sets for the first bone were taken from actual bones of humans grouped on the basis of size.