IP Library › Granted Patent US 12,383,334
Granted Patent B2
US 12,383,334 · App. 17/413,460 · Granted Aug 12, 2025

Orthopedic surgical planning based on soft tissue and bone density modeling

Inventors: Jean Chaoui (Locmaria Plouzané, FR); Charlotte Le Saint (Brest, FR); Maximilien Mayya (Antibes, FR); Manuel Jean-Marie Urvoy (Brest, FR)
Assignee: Howmedica Osteonics Corp.
A61B34/10A61B5/1121A61B5/4519A61B5/4528A61B5/4872A61B6/032A61B34/20A61B34/25A61B90/36A61F2/46G06F3/011G06N3/045G06T7/0012G06T7/30G16H20/40G16H30/20A61B6/505A61B2034/102A61B2034/105A61B2034/108A61B2034/2068A61B2034/252A61B2090/365A61F2002/4018A61F2002/4633G06T2200/24G06T2207/10081G06T2207/20084G06T2207/30008
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Quick Facts
Patent No.
US 12,383,334
App. No.
17/413,460
Filed
Jun 11, 2021
Granted
Aug 12, 2025
Kind
B2
Art Unit
2612
USPC
703/6
Abstract

A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Claims (79)

1. A system for automatically generating a shoulder surgery recommendation for a patient, the system comprising:

a memory configured to store patient-specific image data for the patient; and

processing circuitry configured to:

receive the patient-specific image data from the memory;

determine, based on the patient-specific image data, one or more soft tissue characteristics and a bone density metric associated with a humerus of the patient, wherein the one or more soft tissue characteristics comprises at least one of a fatty infiltration value or an atrophy ratio for one or more soft tissue structures of the patient;

generate, based on at least one of the fatty infiltration value or the atrophy ratio for the one or more soft tissue structures of the patient, a recommendation of a shoulder surgery type to be performed for the patient;

generate, based on the bone density metric associated with the humerus, a recommendation of a humeral implant type for the patient; and

output the recommendation of the shoulder surgery type and the recommendation of humeral implant type for the patient.

2. The system of claim 1 , wherein the humeral implant type comprises one of a stemmed implant type or a stemless implant type.

3. The system of claim 1 , wherein the recommendation of the humeral implant type comprises a recommendation indicating a length of a stem of a humeral implant.

4. The system of claim 1 , wherein the processing circuitry is configured to output, for display, a user interface comprising a graphical representation of the bone density metric over a representation of the humerus.

5. The system of claim 1 , wherein the one or more soft tissue characteristics comprises the fatty infiltration value for the one or more soft tissue structures of the patient.

6. The system of claim 5 , wherein the processing circuitry is configured to determine the fatty infiltration value by:

applying a mask to a patient-specific shape representative of the one or more soft tissue structures;

applying a threshold to voxels under the mask;

determining a fat volume based on the voxels under the threshold; and

determining the fatty infiltration value based on the fat volume and a volume of the patient-specific shape representative of the one or more soft tissue structures.

7. The system of claim 1 , wherein the one or more soft tissue characteristics comprises the atrophy ratio for the one or more soft tissue structures.

8. The system of claim 7 , wherein the processing circuitry is configured to determine the atrophy ratio by:

determining bone to muscle dimensions for the one or more soft tissue structures;

obtaining a statistical mean shape (SMS) for the one or more soft tissue structures;

deforming the SMS by satisfying a threshold of an algorithm to fit a deformed version of the SMS to the bone to muscle dimensions for the one or more soft tissue structures; and

determining the atrophy ratio for the one or more soft tissue structures by dividing a volume of the deformed SMS by a soft-tissue structure volume.

9. The system of claim 1 , wherein the one or more soft tissue characteristics comprises a range of motion of the humerus.

10. The system of claim 1 , wherein the processing circuitry is configured to determine at least one of the one or more soft tissue characteristics or the bone density metric associated with the humerus using a neural network.

11. The system of claim 10 , wherein the one or more soft tissue characteristics comprise at least one of the fatty infiltration value, the atrophy ratio, or a range of motion value for the one or more soft tissue structures of the patient, and wherein the processing circuitry is configured to:

input at least one of the fatty infiltration value, the atrophy ratio, or the range of motion value into the neural network; and

generate the recommendation of the shoulder surgery type based on an output from the neural network.

12. The system of claim 1 , wherein the processing circuitry is configured to determine the one or more soft tissue characteristics from by:

receiving an initial shape for the one or more soft tissue structures, determining a plurality of surface points on the initial shape;

registering the initial shape to the patient-specific image data;

identifying one or more contours in the patient-specific image data representative of a boundary of the one or more soft tissue structures;

iteratively moving the plurality of surface points towards respective locations of the one or more contours to change the initial shape to a patient-specific shape representative of the one or more soft tissue structures; and

determining, based on the patient-specific shape, the one or more soft tissue characteristics for the one or more soft tissue structures.

13. The system of claim 1 , wherein the processing circuitry is configured to control a user interface to display a representation of the one or more soft tissue characteristics.

14. The system of claim 13 , wherein the processing circuitry is configured to control the user interface to display at least one of the representation of the one or more soft tissue characteristics or the bone density metric associated with the humerus as part of a mixed reality user interface.

15. The system of claim 1 , wherein the shoulder surgery type comprises one of an anatomical shoulder replacement or a reverse shoulder replacement.

16. A method for automatically generating a shoulder surgery recommendation for a patient, the method comprising:

receiving, from a memory, patient-specific image data;

determining, by processing circuitry and based on the patient-specific image data, one or more soft tissue characteristics and a bone density metric associated with a humerus of the patient, wherein the one or more soft tissue characteristics comprises at least one of a fatty infiltration value or an atrophy ratio for one or more soft tissue structures of the patient;

generating, by the processing circuitry and based on at least one of the fatty infiltration value or the atrophy ratio for the one or more soft tissue structures, a recommendation of a shoulder surgery type to be performed for the patient;

generating, by the processing circuitry and based on the bone density metric associated with the humerus, a recommendation of a humeral implant type for the patient; and

outputting, by the processing circuitry, the recommendation of the shoulder surgery type and the recommendation of the humeral implant type for the patient.

17. The method of claim 16 , wherein the humeral implant type comprises one of a stemmed implant type or a stemless implant type.

18. The method of claim 16 , wherein the recommendation of the humeral implant type comprises a recommendation indicating a length of a stem of a humeral implant.

19. The method of claim 16 , further comprising outputting, for display, a user interface comprising a graphical representation of the bone density metric over a representation of the humerus.

20. The method of claim 16 , wherein the one or more soft tissue characteristics comprises the fatty infiltration value for the one or more soft tissue structures.

21. The method of claim 20 , wherein determining the fatty infiltration value comprises:

applying a mask to a patient-specific shape representative of the one or more soft tissue structures;

applying a threshold to voxels under the mask;

determining a fat volume based on the voxels under the threshold; and

determining the fatty infiltration value based on the fat volume and a volume of the patient-specific shape representative of the one or more soft tissue structures.

22. The method of claim 16 , wherein the one or more soft tissue characteristics comprises the atrophy ratio for the one or more soft tissue structures.

23. The method of claim 22 , wherein determining the atrophy ratio comprises:

determining bone to muscle dimensions for the one or more soft tissue structures;

obtaining a statistical mean shape (SMS) for the one or more soft tissue structures;

deforming the SMS by satisfying a threshold of an algorithm to fit a deformed version of the SMS to the bone to muscle dimensions for the one or more soft tissue structures; and

determining the atrophy ratio for the one or more soft tissue structures by dividing a volume of the deformed SMS by a soft tissue structure volume.

24. The method of claim 16 , wherein the one or more soft tissue characteristics comprises a range of motion of the humerus.

25. The method of claim 16 , wherein determining at least one of the one or more soft tissue characteristics or the bone density metric associated with the humerus comprises determining at least one of the one or more soft tissue characteristics or the bone density metric associated with the humerus using a neural network.

26. The method of claim 25 , wherein the one or more soft tissue characteristics comprise at least one of fatty infiltration value, the atrophy ratio, or a range of motion value for the one or more soft tissue structures of the patient, and wherein the method further comprises:

inputting at least one of the fatty infiltration value, the atrophy ratio, or the range of motion value into the neural network; and

generating the recommendation of the shoulder surgery type based on an output from the neural network.

27. The method of claim 16 , wherein determining the one or more soft tissue characteristics comprises:

receiving an initial shape for the one or more soft tissue structures;

determining a plurality of surface points on the initial shape;

registering the initial shape to the patient-specific image data;

identifying one or more contours in the patient-specific image data representative of a boundary of the one or more soft tissue structures;

iteratively moving the plurality of surface points towards respective locations of the one or more contours to change the initial shape to a patient-specific shape representative of the one or more soft tissue structures; and

determining, based on the patient-specific shape, the one or more soft tissue characteristics for the one or more soft tissue structures.

28. The method of claim 16 , further comprising controlling a user interface to display a representation of the one or more soft tissue characteristics.

29. The method of claim 28 , wherein controlling the user interface to display at least one of the representation of the one or more soft tissue characteristics or the bone density metric associated with the humerus comprises controlling the user interface to display at least one of the representation of the one or more soft tissue characteristics or the bone density metric associated with the humerus as part of a mixed reality user interface.

30. The method of claim 16 , wherein the shoulder surgery type comprises one of an anatomical shoulder replacement or a reverse shoulder replacement.

31. At least one non-transitory computer-readable storage medium having instructions stored thereon that, when executed, cause processing circuitry to:

receive patient-specific image data from a memory;

determine, based on the patient-specific image data, one or more soft tissue characteristics and a bone density metric associated with a humerus of a patient, wherein the one or more soft tissue characteristics comprises at least one of a fatty infiltration value or an atrophy ratio for one or more soft tissue structures of the patient;

generate, based on at least one of the fatty infiltration value or the atrophy ratio for the one or more soft tissue structures of the patient, a recommendation of a shoulder surgery type to be performed for the patient;

generate, based on the bone density metric associated with the humerus, a recommendation of a humeral implant type for the patient; and

output, for display, the recommendation of the shoulder surgery type and the recommendation of the humeral implant type for the patient.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 24, 2022
From: CHAOUI, JEAN; LE SAINT, CHARLOTTE; MAYYA, MAXIMILIEN; URVOY, MANUEL JEAN-MARIE
To: IMASCAP SAS
Reel/Frame 059996/0690 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 24, 2022
From: IMASCAP SAS
To: TORNIER, INC.
Reel/Frame 059996/0727 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 24, 2022
From: TORNIER, INC.
To: HOWMEDICA OSTEONICS CORP.
Reel/Frame 060165/0707 →
Continuity (7)
Provisional Application 62826146 · Mar 29, 2019
Provisional Application 62826119 · Mar 29, 2019
Provisional Application 62826133 · Mar 29, 2019
Provisional Application 62826156 · Mar 29, 2019
Provisional Application 62826168 · Mar 29, 2019
Provisional Application 62778774 · Dec 12, 2018
Related Publication 20220039868A1 · Feb 10, 2022
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