Method and apparatus for diagnosing joints

• US 5,533,519 A
• Filed: 05/09/1995
• Issued: 07/09/1996
• Est. Priority Date: 06/24/1993
• Status: Expired due to Term

• Current Assignee: Biomet Sports Medicine LLC (Zimmer Biomet Holdings, Inc.)
• Original Assignee: Bio3research SRL
• Inventors: Hershberger, Troy W., Radke, John C., Ryan, Gregory J.
• Primary Examiner(s): HINDENBURG, MAX F
• Application Number:
  US08/437,704
• Time in Patent Office: 427 Days
• Field of Search: 128/773, 128/774, 128/777, 128/779, 128/782, 606/53, 606/86
• US Class Current: 600/595
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1127 using markers
• CPC Class Codes: A61B 5/4514 Cartilage
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/682 Mouth, e.g., oral cavity; tongue; Lips; Teeth
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 7/006 Detecting skeletal, cartilage or muscle noise A61B7/003 and A61B7/008 take precedence
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

A method and apparatus for diagnosing joints based on sensed joint vibrations. Accelerometers disposed on the skin adjacent to the joint detect vibrational patterns during movement of the joint. These patterns are then processed by one processor to generate a predetermined set of data parameters descriptive of the vibration pattern. Also, the position and velocity of the joint during the vibration is recorded. This information from numerous patients with known joint conditions is used to train a adaptive interpreter, such as a neural network, to produce an output in response to these inputs which is indicative of the known joint condition. Once trained, the adaptive interpreter can then interpret this set of parameters for an unknown joint to generate a fast and reliable diagnosis. The result is a non-subjective joint disorder classification system that can be utilized by persons without particular expertise in analyzing joint vibrational patterns.

Claims

1. A system for diagnosing an implantable orthopedic device within a body, said system comprising:

a) sensor means for detecting a vibration pattern from the implantable orthopedic device;

b) preprocessor means for producing a predetermined set of data parameters in response to said sensor means which are descriptive of the vibration pattern; and

c) adaptive interpreter means for receiving said data parameters as input and providing an output which indicates at least one condition of the implantable orthopedic device.

2. The system of claim 1, wherein said adaptive interpreter is a neural network.

3. The system of claim 1, wherein said sensor means detects vibration patterns from the implantable orthopedic device during movement of a joint.

4. The system of claim 1, wherein said sensor means detects vibration patterns from the implantable orthopedic device during shifts in the load on said implantable orthopedic device.

5. The system of claim 1, wherein the implantable orthopedic device is a joint prosthetic.

6. The system of claim 5 further comprising means for measuring the instantaneous position of the joint during movement of the joint.

7. The system of claim 5, further comprising means for measuring the velocity of the joint during movement of the joint.

8. The system of claim 5, further comprising means for determining the range of motion of said joint, said range of motion being received by said interpreter as input.

9. The system of claim 1, wherein said implantable orthopedic device is a fracture fixation device.

10. The system of claim 1, wherein said preprocessor means includes means for determining the frequency spectrum of said vibration pattern.

11. The system of claim 10, wherein said preprocessing means includes means for determining an integral of the frequency spectrum of said vibration pattern.

12. A method for classifying conditions in an implanted orthopedic device within a body, said method comprising the steps of:

a) detecting a first set of vibration patterns from the implanted orthopedic device;

b) using said first set of vibration patterns to train an adaptive interpreter to produce an output indicating particular possible conditions in response to particular vibration patterns;

c) detecting a second set of vibration patterns from a subject implanted orthopedic device; and

d) transmitting said second set of vibration patterns to said adaptive interpreter wherein said adaptive interpreter generates an output indicating the presence of one or more of said possible conditions in said subject implanted orthopedic device.

13. The method of claim 12, wherein the step of using said first set of vibration patterns to train an adaptive interpreter further comprises the steps of iteratively processing said first set of vibration patterns in a neural network until the output indicates one of the possible conditions.

14. The method of claim 12, further comprising the step of detecting the frequency spectrum of the vibration pattern.

15. The method of claim 14, further comprising the step of determining the integral of the frequency spectrum of the vibration pattern.

16. The method of claim 12, wherein said adaptive interpreter is a neural network and wherein the step of using said first set of vibration patterns to train an adaptive interpreter includes the step of employing back propagation to train the adaptive interpreter.

17. The system of claim 12, wherein said sensor means detects vibration patterns from the implantable orthopedic device during movement of a joint.

18. A system for classifying conditions in a joint prosthesis within a body, the system comprising:

a) sensor means for detecting a vibration pattern from the joint prosthesis;

b) means for receiving non-vibrational diagnostic information relating to said joint prosthesis;

c) preprocessor means for providing a predetermined set of data parameters in response to said sensor means and said means for receiving non-vibrational diagnostic information which are descriptive of the vibration pattern and said diagnostic information; and

d) adaptive interpreter means for receiving said data parameters as input and producing an output which indicates at least one classification of the condition of said joint prosthesis.

19. The system of claim 18 where said adaptive interpreter is a trainable interpreter.

20. The system of claim 19 wherein said adaptive interpreter is a neural network.