Surgical generator for ultrasonic and electrosurgical devices

• US 10,263,171 B2
• Filed: 03/13/2015
• Issued: 04/16/2019
• Est. Priority Date: 10/09/2009
• Status: Active Grant

• Current Assignee: Cilag Gmbh International (Johnson & Johnson)
• Original Assignee: Ethicon, LLC (Johnson & Johnson)
• Inventors: Wiener, Eitan T., Aldridge, Jeffrey L., Noyes, Brian T., Giordano, James R., Beetel, III, Robert J., Price, Nathan J., Miller, Matthew C., Wiley, Jeffrey P., Price, Daniel W., Koch, Jr., Robert L., Brotz, Joseph A., Hein, John E.
• Primary Examiner(s): Bertram, Eric D.
• Application Number:
  US14/657,876
• Publication Number:
  US 20150182277A1
• Time in Patent Office: 1,495 Days
• CPC Class Codes: A61B 18/1206 Generators therefor
• CPC Class Codes: A61B 18/1233 with circuits for assuring patient safety
• CPC Class Codes: A61B 18/14 Probes or electrodes therefor
• CPC Class Codes: A61B 18/1445 at the distal end of a shaft, e.g. forceps or scissors at the end of a rigid rod
• CPC Class Codes: A61B 2017/00017 Electrical control of surgical instruments
• CPC Class Codes: A61B 2017/00477 Coupling A61B2017/0046 takes precedence
• CPC Class Codes: A61B 2017/00482 with a code
• CPC Class Codes: A61B 2017/00486 Adaptors for coupling parts with incompatible geometries
• CPC Class Codes: A61B 2017/2934 arcuate shaped guiding means
• CPC Class Codes: A61B 2017/2936 Pins in guiding slots
• CPC Class Codes: A61B 2017/320069 for ablating tissue
• CPC Class Codes: A61B 2017/320071 with articulating means for working tip
• CPC Class Codes: A61B 2017/320094 additional movable means performing clamping operation
• CPC Class Codes: A61B 2017/320095 with sealing or cauterizing means
• CPC Class Codes: A61B 2018/00178 Electrical connectors
• CPC Class Codes: A61B 2018/00601 Cutting
• CPC Class Codes: A61B 2018/0063 Sealing
• CPC Class Codes: A61B 2018/00666 using a threshold value
• CPC Class Codes: A61B 2018/00678 upper
• CPC Class Codes: A61B 2018/00684 using lookup tables
• CPC Class Codes: A61B 2018/00702 : Power or energy
• CPC Class Codes: A61B 2018/0072 : Current
• CPC Class Codes: A61B 2018/00732 : Frequency
• CPC Class Codes: A61B 2018/00767 : Voltage
• CPC Class Codes: A61B 2018/00779 : Power or energy
• CPC Class Codes: A61B 2018/00803 : with temperature prediction
• CPC Class Codes: A61B 2018/00827 : Current
• CPC Class Codes: A61B 2018/00875 : Resistance or impedance
• CPC Class Codes: A61B 2018/00892 : Voltage
• CPC Class Codes: A61B 2018/00988 : Means for storing information, e.g. calibration constants, or for preventing excessive use, e.g. usage, service life counter
• CPC Class Codes: A61B 2018/00994 : combining two or more different kinds of non-mechanical energy or combining one or more non-mechanical energies with ultrasound
• CPC Class Codes: H01R 2201/12 : for medicine and surgery
• CPC Class Codes: H10N 30/802 : Drive or control circuitry or methods for piezoelectric or electrostrictive devices not otherwise provided for

Abstract

A method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal.

Claims

1. A surgical system, comprising:

a surgical device, wherein the surgical device comprises an end effector to treat tissue;

a surgical generator in communication with the surgical device, the surgical generator comprising at least one programmable logic device, wherein the at least one programmable logic device is programmed to;

provide a drive signal to drive the end effector;

receive a frequency of the drive signal; and

determine a temperature at the end effector considering the frequency of the drive signal and executing a neural network, wherein executing the neural network comprises executing a first hidden layer node and a second hidden layer node;

wherein the first hidden layer node is programmed to;

receive a set of input variables selected from a portion of a plurality of input variables, the selected set of input variables including the frequency of the drive signal;

weight at least a portion of the input variables selected from the set of input variables to generate a weighted set of input variables;

sum the weighted set of input variables to generate a first hidden layer node output; and

wherein the second hidden layer node is programmed to;

receive a set of outputs from hidden layer nodes, wherein the set of outputs from the hidden layer nodes comprises the first hidden layer node output;

weight at least a portion of the set of outputs from the hidden layer nodes to generate a weighted set of outputs from the hidden layer nodes;

sum the weighted set of outputs from the hidden layer nodes; and

apply a transform function to the sum of the weighted set of outputs from the hidden layer nodes to generate a second layer node output.

2. The surgical system of claim 1, wherein determining the temperature at the end effector comprises training a model of end effector temperature.

3. The surgical system of claim 2, wherein training the model comprises:

providing to the model a set of input values including the frequency of the drive signal; and

comparing an output temperature of the model to a known output temperature associated with the set of input values.

4. The surgical system of claim 1, wherein the at least one programmable logic device is further programmed to receive a plurality of input variables describing the end effector, wherein the plurality of input variables describing the end effector comprises the frequency of the drive signal.

5. The surgical system of claim 4, wherein the plurality of input variables describing the end effector further comprises at least one of a voltage of the drive signal, a current of the drive signal, a power of the drive signal, an energy of the drive signal, or an impedance of an ultrasonic transducer.

6. The surgical system of claim 4, wherein the plurality of input variables describing the end effector further comprises at least one characteristic of the end effector.

7. The surgical system of claim 1, wherein the second layer node output indicates the temperature at the end effector.

8. The surgical system of claim 1, wherein, to determine the temperature at the end effector, the at least one programmable logic device is further programmed to:

execute a third layer node, wherein the third layer node is programmed to;

receive a set of outputs from second layer nodes, wherein the set of outputs from the second layer nodes comprises the second layer node output;

weight at least a portion of the set of outputs from the second layer nodes to generate a weighted set of outputs from the second layer nodes;

sum the weighted set of outputs from the second layer nodes; and

apply a third layer node transform function to the sum of the weighted set of outputs from the second layer nodes to generate a third layer node output.

9. The surgical system of claim 1, wherein the at least one programmable logic device is further programmed to:

compare the temperature at the end effector to a temperature setpoint; and

modify the drive signal to reduce a difference between the temperature at the end effector and the temperature setpoint.

10. The surgical system of claim 9, wherein the at least one programmable logic device is further programmed to execute a control algorithm to minimize the difference between the temperature at the end effector and the temperature setpoint.

11. The surgical system of claim 9, wherein modifying the drive signal to reduce the difference between the temperature at the end effector and the temperature setpoint comprises modifying a current of the drive signal.

12. The surgical system of claim 1, wherein the surgical device is an ultrasonic surgical device comprising an ultrasonic transducer, and wherein the surgical generator is further programmed to provide the drive signal to the ultrasonic transducer.

13. The surgical system of claim 1, wherein the end effector comprises a first electrode and a second electrode, and wherein the surgical generator is further programmed to provide the drive signal to the first and second electrodes.

14. The surgical system of claim 1, wherein the programmable logic device comprises at least one of a digital signal processor (DSP) or a field-programmable gate array (FPGA).

15. A method of operating a surgical device, the method comprising:

providing a drive signal to the surgical device to drive an end effector of the surgical device;

receiving a frequency of the drive signal; and

determining a temperature at the end effector considering the frequency of the drive signal, wherein determining the temperature at the end effector comprises;

receiving a set of input variables selected from a portion of a plurality of input variables, the selected set of input variables including the frequency of the drive signal;

weighting at least a portion of the input variables selected from the set of input variables to generate a weighted set of input variables;

summing the weighted set of input variables to generate a first hidden layer node output;

receiving a set of outputs from hidden layer nodes, wherein the set of outputs from the hidden layer nodes comprises the first hidden layer node output;

weighting at least a portion of the set of outputs from the hidden layer nodes to generate a weighted set of outputs from the hidden layer nodes;

summing the weighted set of outputs from the hidden layer nodes; and

applying a transform function to the sum of the weighted set of outputs from the hidden layer nodes to generate a second layer node output.

16. The method of claim 15, wherein determining the temperature at the end effector comprises training a model of end effector temperature.

17. The method of claim 15, further comprising receiving a plurality of input variables describing the end effector, wherein the plurality of input variables describing the end effector comprises the frequency of the drive signal.

18. The method of claim 15, wherein the second layer node output indicates the temperature at the end effector.

19. The method of claim 15, further comprising;

comparing the temperature at the end effector to a temperature setpoint; and

modifying the drive signal to reduce a difference between the temperature at the end effector and the temperature setpoint.

20. The method of claim 15, wherein the surgical device is an ultrasonic surgical device comprising an ultrasonic transducer, and further comprising providing the drive signal to the ultrasonic transducer.

21. The method of claim 15, wherein the end effector comprises a first electrode and a second electrode, and further comprising providing the drive signal to the first and second electrodes.

22. A surgical system, comprising:

a surgical device, wherein the surgical device comprises an end effector to treat tissue; and

a surgical generator in communication with the surgical device, the surgical generator comprising at least one programmable logic device programmed to;

provide a drive signal to drive the end effector;

receive a frequency of the drive signal; and

determine a temperature at the end effector considering the frequency of the drive signal and training a neural network model of end effector temperature, wherein training the neural network model comprises;

providing to the model a set of input values including the frequency of the drive signal and changing or varying weight values w, bias values b, and transform functions f of the set of input values such that an output temperature of the neural network model approximates a measured dependency of the output temperature for known values of the set of input values;

comparing the output temperature of the neural network model to a known output temperature associated with the set of input values; and

modifying the weight values w, the bias values b, or the transform functions f of the set of input values until an error between the output temperature of the neural network model and a corresponding measured output temperature is below a predetermined error level.

23. The surgical system of claim 22, further comprising executing the neural network model to determine the temperature at the end effector.

24. The surgical system of claim 22, wherein the at least one programmable logic device is further programmed to:

compare the temperature at the end effector to a temperature setpoint; and

modify the drive signal to reduce a difference between the temperature at the end effector and the temperature setpoint.

25. The surgical system of claim 24, wherein the at least one programmable logic device is further programmed to execute a control algorithm to minimize the difference between the temperature at the end effector and the temperature setpoint.

26. The surgical system of claim 24, wherein modifying the drive signal to reduce the difference between the temperature at the end effector and the temperature setpoint comprises modifying a current of the drive signal.