Artificial intelligence guidance system for robotic surgery
This invention is a system and method for utilizing artificial intelligence to operate a surgical robot (e.g., to perform a laminectomy), including a surgical robot, an artificial intelligence guidance system, an image recognition system, an image recognition database, and a database of past procedures with sensor data, electronic medical records, and imaging data. The image recognition system may identify the tissue type present in the patient and if it is the desired tissue type, the AI guidance system may remove a layer of that tissue with the end effector on the surgical robot, and have the surgeon define the tissue type if the image recognition system identified the tissue as anything other than the desired tissue type.
1. A computer-implemented method for analyzing tissue during a surgical procedure, the surgical procedure using a surgical robot, the method comprising:
obtaining an image of a surgical point of interest during the surgical procedure;
determining an identified tissue type associated with at least a portion of the surgical point of interest, at least in part, by providing at least a portion of the image to a tissue recognition system;
retrieving data related to the identified tissue type from a database, the database comprising stored tissue data associated with tissue of the identified tissue type; and
comparing tissue characteristics of the retrieved data with the surgical point of interest during the procedure.
2. The computer-implemented method of claim 1 , wherein comparing tissue characteristics comprises:
determining an average volume of the identified tissue type in the retrieved data; and
comparing a volume of the identified tissue type at the surgical point of interest with the average volume.
3. The computer-implemented method of claim 1 , wherein retrieving data related to the identified tissue type from the database comprises:
determining a first volume associated with the surgical point of interest; and
filtering the data related to the identified tissue type from the database based upon the first volume associated with the surgical point of interest.
4. The computer-implemented method of claim 3 , wherein
data related to the identified tissue type from the database are associated with a plurality of volumes, the plurality of volumes associated with a distribution having a standard deviation, and wherein
filtering the data related to the identified tissue type from the database comprises selecting data in the database associated with volumes not less than the first volume associated with the surgical point of interest minus the standard deviation and not greater than the first volume associated with the surgical point of interest plus the standard deviation.
5. The computer-implemented method of claim 3 , wherein the tissue recognition system determines both a type of tissue and the tissue's position in the at least a portion of the image.
6. The computer-implemented method of claim 5 , wherein the tissue recognition system was trained, at least in part, upon temporal sequences of images of tissue.
7. The computer-implemented method of claim 1 , the method further comprising:
inputting a volume of the identified tissue type into the database.
8. A non-transitory computer-readable medium comprising instructions configured to cause a computer system to perform a method for analyzing tissue during a surgical procedure, the surgical procedure using a surgical robot, the method comprising:
obtaining an image of a surgical point of interest during the surgical procedure;
determining an identified tissue type associated with at least a portion of the surgical point of interest, at least in part, by providing at least a portion of the image to a tissue recognition system;
retrieving data related to the identified tissue type from a database, the database comprising stored tissue data associated with tissue of the identified tissue type; and
comparing tissue characteristics of the retrieved data with the surgical point of interest during the procedure.
9. The non-transitory computer-readable medium of claim 8 , wherein comparing tissue characteristics comprises:
determining an average volume of the identified tissue type in the retrieved data; and
comparing a volume of the identified tissue type at the surgical point of interest with the average volume.
10. The non-transitory computer-readable medium of claim 8 , wherein retrieving data related to the identified tissue type from the database comprises:
determining a first volume associated with the surgical point of interest; and
filtering the data related to the identified tissue type from the database based upon the first volume associated with the surgical point of interest.
11. The non-transitory computer-readable medium of claim 10 , wherein
data related to the identified tissue type from the database are associated with a plurality of volumes, the plurality of volumes associated with a distribution having a standard deviation, and wherein
filtering the data related to the identified tissue type from the database comprises selecting data in the database associated with volumes not less than the first volume associated with the surgical point of interest minus the standard deviation and not greater than the first volume associated with the surgical point of interest plus the standard deviation.
12. The non-transitory computer-readable medium of claim 10 , wherein the tissue recognition system determines both a type of tissue and the tissue's position in the at least a portion of the image.
13. The non-transitory computer-readable medium of claim 12 , wherein the tissue recognition system was trained, at least in part, upon temporal sequences of images of tissue.
14. The non-transitory computer-readable medium of claim 8 , the method further comprising:
inputting a volume of the identified tissue type into the database.
15. A system configured to analyze tissue during a surgical procedure, the surgical procedure using a surgical robot, the system comprising:
at least one processor; and
a memory comprising instructions configured, when executed by the at least one processor, to cause the system to perform the following method:
obtaining an image of a surgical point of interest during the surgical procedure;
determining an identified tissue type associated with at least a portion of the surgical point of interest, at least in part, by providing at least a portion of the image to a tissue recognition system;
retrieving data related to the identified tissue type from a database, the database comprising stored tissue data associated with tissue of the identified tissue type; and
comparing tissue characteristics of the retrieved data with the surgical point of interest during the procedure.
16. The system of claim 15 , wherein comparing tissue characteristics comprises:
determining an average volume of the identified tissue type in the retrieved data; and
comparing a volume of the identified tissue type at the surgical point of interest with the average volume.
17. The system of claim 15 , wherein retrieving data related to the identified tissue type from the database comprises:
determining a first volume associated with the surgical point of interest; and
filtering the data related to the identified tissue type from the database based upon the first volume associated with the surgical point of interest.
18. The system of claim 17 , wherein
data related to the identified tissue type from the database are associated with a plurality of volumes, the plurality of volumes associated with a distribution having a standard deviation, and wherein
filtering the data related to the identified tissue type from the database comprises selecting data in the database associated with volumes not less than the first volume associated with the surgical point of interest minus the standard deviation and not greater than the first volume associated with the surgical point of interest plus the standard deviation.
19. The system of claim 17 , wherein the tissue recognition system determines both a type of tissue and the tissue's position in the at least a portion of the image.
20. The system of claim 19 , wherein the tissue recognition system was trained, at least in part, upon temporal sequences of images of tissue.