Systems and methods for dental treatment and verification
Some aspects relate to systems and methods for dental treatment and verification. An exemplary system may include a laser configured to generate a laser beam as a function of a laser parameter, a beam delivery system configured to deliver the laser beam from the laser, a hand piece configured to accept the laser beam from the beam delivery system and direct the laser beam to dental tissue, a sensor configured to detect a treatment parameter as a function of a treatment phenomenon; and a computing device configured to receive the treatment parameter from the sensor; and determine aggregated treated surfaces as a function of the treatment parameter.
1. A system for dental treatment and verification, the system comprising:
a laser configured to generate a laser beam as a function of a laser parameter;
a beam delivery system configured to deliver the laser beam from the laser;
a hand piece configured to accept the laser beam from the beam delivery system and direct the laser beam to dental tissue, wherein the laser, the beam delivery system, and the hand piece, in concert, facilitate a dental laser treatment;
a sensor configured to detect a laser treatment parameter, simultaneously with laser beam generation, as a function of a laser treatment phenomenon resulting from the dental laser treatment, wherein the laser treatment parameter comprises a laser treatment location representing direction of the laser beam coincided with generating the laser beam; and
a computing device configured to:
receive the laser treatment parameter from the sensor; and
automatically determine aggregated laser treated surfaces as a function of the laser treatment parameter and the laser parameter.
2. The system of claim 1 , wherein the sensor comprises a position sensor comprising one or more of an inertial measurement unit, an accelerometer, and a gyroscope.
3. The system of claim 2 , wherein the computing device is configured to generate a three-dimensional representation of the laser treated location.
4. The system of claim 3 , wherein the computing device is further configured to differentiate between laser treated surfaces and untreated surfaces on the three-dimensional representation of the aggregated laser treated surfaces as a function of the aggregated laser treated surfaces, the at least a laser treatment parameter, and the at least a laser parameter.
5. The system of claim 1 , wherein the computing device is further configured to:
generate a laser treatment metric as a function of one or more of the laser treatment parameter and the aggregated laser treated surfaces; and
communicate the laser treatment metric to a remote device.
6. The system of claim 5 , wherein generating the laser treatment metric comprises:
inputting one or more of the laser treatment parameter and a representation of the aggregated laser treated surfaces into a laser treatment metric machine learning model; and
outputting the laser treatment metric as a function of the laser treatment metric machine learning model and one or more of the laser treatment parameter and the representation of the aggregated laser treated surfaces.
7. The system of claim 6 wherein generating the laser treatment metric further comprises:
training the laser treatment metric machine learning model by:
inputting a laser treatment metric training set into a machine learning algorithm, wherein the laser treatment metric training set correlates laser treatment metrics to one or more of the laser treatment parameter and the representation of the aggregated laser treated surfaces; and
training the laser treatment metric machine learning model as a function of the laser treatment metric training set and the machine learning algorithm.
8. The system of claim 7 wherein, the laser treatment metric is related to a laser treatment including a dental laser cleaning treatment configured to clean and substantially remove plaque from dental hard tissue.
9. The system of claim 8 wherein, wherein the dental laser cleaning treatment is a prophylactic treatment configured to remove plaque from a patient's teeth, periodically at least once a year.
10. The system of claim 1 , further comprising a display device and wherein the computing device is further configured to:
display a representation of the aggregated laser treated surfaces to a user of the system during the dental laser treatment.
11. A method of dental treatment and verification, the method comprising:
generating, using a laser, a laser beam as a function of a laser parameter;
delivering, using a beam delivery system, the laser beam from the laser;
accepting, using a hand piece, the laser beam from the beam delivery system;
directing, using the hand piece, the laser beam to dental tissue, wherein the laser, the beam delivery system, and the hand piece, in concert, facilitate a dental laser treatment;
detecting, using a sensor, a laser treatment parameter, simultaneously with laser beam generation, as a function of a laser treatment phenomenon resulting from the dental laser treatment, wherein the laser treatment parameter comprises a laser treatment location representing direction of the laser beam coincided with generating the laser beam;
receiving, using a computing device, the laser treatment parameter from the sensor; and
automatically determining, using the computing device, aggregated laser treated surfaces as a function of the laser treatment parameter and the laser parameter.
12. The method of claim 11 , wherein the sensor comprises a position sensor comprising one or more of an inertial measurement unit, an accelerometer, and a gyroscope.
13. The method of claim 12 , further comprising generating, using the computing device, a three-dimensional representation of the laser treated location.
14. The method of claim 13 , further comprising differentiating, using the computing device, between laser treated surfaces and untreated surfaces on the three-dimensional representation of the aggregated laser treated surfaces as a function of the aggregated laser treated surfaces, the at least a laser treatment parameter, and the at least a laser parameter.
15. The method of claim 11 , further comprising:
generating, using the computing device, a laser treatment metric as a function of one or more of the laser treatment parameter and the aggregated laser treated surfaces; and
communicating, using the computing device, the laser treatment metric to a remote device.
16. The method of claim 15 , wherein generating the laser treatment metric comprises:
inputting one or more of the laser treatment parameter and a representation of the aggregated laser treated surfaces into a laser treatment metric machine learning model; and
outputting the laser treatment metric as a function of the laser treatment metric machine learning model and one or more of the laser treatment parameter and the representation of the aggregated laser treated surfaces.
17. The method of claim 16 wherein generating the laser treatment metric further comprises:
training the laser treatment metric machine learning model by:
inputting a laser treatment metric training set into a machine learning algorithm, wherein the laser treatment metric training set correlates laser treatment metrics to one or more of the laser treatment parameter and the representation of the aggregated laser treated surfaces; and
training the laser treatment metric machine learning model as a function of the laser treatment metric training set and the machine learning algorithm.
18. The method of claim 17 wherein the laser treatment metric is related to a treatment including a dental laser cleaning treatment configured to clean and substantially remove plaque from dental hard tissue.
19. The method of claim 18 wherein the dental laser cleaning treatment is a prophylactic treatment configured to remove plaque from a patient's teeth periodically at least once a year.
20. The method of claim 11 , further comprising:
displaying, using the computing device and a display device, a representation of the aggregated laser treated surfaces to a user of the system during the dental laser treatment.
21. The system of claim 1 , wherein automatically determining the aggregated laser treated surfaces comprises:
inputting the laser treatment parameter and the laser parameter into a machine learning process; and
outputting the aggregated laser treated surfaces.
22. The method of claim 11 , wherein automatically determining the aggregated laser treated surfaces comprises:
inputting the laser treatment parameter and the laser parameter into a machine learning process; and
outputting the aggregated laser treated surfaces.