SYSTEMS AND METHODS FOR DENTAL TREATMENT AND VERIFICATION

1 . A system for generating an image representative of oral tissue concurrently with dental preventative laser treatment, 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 beam performs a non-ablative treatment of the dental tissue, as a function of the laser parameter;

a sensor configured to detect a plurality of oral images as a function of oral phenomena concurrently with delivery of the laser beam to the dental tissue; and

a computing device configured to:

receive the plurality of oral images from the sensor; and

aggregate an aggregated oral image as a function of the plurality of oral images.

2 . The system of claim 1 , wherein the aggregated oral image comprises a three-dimensional representation of oral tissue.

3 . The system of claim 2 , wherein the computing device is further configured to associate the aggregated oral image with the laser parameter.

4 . The system of claim 1 , wherein aggregating the aggregated oral image further comprises:

identifying at least a common feature in a first oral image and a second oral image of the plurality of oral images; and

transforming one or more of the first oral image and the second oral image as a function of the at least a common feature.

5 . The system of claim 4 , wherein aggregating the aggregated oral image further comprises blending a demarcation between the first oral image and the second oral image.

6 . The system of claim 5 , wherein blending the demarcation comprises:

comparing pixel values between overlapping pixels in the first oral image and the second oral image; and

altering the demarcation as a function of the comparison.

7 . The system of claim 6 , wherein altering the demarcation comprises minimizing a difference in value between overlapping pixels between the first oral image and the second oral image along the demarcation.

8 . The system of claim 2 wherein the aggregated oral image has a resolution which is finer than one or more of 500, 250, 150, 100, 50, or 25 micrometers.

9 . The system of claim 1 wherein aggregating the aggregated oral image comprises:

inputting the plurality of oral images into an image aggregation machine learning model; and

outputting the aggregated oral image as a function of the plurality of oral images and the image aggregation machine learning model.

10 . The system of claim 9 wherein aggregating the aggregated oral image further comprises:

training the image aggregation machine learning model by:

inputting an image aggregation training set into a machine learning process, wherein the image aggregation training set correlates aggregated oral images to pluralities of oral images; and

training the image aggregation metric machine learning model as a function of the image aggregation training set and the machine learning algorithm.

11 . A method of generating an image representative of oral tissue concurrently with preventative dental laser treatment, the system 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 beam performs a non-ablative treatment of the dental tissue, as a function of the laser parameter;

detect, using a sensor, a plurality of oral images as a function of oral phenomena concurrently with delivery of the laser beam to the dental tissue;

receiving, using a computing device, the plurality of oral images from the sensor; and

aggregating, using the computing device, an aggregated oral image as a function of the plurality of oral images.

12 . The method of claim 11 , wherein the aggregated oral image comprises a three-dimensional representation of oral tissue.

13 . The method of claim 12 , further comprising associating, using the computing device, the aggregated oral image with the laser parameter.

14 . The method of claim 11 , wherein aggregating the aggregated oral image further comprises:

identifying at least a common feature in a first oral image and a second oral image of the plurality of oral images; and

transforming one or more of the first oral image and the second oral image as a function of the at least a common feature.

15 . The method of claim 14 , wherein aggregating the aggregated oral image further comprises blending a demarcation between the first oral image and the second oral image.

16 . The method of claim 15 , wherein blending the demarcation comprises:

comparing pixel values between overlapping pixels in the first oral image and the second oral image; and

altering the demarcation as a function of the comparison.

17 . The method of claim 16 , wherein altering the demarcation comprises minimizing a difference in value between overlapping pixels between the first oral image and the second oral image along the demarcation.

18 . The method of claim 11 , wherein the aggregated oral image has a resolution which is finer than one or more of 500, 250, 150, 100, 50, or 25 micrometers.

19 . The method of claim 11 , wherein aggregating the aggregated oral image comprises:

inputting the plurality of oral images into an image aggregation machine learning model; and

outputting the aggregated oral image as a function of the plurality of oral images and the image aggregation machine learning model.

20 . The method of claim 19 , wherein aggregating the aggregated oral image further comprises:

training the image aggregation machine learning model by:

inputting an image aggregation training set into a machine learning process, wherein the image aggregation training set correlates aggregated oral images to pluralities of oral images; and

training the image aggregation metric machine learning model as a function of the image aggregation training set and the machine learning algorithm.