Systems and methods for estimating a trend associated with dental tissue

1. A system for estimating a trend associated with dental tissue, the system comprising:

a laser configured to generate a laser beam as a function of a laser parameter including repetition rate and pulse duration to cause the laser beam to have an average power of less than 2 Watts, wherein the laser beam performs a treatment of dental hard tissue to raise the temperature of the dental hard tissue within a range of 100° C. and 1300° C., as a function of the 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, using a laser beam output configured to be positioned within an oral cavity, to the dental hard tissue;

a sensor configured to detect, by way of the laser output of the hand piece, simultaneously with the laser treatment, a plurality of oral images representing a plurality of exposed tooth surfaces that include the treated dental hard tissue; and

a computing device, in communication with the sensor, configured to:

receive the plurality of oral images from the sensor;

aggregate a first aggregated oral image as a function of a first plurality of oral images detected, during a first laser treatment, at a first time;

aggregate a second aggregated oral image as a function of a second plurality of oral images detected, during a second laser treatment, at a second time; and

estimate a trend as a function of the first aggregated oral image and the second aggregated oral image, wherein the trend includes one or more of a future bite arrangement trend or an alignment trend.

2. The system of claim 1 , further comprising a scheduling system, configured to schedule laser treatment and a patient for detecting a plurality of oral images representing a plurality of exposed tooth surfaces on a plurality of the patient's teeth.

3. The system of claim 1 , wherein the patient includes a pediatric patient.

4. The system of claim 3 , wherein the pediatric patient has deciduous teeth.

5. The system of claim 1 , wherein the trend includes one or more of a future bite arrangement trend or an alignment trend.

6. The system of claim 1 , wherein estimating the trend further comprises:

quantifying a first oral metric as a function of the first plurality of oral images;

quantifying a second oral metric as a function of the second plurality of oral images; and

estimating the trend as a function of the first oral metric and the second oral metric.

7. The system of claim 6 , wherein quantifying the first oral metric further comprises:

inputting into an oral metric machine learning model at least one image from the first plurality of oral images; and

outputting the first oral metric from the oral metric machine learning model as a function of the at least one image from the first plurality of oral images.

8. The system of claim 7 , wherein quantifying the first oral metric further comprises:

receiving an oral metric machine learning training set that correlates oral metrics to oral images; and

training the oral metric machine learning model as a function of the oral metric machine learning training set.

9. The system of claim 6 , wherein estimating the trend further comprises:

inputting the first oral metric and the second oral metric into a prediction machine learning process; and

outputting the trend as a function of the prediction machine learning process, the first oral metric, and the second oral metric.

10. The system of claim 9 , wherein estimating the trend further comprises:

receiving a prediction training set that includes longitudinal oral metrics;

training a prediction machine learning model as a function of prediction training set and a machine learning algorithm;

inputting the first oral metric and the second oral metric into a prediction machine learning model; and

outputting the trend as a function of the prediction machine learning model, the first oral metric, and the second oral metric.

11. A method of estimating a trend associated with dental tissue, the method comprising:

generating, using a laser, a laser beam as a function of a laser parameter including repetition rate and pulse duration to cause the laser beam to have an average power of less than 2 Watts, wherein the laser beam performs a treatment of dental hard tissue to raise the temperature of the dental hard tissue within a range of 100° C. and 1300° C., as a function of the 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, using a laser beam output configured to be positioned within an oral cavity, to the dental hard tissue;

detecting, using a sensor, by way of the laser beam output of the hand piece, simultaneously with the laser treatment, a plurality of oral images that include the treated dental hard tissue;

receiving, using a computing device in communication with the sensor, the plurality of oral images from the sensor;

aggregating, using the computing device, a first aggregated oral image as a function of a first plurality of oral images detected during a first laser treatment at a first time;

aggregating, using the computing device, a second aggregated oral image as a function of a second plurality of oral images detected during a second laser treatment at a second time; and

estimating, using the computing device, a future bite arrangement as a function of the first aggregated oral image and the second aggregated oral image, wherein the trend includes one or more of a future bite arrangement trend or an alignment trend.

12. The method of claim 11 further comprising: scheduling, using a scheduling system, the laser treatment and a patient for detecting a plurality of oral images representing a plurality of exposed tooth surfaces on a plurality of the patient's teeth.

13. The method of claim 11 , wherein the patient includes a pediatric patient.

14. The method of claim 13 , wherein the pediatric patient has deciduous teeth.

15. The method of claim 11 , wherein the trend includes one or more of a future bite arrangement trend, or an alignment trend.

16. The method of claim 11 , wherein estimating the trend further comprises:

quantifying a first oral metric as a function of the first plurality of oral images;

quantifying a second or metric as a function of the second plurality of oral images; and

estimating the trend as a function of the first oral metric and the second oral metric.

17. The method of claim 16 , wherein quantifying the first oral metric further comprises:

inputting into an oral metric machine learning model at least one image form the first plurality of oral images; and

outputting the first oral metric from the oral metric machine learning model as a function of the at least one image from the first plurality of oral images.

18. The method of claim 17 , wherein quantifying the first oral metric further comprises:

receiving an oral metric machine learning training set that correlates oral metrics to oral images; and

training the oral metric machine learning model as a function of the oral metric machine learning training set.

19. The method of claim 16 , wherein estimating the trend further comprises:

inputting the first oral metric and the second oral metric into a prediction machine learning process; and

outputting the trend as a function of the prediction machine learning process, the first oral metric, and the second oral metric.

20. The method of claim 19 , wherein estimating the trend further comprises:

receiving a prediction training set that includes longitudinal oral metrics;

training a prediction machine learning model as a function of prediction training set and a machine learning algorithm;

inputting the first oral metric and the second oral metric into a prediction machine learning model; and

outputting the trend as a function of the prediction machine learning model, the first oral metric, and the second oral metric.