SYSTEMS AND METHODS FOR ESTIMATING A TREND ASSOCIATED WITH DENTAL TISSUE
Some aspects relate to systems and methods for estimating a trend associated with dental tissue. An exemplary system may include a sensor configured to periodically detect a plurality of oral images representing a plurality of exposed tooth surfaces and a computing device 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 at a first time, aggregate a second aggregated oral image as a function of a second plurality of oral images detected at a second time, and estimate a trend as a function of the first aggregated oral image and the second aggregated oral image.
1 . A system for estimating a trend associated with dental tissue, the system comprising:
a sensor configured to detect, a plurality of oral images representing a plurality of exposed tooth surfaces of a patient that include 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, at a first time;
aggregate a second aggregated oral image as a function of a second plurality of oral images detected, 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 , wherein estimating the trend further comprises:
quantifying a first alignment metric as a function of the first plurality of oral images;
quantifying a second alignment metric as a function of the second plurality of oral images; and
estimating the one or more of the future bite arrangement trend or the alignment trend as a function of the first alignment metric and the second alignment metric.
3 . The system of claim 2 , wherein quantifying the first alignment metric further comprises:
inputting into an alignment metric machine learning model at least one image from the first plurality of oral images; and
outputting the first alignment metric from the alignment metric machine learning model as a function of the at least one image from the first plurality of oral images.
4 . The system of claim 3 , wherein quantifying the first alignment metric further comprises:
receiving an alignment metric machine learning training set that correlates alignment metrics to oral images; and
training the alignment metric machine learning model as a function of the alignment metric machine learning training set.
5 . The system of claim 2 , wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises:
inputting the first alignment metric and the second alignment metric into an alignment prediction machine learning process; and
outputting the trend as a function of the alignment prediction machine learning process, the first alignment metric, and the second alignment metric.
6 . The system of claim 5 , wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises:
receiving a bite estimation training data comprising correlates oral images to subsequent bite arrangements;
training the alignment prediction machine learning model as a function of the bite estimation training data and a machine learning algorithm;
inputting the first alignment metric and the second alignment metric into the trained alignment prediction machine learning model; and
outputting the one or more of the future bite arrangement trend or the alignment trend as a function of the trained alignment prediction machine learning model, the first alignment metric, and the second alignment metric.
7 . The system of claim 6 , wherein the patient includes a pediatric patient.
8 . The system of claim 7 , wherein the pediatric patient has deciduous teeth.
9 . The system of claim 8 , wherein the pediatric patient's bite arrangement changes between the first time and the second time.
10 . The system of claim 9 , wherein the sensor comprises a camera further comprising:
a lens; and
an image sensor having a global shutter.
11 . A method of estimating a trend associated with dental tissue, the system comprising:
detecting, using a sensor, a plurality of oral images representing a plurality of exposed tooth surfaces of a patient that include 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, 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, at a second time; and
estimating, using the computing device, 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.
12 . The method of claim 11 , wherein estimating the trend further comprises:
quantifying a first alignment metric as a function of the first plurality of oral images;
quantifying a second alignment metric as a function of the second plurality of oral images; and
estimating the one or more of the future bite arrangement trend or the alignment trend as a function of the first alignment metric and the second alignment metric.
13 . The method of claim 12 , wherein quantifying the first alignment metric further comprises:
inputting into an alignment metric machine learning model at least one image from the first plurality of oral images; and
outputting the first alignment metric from the alignment metric machine learning model as a function of the at least one image from the first plurality of oral images.
14 . The method of claim 13 , wherein quantifying the first alignment metric further comprises:
receiving an alignment metric machine learning training set that correlates alignment metrics to oral images; and
training the alignment metric machine learning model as a function of the alignment metric machine learning training set.
15 . The method of claim 12 , wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises:
inputting the first alignment metric and the second alignment metric into an alignment prediction machine learning process; and
outputting the trend as a function of the alignment prediction machine learning process, the first alignment metric, and the second alignment metric.
16 . The method of claim 15 , wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises:
receiving a bite estimation training data comprising correlates oral images to subsequent bite arrangements;
training the alignment prediction machine learning model as a function of the bite estimation training data and a machine learning algorithm;
inputting the first alignment metric and the second alignment metric into the trained alignment prediction machine learning model; and
outputting the one or more of the future bite arrangement trend or the alignment trend as a function of the trained alignment prediction machine learning model, the first alignment metric, and the second alignment metric.
17 . The method of claim 16 , wherein the patient includes a pediatric patient.
18 . The method of claim 17 , wherein the pediatric patient has deciduous teeth.
19 . The method of claim 18 , wherein the pediatric patient's bite arrangement changes between the first time and the second time.
20 . The method of claim 19 , wherein the sensor comprises a camera further comprising:
a lens; and
an image sensor having a global shutter.