Diagnosis of dental caries using artificial intelligence

Abstract

A device for diagnosing a dental condition is disclosed. The device captures image data representative of a patient's teeth based on data obtained from a hardware device that scans the teeth. The device inputs the image data into a first supervised machine learning model and receives as output a plurality of biomarkers from the first supervised machine learning model, each biomarker corresponding to a different location on the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model and receives as output a diagnosis of the dental condition from the second supervised machine learning model.

Description

[Background technology]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 219,216, filed December 17, 2021, the contents of which are incorporated by reference herein in their entirety.

[0002] (background) The risk of dental caries (caries) is widespread throughout society and any human being is at risk for caries, especially due to a diet rich in carbohydrates and ultra-processed foods. Dental caries is a major cause of disability, pain, and health care costs. Effective treatments exist to prevent and ameliorate dental caries, especially when it is found in its early stages, when it is still asymptomatic. However, adherence to early diagnosis is suboptimal and leads to unnecessary progression of caries when the standard of care is dental examination by a dentist and additionally x-rays. While standard of care dental examination by a dentist and additionally x-rays is effective in diagnosing caries early, this is difficult to scale due to the need and shortage of either highly skilled professionals (i.e. dentists) or highly skilled x-ray technicians, with requirements that vary widely by state, as well as exposure to x-rays that can have long-term health effects, plus the need for expert evaluation through some forms of telehealth, which leads to relatively high costs and reduces access. Summary of the Invention [Means for solving the problem]

[0003] (Abstract) Therefore, an autonomous dental screening artificial intelligence (AI) technology that a) does not require x-rays and b) does not require expertise to evaluate the images would be highly beneficial for increasing access, lowering costs and thereby avoiding permanent disability and costs. Specifically, deep penetration optical coherence tomography (DPOCT) is a non-radiative optical technology based on interferograms of low coherence light to penetrate up to 4-5 mm into dental elements, including molars, which allows all sides of each element to be imaged, including any cavities on all sides. In particular, it allows the so-called interdental cavities between two elements to be imaged, which is where 60% of cavities occur and are inaccessible to visual inspection. Autonomous AI is a technology that allows real-time point-of-care diagnosis of cavities from multi-planar DPOCT images.

[0004] Since DPOCT cannot penetrate the entire tooth, especially the larger molars, imaging of each accessible surface may be required. This can be achieved through multiplanar DPOCT using a small probe or multiple probes covering at least the facial, lingual and occlusal surfaces of the molar row, illuminating with low-coherence light and collecting the reflected low-coherence light to a DPOCT device that calculates an interference image, using a so-called swept-source or Michelson interferometry approach for each plane in the domain OCT interference pattern that allows for fast scanning of the entire element, and then obtaining one or more B-scans (two-sided scans) from these three planes coaxial with the probe, and as the probe is advanced back and forth manually or with a servo, a multiplanar 3D PDOCT volume is collected. In one example, the probe is applied to the other three rows of molars to calculate a multi-sided image of all molar elements in the patient.

[0005] The autonomous AI has as input a multi-sided DPOCT volume and outputs either the likelihood of a cavity in the volume or a diagnostic dichotomous or multi-level presence / absence or severity of a cavity in the volume. Other diagnostically relevant outputs may also be made. The AI ​​can be constructed as a hybrid partially independent biomarker multi-detector AI with a fusion stage, or as a multi-image based convolutional neural network using deep learning, shallow learning, recurrent networks, or any combination of such AI designs known to those skilled in the art. An exemplary AI of this approach is disclosed in further detail in commonly owned U.S. Patent No. 10,115,194, issued October 30, 2018, the disclosure of which is hereby incorporated by reference herein in its entirety. Augmentation and transfer learning approaches, including unsupervised transfer learning, can be used as well. DPOCT training images labeled with the presence or absence or location or likelihood or probability of a cavity can be used to train such an AI system, or in the case of a multi-detector based approach, labeled image samples can be used to train a detector, or a mathematical description of the biomarkers can be used to design the detector. DPOCT is merely exemplary, and whenever DPOCT or any other imaging technique is mentioned, any other form of imaging that captures the functionality described herein with respect to DPOCT (e.g., using low coherence light, sound, vibration, or any other waveform generating technique using interferometric interference techniques) equally applies. Additionally, any low coherence tomography (LCT) technique also equally applies when DPOCT is mentioned herein, including ultraviolet, infrared, dental penetrating or deep penetrating optical coherence tomography, microwave, visible light LCT, ultrasound imaging techniques, and the like. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 illustrates one embodiment of a system environment for implementing a caries determination tool.

[0007] [Diagram 2] FIG. 2 illustrates one embodiment of exemplary modules and databases used by the treatment decision tool in using artificial intelligence to directly output a treatment.

[0008] [Diagram 3] FIG. 3 shows one embodiment of further details of the sub-modules used by the caries decision module of the treatment decision tool.

[0009] [Figure 4] FIG. 4 shows one embodiment of a DPOCT intensity map.

[0010] [Diagram 5] FIG. 5 illustrates an exemplary process for determining a diagnosis of a dental condition using a supervised machine learning approach.

[0011] The figures depict various embodiments of the present invention for illustrative purposes only. Those skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be used without departing from the principles of the invention described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (Detailed Description) (a) Overview Figure 1 illustrates one embodiment of a system environment for implementing a caries determination tool. As depicted in Figure 1, environment 100 includes an imaging instrument 110, a network 120, a caries determination tool 130, and patient data 140. The elements of environment 100 are merely exemplary, and fewer or more elements may be incorporated into environment 100 to achieve the functionality disclosed herein.

[0013] The imaging instrument 110 may be any instrument that captures images of the patient's teeth. The imaging instrument 110 may include a camera sensor, a DPOCT sensor, and any other long-wavelength sensor that can capture indications of dental problems and caries. The imaging instrument 110 may obtain credentials and other information about the patient. These may be entered by a technician through a user interface, or directly by the patient. That is, the imaging instrument 110 may include a keyboard or touch screen interface for entering data about the patient, plus one or more imaging components that capture images. The imaging components may be any known imaging components (e.g., a slider that is slid over and around the teeth by the technician). The imaging instrument 110 may capture images and prompts for patient information based on running software controlled or otherwise dispensed by the caries determination tool 130. Because the imaging device 110 uses non-X-ray technology, it can be operated by individuals not qualified to operate X-ray equipment (e.g., technicians with minimal training and no more than a high school education), thus enabling scalability of caries detection.

[0014] The imaging instrument 110 may use multi-planar OCT technology in addition to the DPOCT sensor. For example, multi-planar OCT may be used to capture images showing caries that DPOCT may not be able to capture because DPOCT cannot fully penetrate some teeth. The imaging instrument 110 may use multi-planar OCT to simultaneously or sequentially measure captured images (e.g., 2D images) and convert these images into 3D or other multi-volume measurements of any parameterization of the caries (or defects) at any given point on the tooth. The AI ​​disclosed herein may process multiple 3D volumes or other multi-volumes captured together in this manner. For example, if multiple teeth or multiple sections of teeth are scanned together, the 3D or other multi-volume image may be a capture of those multiple teeth and / or multiple sections of teeth, and may be processed with respect to each other if the information in adjacent volumes constrains each other (e.g., by occlusion).

[0015] The network 120 may be any data network capable of transmitting data communications between the client device 110 and the treatment decision tool 130. The network 120 may be, for example, the Internet, a local area network, a wide area network, or any other network.

[0016] Cavity determination tool 130 receives and processes images from imaging instrument 110. Cavity determination tool 130 may also receive other patient information from imaging instrument 110 and may retrieve information from patient data 140 about the patient. Based on some or all of the received information, caries determination tool 130 may output a status of the patient. Further details of the operation of caries determination tool 130 are discussed below with reference to FIG. 2. The operation of treatment decision tool 130 may be instantiated in whole or in part on imaging instrument 110 (e.g., through an application installed on imaging instrument 110 or accessed by imaging instrument 110 through a browser).

[0017] The patient data 140 is a database that stores records of data of one or more patients. The patient data 140 can be hospital records, patient personal records, doctor's notes, and the like. The patient data 140 can be co-located with the imaging instrument 110 and / or the caries determination tool 130. In an exemplary embodiment, the patient data 140 includes profile information about the patient (e.g., age, sex, race, etc.), previous images (if any) captured by the imaging instrument 110 of the patient's teeth, and previous conditions (if any) detected by the caries determination tool 130. The patient data 140 can be integrated with a dental clinic where a dentist can indicate a treatment (e.g., filling a cavity) or a decision that no treatment was necessary (e.g., a false positive) in a consultation prompted by a condition identified by the caries determination tool 130. The patient data 140 can be used as a baseline to detect caries progression as images of the patient's teeth are captured over time. The patient data 140 may be used to retrain the machine learning model used by the caries determination tool 130 (e.g., relabeling the training data as indicating that it does not correspond to a caries condition if a false positive is indicated by the dentist). In some embodiments, the use of the patient data 140 is optional (e.g., in cases where the patient data 140 may contain noise that reduces the accuracy or other performance of the AI ​​model).

[0018] 2 illustrates one embodiment of exemplary modules and databases used by the caries determination tool in using artificial intelligence to output tooth conditions. As depicted in FIG. 2, the caries determination tool 130 includes an image capture module 231, a caries determination module 232, and an image pre-processing module 233, as well as a machine learning module 241. The modules and databases depicted in FIG. 2 are merely exemplary, and the caries determination tool 130 may include more or fewer modules and / or databases and still achieve the functionality described herein. Additionally, the modules and / or databases of the treatment decision tool 130 may be instantiated in whole or in part on the client device 110 and / or one or more servers.

[0019] The image capture module 231 performs activities related to causing the imaging instrument 110 to capture information and transmit that information to the caries determination tool 130 and / or the patient data database 140. This activity may include directing the imaging instrument 110 (e.g., via a locally installed application or via a remote command) to capture images of the patient's teeth and transmit them to the image capture module 231 for processing. In response to receiving the images, the image capture module 231 optionally performs pre-processing on the images (e.g., with the image pre-processing module 233). Pre-processing may include optimizing the images, removing artifacts, verifying that the images comply with one or more protocols (e.g., the images show certain teeth that they are supposed to show), and the like.

[0020] In some embodiments, the image capture module 231 prompts the technician to perform certain activities. For example, the technician may be prompted by the image capture module 231 to capture images of certain teeth, gum areas, slide the instrument to capture images from certain angles, or any other manipulation of the image capture device. Images captured during actions corresponding to these prompts are verified through a pre-processing protocol that validates images that comply with the protocol, and in response to them not complying with the protocol, the image capture module 231 may prompt the technician to re-capture such images.

[0021] The caries determination module 232 determines one or more conditions of the patient based on the captured images. In one embodiment, the caries determination module 232 determines a condition that is a binary decision of whether the patient may have caries or not. That is, the output of the caries determination module 232 is either that the patient may have caries and should see a dentist, or that the patient does not have caries and does not need to see a dentist. In other embodiments, the caries determination module 232 may output a diagnosis (e.g., the upper right incisor has caries through the enamel). Additionally or alternatively, the caries determination module 232 may output a treatment (e.g., a filling is needed on the upper right incisor). Additionally or alternatively, the caries determination module 232 may output a risk urgency (e.g., a binary "urgent" or "not urgent", a scale of 1 to 10 that increases in proportion to urgency, or any other manner of indication) based on how likely the caries will become serious within a threshold time period (e.g., needing a filling now, but needing a root canal in a month). Additionally or alternatively, the caries determination module 232 may output an indication of any risk posed by the caries (e.g., developing periodontal disease if the caries is not treated).

[0022] The caries determination module 232 may make these determinations by using machine learning. In an embodiment, one or more longwave images (e.g., DPOCT images) are obtained by the image capture module 231 and input into the machine learning model by the caries determination module 232. The machine learning model is trained using training data that labels images with one or more conditions (e.g., the training images are labeled with indications of whether a dental consultation is required, whether a particular diagnosis exists, urgency, whether a certain treatment is required, risk, and the like). The machine learning model may be a neural network (e.g., a convolutional neural network), a deep learning network, or any other type of machine learning model. The machine learning model may be trained with other auxiliary data, such as patient profile information to be paired with the patient image, and may accept as input a given patient profile data in addition to the longwave image. Optical images showing tooth surfaces, gum surfaces, and the like may be used as inputs to the machine learning model or portions thereof (e.g., tooth boundary polygons).

[0023] A single model method may be accurate but may allow bias. For example, if the captured image contains gum pigment, the gum pigment may form the basis for training a model to determine a condition. For this purpose, two machine learning models may be used. A first machine learning model may accept an image as input and output, for each region of the tooth, the likelihood that the tooth contains a certain property (e.g., caries, stain, previous fillings or sealants, or other dental treatments, and the like), or some other indication of the image (e.g., intensity at each location of the image). A second machine learning model may be trained to take as input a vector of likelihoods (or other indications) for a given tooth or set of teeth and output a condition based on that vector. This removes the risk of unnecessary factors biasing the condition determination. In an embodiment, the likelihood vector may be supplemented with auxiliary information, such as a visible light image of the periodontal tissue.

[0024] In an embodiment, patient data 140 may be input into the model in conjunction with the longwave image. For example, a baseline image from a previous image may be input, or image processing may be used to identify differences between the baseline image and the new image. The differences may be input into a machine learning model(s) to output a disease state. The machine learning model may be trained using historical data of the same tooth at different times, and differences in intensity of biomarkers over time are labeled to indicate the severity of the condition (e.g., worsening or improving cavities, worsening staining, etc.).

[0025] 3 shows one embodiment of further details of the sub-modules used by the caries determination module of the treatment decision tool. As depicted in FIG. 3, the sub-modules of the caries determination module 232 can include a DPOCT abstraction module 315, an intensity map module 325, a biomarker determination module 335, and a caries diagnosis module 345. Fewer or more modules can be used to achieve the functionality described herein.

[0026] As described above, in an embodiment, the caries determination module 232 inputs the image into the machine learning model. However, in addition or by design, one or more abstractions of the DPOCT image may be input into the machine learning model. Abstraction as described herein may refer to a representation of the image. For example, the DPOCT image may include pixels of various intensities. The abstraction may include a difference in intensity between adjacent pixels in respective regions of the image, and the intensity difference may be input into the machine learning model. In an embodiment, the DPOCT abstraction module 315 may determine regions that have at least a threshold intensity difference (e.g., average intensity difference or median intensity difference) for those regions and adjacent pixels, and may use a map of those regions as input into the machine learning model. Ground truth training data may exist for any form of abstraction that labels the abstraction. In an embodiment where a single machine learning model is used, the training data may include a label of the caries diagnosis corresponding to the abstraction. In an embodiment where the image is input into a first machine learning model, the abstraction may be labeled with a ground truth for any of the aforementioned output classifications.

[0027] The intensity map 325 may generate one or more intensity maps for an image (e.g., a DPOCT image). The intensity maps may indicate the intensity at each pixel of the image for a given property (e.g., reflectance, refractive index, brightness). These intensity maps may be used for any purpose (e.g., to abstract intensity differences, for direct input into a machine learning model, and the like). Generally, healthy dental materials do not absorb much of the DPOCT scan. However, caries or other artifacts (e.g., cavity fillings) may absorb relatively more and have higher intensity. Therefore, ground truth data labeling classifications (e.g., caries, stains, previous fillings) for the intensity maps may enable the machine learning model to output classifications based on the intensity maps.

[0028] In one embodiment, the intensity map module 325 may determine the intensity map using the intensity difference between the intensity of the most recent image and the intensity of a previous image from the patient data 140 (e.g., the intensity difference of the current image relative to a previous image of the same tooth).

[0029] The caries determination module 232 may input either the full color image, the DPOCT image, and any mentioned abstractions, either alone or together, into a machine learning model trained to either directly output a diagnosis, or to output a likelihood for a region of the tooth containing a certain property. When a machine learning model is trained to take two or more of these inputs together, the ground truth for each type of input is leveraged to optimally determine the associated classification.

[0030] In an embodiment, the biomarker determination module 335 applies heuristics to the intensity map and / or abstractions representing intensity differences, where the heuristics identify parameters for the size of a region and / or the intensity change of a region to be classified as a biomarker. These biomarkers may be input into a single machine learning model that outputs a diagnosis based on the biomarkers. In an embodiment where two machine learning models are used, a first machine learning model may be trained to detect intensity changes from the intensity map and output a respective classification for each location on the tooth based on the intensity change. From the classification and optionally other heuristics (e.g., location size), the biomarker determination module 335 may determine the likelihood that each region with a given intensity change forms a particular type of biomarker (e.g., cavity, previous filling, etc.). The biomarker determination module 335 may compare these likelihoods to previous likelihoods from the patient data 140 (e.g., from images captured in a previous scan of an earlier examination of the same tooth) and use the comparison as input to the second machine learning model. The biomarker determination module 335 may additionally or alternatively annotate biomarkers using intensity differences of biomarkers from earlier scans of the patient 140 for input into a second machine learning model.

[0031] The caries diagnosis module 345 generates a diagnosis for one or more regions of the tooth. In an embodiment, the caries diagnosis module 345 determines a diagnosis for each biomarker of the tooth. That is, the machine learning model (e.g., a single model, or a second machine learning model in a two model approach) may output a probability that the tooth (or biomarkers) have one or more states (e.g., cavity, previous filling, stain, etc.) for either the tooth as a whole or different biomarkers of the tooth. The caries diagnosis module module 345 may apply a threshold such that a probability higher than a minimum threshold leads to the caries diagnosis module 345 determining that the corresponding state applies as a diagnosis. Thus, a tooth with three biomarkers (one for cavity, one for previous filling, and one for stain) may have each of those biomarkers diagnosed individually in connection with its location on the tooth. Alternatively, the tooth may be labeled externally to have each of those three states without labeling different regions of the tooth accordingly. Finally, the caries diagnosis module 345 may output an abstraction of the diagnosis. For example, heuristics may be applied to indicate whether to classify the diagnosis as "go see dentist" or "no problems detected." For example, classifications indicating staining and previous fillings on the tooth may lead to "no problems detected" based on those heuristics, while any findings of cavities or pre-cavity material may lead to a classification of "go see dentist."

[0032] FIG. 4 illustrates one embodiment of a DPOCT intensity map. As shown in FIG. 4, image 400 illustrates a tooth color image and corresponding intensity map. These intensity maps can be used as training data where an expert indicates labels (e.g., enamel, dentin, secondary caries, fissures, and composite fillings). The labels can be for any type of tooth, such as healthy molars versus reconstructed molars. The patient data 140 can be used to determine the tooth type (e.g., if the patient data 140 determines that a molar is reconstructed, and then the machine learning model matches the reconstructed molar to the ground truth data). Different machine learning models can be trained for different tooth types, and the caries determination module 232 can select the machine learning model that matches the patient's tooth type as determined from the patient data 140 to diagnose the condition for a given tooth. This can improve efficiency and reduce noise in the output of the machine learning model by using a better target set of ground truth data.

[0033] FIG. 5 illustrates an exemplary process for determining a diagnosis of a dental condition using a supervised machine learning approach. As depicted in FIG. 5, the process 500 begins with the caries determination tool 130 capturing 502 (e.g., using the image capture module 231) image data representation of a patient's teeth based on data obtained from a hardware device that scans the teeth. In an embodiment, the data obtained from the hardware device that scans the teeth may include deep penetration optical coherence tomography (DPOCT) data. The image data may include or be an intensity map reflecting one or more optical properties of the teeth based on the DPOCT data.

[0034] The caries determination module 130 inputs 504 the image data into a first supervised machine learning model (e.g., from the machine learning model 241). The first supervised machine learning model, in an embodiment, may be trained to detect intensity changes between regions in the intensity map and output a respective classification for each different location of the tooth based on the intensity changes, each respective classification forming a biomarker. In an embodiment, the first supervised machine learning model may additionally take a tooth color image as an input and output a biomarker based on both the image and the intensity map.

[0035] The caries determination module 130 receives 506 a plurality of biomarkers from the first supervised machine learning model as output, each biomarker corresponding to a different location of the tooth. If the input includes a color image, the output of the first machine learning model may exclude color data from the color image (e.g., to eliminate bias in the use of the second machine learning model). The caries determination module 130 inputs 508 the plurality of biomarkers into the second supervised machine learning model and receives 510 a diagnosis of the tooth condition from the second supervised machine learning model as output. In an embodiment, tooth history biomarkers (e.g., from the patient data 140 where previous images were captured and decisions were made from the first model and / or the second model) are retrieved and input into the second machine learning model. In an embodiment where intensity information is input into the model, intensity differences may be input into the second machine learning model of current intensity versus previous intensity, since changes in intensity over time may be informative for certain classifications. In an embodiment, the diagnosis may include a classification for each biomarker of a given tooth.

[0036] Although the disclosure herein refers to human patients, the patient need not be human. Dogs, horses, etc. may have cavities using the systems and methods disclosed herein. For example, training data may be input for the animal's teeth to train an animal-specific machine learning model(s) to perform a condition diagnosis. The machine learning model may be trained using cross-species training data in some embodiments. The correct answer in any of these training sets may be based on one or more of outcomes from treatment, pain, and clinician labeling.

[0037] (b) Summary The foregoing description of embodiments of the present invention has been presented for purposes of illustration. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Those skilled in the art will recognize that numerous modifications and variations are possible in light of the above disclosure.

[0038] Some portions of this description describe embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to effectively convey the substance of their work to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electronic circuits, microcode, or the like. Moreover, without loss of generality, it has also proven convenient at times to refer to these arrangements of operations as modules. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combination thereof.

[0039] Any of the steps, operations or processes described herein may be performed or implemented using one or more hardware or software modules, alone or in combination with other devices. In an embodiment, a software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the steps, operations or processes described.

[0040] An embodiment of the present invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes and / or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory tangible computer-readable storage medium or any type of medium suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing system referred to herein may include a single processor or may be an architecture that uses multiple processor designs for increased computing power.

[0041] Embodiments of the invention may also relate to products produced by the computational processes described herein. Such products may comprise information resulting from the computational processes, which information is stored on a non-transitory, tangible, computer-readable storage medium, and may include any embodiment of a computer program product or other data combination described herein.

[0042] Finally, the language used herein has been selected primarily for readability and instructional purposes, and not for the purpose of delineating or limiting the subject matter of the present invention. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims issued in an application based thereon. In addition, the disclosure of embodiments of the present invention is intended to be illustrative but not limiting of the scope of the invention, which is set forth in the following claims.

Claims

1. 1. A method of diagnosing a dental condition, the method being implemented on a computer having one or more processors, the method comprising: the one or more processors capturing image data indicative of the patient's teeth based on data obtained from a hardware device scanning the teeth, the image data obtained from the hardware device scanning the teeth comprising deep penetration optical coherence tomography (DPOCT) data, and the image data comprising an intensity map reflecting one or more optical properties of the teeth based on the DPOCT data; the one or more processors input the image data into a first supervised machine learning model, the first supervised machine learning model being trained to detect intensity variations between regions in the intensity map and to output a respective classification for each different location of the tooth based on the intensity variations, each classification forming a biomarker; the first supervised machine learning model additionally taking a color image of the tooth as input, the first supervised machine learning model being additionally trained to output a biomarker based on both the color image and the intensity map, the output of the first supervised machine learning model excluding color data from the color image; the one or more processors receiving as output a plurality of biomarkers from the first supervised machine learning model, each biomarker corresponding to a different location on the tooth; and the one or more processors inputting the plurality of biomarkers into a second supervised machine learning model; the one or more processors receiving as output a diagnosis of a dental condition from the second supervised machine learning model; A method comprising:

2. 10. The method of claim 1, further comprising: the one or more processors accessing historical biomarkers for the teeth; the historical biomarkers being input by the one or more processors into the second supervised machine learning model along with the plurality of biomarkers; and the second supervised machine learning model outputting the diagnosis based on both the historical biomarkers and the plurality of biomarkers.

3. 3. The method of claim 2, wherein inputting the historical biomarker along with the plurality of biomarkers into the second supervised machine learning model comprises the one or more processors calculating an intensity difference for each biomarker of the plurality of biomarkers relative to the historical biomarker and inputting each intensity difference into the second supervised machine learning model.

4. 2. The method of claim 1, wherein receiving the diagnosis of the dental condition from the second supervised machine learning model as output comprises the one or more processors receiving a plurality of diagnoses, each diagnosis of the plurality of diagnoses corresponding to a different one of the plurality of biomarkers.

5. 1. A non-transitory computer-readable medium comprising a memory having instructions encoded thereon that, when executed, cause one or more processors to perform operations for diagnosing a dental condition, the instructions comprising: capturing image data indicative of a patient's teeth based on data obtained from a hardware device scanning the teeth, the image data obtained from the hardware device scanning the teeth comprising deep penetration optical coherence tomography (DPOCT) data, and the image data comprising an intensity map reflecting one or more optical properties of the teeth based on the DPOCT data; inputting the image data into a first supervised machine learning model, the first supervised machine learning model being trained to detect intensity variations between regions in the intensity map and to output a respective classification for each different location of the tooth based on the intensity variations, each classification forming a biomarker; the first supervised machine learning model additionally taking a color image of the tooth as input, the first supervised machine learning model being additionally trained to output a biomarker based on both the color image and the intensity map, the output of the first supervised machine learning model excluding color data from the color image; receiving a plurality of biomarkers from the first supervised machine learning model as output, each biomarker corresponding to a different location on the tooth; inputting the plurality of biomarkers into a second supervised machine learning model; receiving as output a diagnosis of the dental condition from the second supervised machine learning model; A non-transitory computer-readable medium comprising instructions for performing

6. 6. The non-transitory computer-readable medium of claim 5, wherein the instructions further comprise instructions for accessing historical biomarkers for the teeth, the historical biomarkers being input along with the plurality of biomarkers into the second supervised machine learning model, and the second supervised machine learning model outputting the diagnosis based on both the historical biomarkers and the plurality of biomarkers.

7. 7. The non-transitory computer-readable medium of claim 6, wherein the instructions for inputting the historical biomarker along with the plurality of biomarkers into the second supervised machine learning model comprise instructions for calculating an intensity difference for each biomarker of the plurality of biomarkers relative to the historical biomarker, and inputting each intensity difference into the second supervised machine learning model.

8. 6. The non-transitory computer-readable medium of claim 5, wherein the instructions for receiving the diagnosis of the dental condition from the second supervised machine learning model as output comprise instructions for receiving a plurality of diagnoses, each diagnosis of the plurality of diagnoses corresponding to a different one of the plurality of biomarkers.

9. 1. A system comprising: a memory having instructions encoded thereon for diagnosing a dental condition; one or more processors Equipped with The one or more processors, when executing the instructions, perform actions, the actions including: capturing image data indicative of a patient's teeth based on data obtained from a hardware device scanning the teeth, the image data obtained from the hardware device scanning the teeth comprising deep penetration optical coherence tomography (DPOCT) data, and the image data comprising an intensity map reflecting one or more optical properties of the teeth based on the DPOCT data; inputting the image data into a first supervised machine learning model, the first supervised machine learning model being trained to detect intensity variations between regions in the intensity map and to output a respective classification for each different location of the tooth based on the intensity variations, each classification forming a biomarker; the first supervised machine learning model additionally taking a color image of the tooth as input, the first supervised machine learning model being additionally trained to output a biomarker based on both the color image and the intensity map, the output of the first supervised machine learning model excluding color data from the color image; receiving a plurality of biomarkers from the first supervised machine learning model as output, each biomarker corresponding to a different location on the tooth; inputting the plurality of biomarkers into a second supervised machine learning model; receiving as output a diagnosis of the dental condition from the second supervised machine learning model; Including, the system.

10. 10. The system of claim 9, wherein the operations further include accessing historical biomarkers for the tooth, the historical biomarkers being input along with the plurality of biomarkers into the second supervised machine learning model, and the second supervised machine learning model outputting the diagnosis based on both the historical biomarkers and the plurality of biomarkers.

11. 11. The system of claim 10, wherein inputting a historical biomarker along with the plurality of biomarkers into the second supervised machine learning model comprises calculating an intensity difference for each biomarker of the plurality of biomarkers relative to the historical biomarker and inputting each intensity difference into the second supervised machine learning model.

Images