Abstract: Bone level measurements on dental images with machine learning algorithms is described. In an example scenario, a computer processor and memory receives a dental image. The dental image is processed with a heuristic periapical bitewing algorithm to classify the dental image. The dental image is then processed with a machine learning (ML) cementoenamel junction (CEJ) algorithm, a ML first intersection of coronal alveolar bone and tooth algorithm, a ML apex of root algorithm to produce an identified CEJ, an identified first intersection of coronal alveolar bone and tooth, an identified apex of root. The algorithm is configured to calculate the shortest contiguous distance from the identified CEJ to the identified first intersection of coronal alveolar bone and tooth (numerator) divided by the shortest contiguous distance from the identified CEJ to the identified apex of root (denominator) to produce a bone level percentage measurement which is associated with the dental image.

Abstract: The field of the invention relates to a system to provide machine learning of a dental image for expediting insurance claim approvals and to assist insurance companies in identifying insurance fraud. The dental image or image (received from a source such as an x-ray, a camera, or an image capturing device) may be matched to a known computer stored dental image dataset. Anatomic variances on a dental x-ray, such as impacted wisdom teeth can be matched and identified to a known insurance datasets to produce at least one of; an insurance approval, an insurance denial, an automated insurance approval, a fraud alert, an insurance claim for human review.

Abstract: The field of the invention relates to a system to provide artificial intelligence processing of a dental image for determining a relative health risk, i.e. the probability of getting a human disease based on dental x-ray pathologies. The dental image or image (received from a source such as an x-ray, a camera, or an image capturing device) may be matched to a known computer stored genomic pathology dataset, such as a DNA dataset or a RNA dataset. Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk, a treatment recommendation, a product recommendation for an individual patient.

Abstract: Deep learning of dental images for national security is described. A computer may receive dental images of a patient from a dental provider. The computer is configured to match dental images to law enforcement databases such as a terrorist database. The process begins with matching a dental image to an anatomy probability dataset and a pathology probability dataset, then correlating it with a patient database to identify a person of interest. With different resolutions each neural network deep learns to probability map and detect different dental object probabilities to authenticate an individual. Further, the dental images will be associated with GPS coordinates to track movements of an individual. The datasets will be provided to e-commerce providers, e-commerce consumers, e-commerce administrators, machine learning entities, government entities and law enforcement entities which may exchange or transfer the data over a communication network such as the internet to locate a person of interest.

Abstract: Deep learning of dental images for national security is described. A computer may receive dental images of a patient from a dental provider. The computer is configured to match dental images to law enforcement databases such as a terrorist database. The process begins with matching a dental image to an anatomy probability dataset and a pathology probability dataset, then correlating it with a patient database to identify a person of interest. With different resolutions each neural network deep learns to probability map and detect different dental object probabilities to authenticate an individual. Further, the dental images will be associated with GPS coordinates to track movements of an individual. The datasets will be provided to e-commerce providers, e-commerce consumers, e-commerce administrators, machine learning entities, government entities and law enforcement entities which may exchange or transfer the data over a communication network such as the internet to locate a person of interest.

Abstract: Dental images processed with artificial intelligence for e-commerce is described. In an example scenario, a processing device receives a dental image. The dental image is processed with a deep neural network and matched to anatomy and pathology datasets in real time. The matched and identified dental image is matched to a dental treatment recommendation dataset to further produce a real time dental product recommendation for a patient. One or more artificial intelligence entities may provide in real time dental treatment recommendations and/or real time dental product recommendations to a dental professional and/or an individual. The process may also produce a diagnostic treatment aid and/or a product recommendation such as an orthodontic aligner.

Abstract: Machine learning of a dental image for e-commerce is described. An aggregator server may receive dental images of an e-commerce consumer from an e-commerce provider. The dental images are matched to a probability dataset and correlated with an e-commerce consumer dataset to produce an e-commerce dataset. With different resolutions each neural network machine learns to probability map and detect different dental object probabilities.