Dental images correlated to the human genome with artificial intelligence

A method of identifying a relative genomic health risk from at least one of:

• a dental image, a dental image landmark, an image, and an image landmark when matched and identified to at least one of:
• a deoxyribonucleic acid (DNA) sequence, a ribonucleic acid (RNA), and a genomic sequence, the method implemented by a processor, wherein the method implemented by the processor is configured to:
• receive at least one of:
• the dental image, the dental image landmark, the image, and the image landmark;
• wherein the method may be configured to execute an instruction in any order;
• calculate a quantitative score by weighting the level of at least one of:
• a deoxyribonucleic acid (DNA) sequence, an individual deoxyribonucleic acid (DNA) sequence, a ribonucleic acid (RNA) sequence, an individual ribonucleic acid (RNA) sequence, a genetic sequence, and a dataset or an expression product thereof and at least one of:
• the dental image, the dental image landmark, the image, and the image landmark to determine the relative genomic health risk and provide to a dental image dataset;
• determine an image variant in at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and match it with at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, and the genomic sequence to determine the relative genomic health risk and provide to the dental image dataset;
• determine if no image variant is detected in at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and match it with at least one of:
• the deoxyribonucleic acid (DNA), sequence, the ribonucleic acid (RNA) sequence, and the genomic sequence to determine a relative genomic health risk and provide to the dental image dataset;
• match and identify the dental image dataset to the individual information dataset to generate the relative health risk and provide to the dataset;
• process a transaction of at least one of:
• an exchange, a transfer, a purchase, a sell of at least one of:
• the dental image dataset, the individual information dataset, the relative genomic health risk, and the correlation dataset over a communication network;
• wherein the communication network includes at least one of:
• the internet, an intranet, an extranet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, and a transaction platform.

A method implemented by a microprocessor for identifying a genetic probability from matching and identifying at least one of:

• a dental image, a dental image landmark, an image, and an image landmark the method comprising:
• execute an instruction in any order;
• determine a quantitative score by weighting a level of at least one of:
• a deoxyribonucleic acid (DNA) sequence, an individual deoxyribonucleic acid (DNA) sequence, a ribonucleic acid (RNA) sequence, an individual ribonucleic acid (RNA) sequence, a RNA transcript sequence of a gene, a and the genetic sequence from at least one of:
• the genetic sequence, the genetic sample, a tissue sample, a saliva sample, a blood sample, a sample and at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and
• provide to a dental image dataset;
• determine a quantitative score by weighting a normalized level of the deoxyribonucleic acid (DNA) sequence, the individual deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the individual ribonucleic acid (RNA) sequence, the RNA transcript sequence of the gene, and a genetic sequence of a gene to at least one reference gene and at least one of:
• the dental image, the dental image landmark, the image, and the image landmark to produce a normalized gene expression level and provide to the dental image dataset;
• comparing the normalized gene expression level of a gene to a range of normalized gene expression levels of the reference gene obtained from at least one of:
• a dental anatomy reference dataset, a dental pathology reference dataset, and a reference dataset set and at least one of:
• associate, correlate it to at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and provide to the dental image dataset;
• predict the relative health risk of at least one of:
• a pathology, a dental pathology, and no pathology based on the comparison of the normalized level of at least one of:
• the deoxyribonucleic acid (DNA) sequence, the individual deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the individual ribonucleic acid (RNA) sequence, the RNA transcript sequence of the gene, and the genetic sequence expression level of the gene to the normalized reference gene dataset and at least one of:
• associate, correlate it to at least one of:
• the dental image, the dental image landmark, the image, the image landmark and provide to the dental image dataset;
• match and identify the dental image dataset to an individual information dataset to produce at least one of:
• a genetic probability, the relative health risk and provide to a correlation dataset;
• process a transaction of at least one of:
• an exchange, a transfer, a purchase, a sell of at least one of:
• the genetic probability, the relative health risk, and the correlation dataset over a network.

A method of predicting at least one of:

• a product recommendation, a treatment recommendation, a likelihood of a clinical genomic outcome of an individual from at least one of:
• a dental image, a dental image landmark, an image, and an image landmark based on matching and identifying to at least one of:
• a deoxyribonudeic acid (DNA) sequence, an ribonucleic acid (RNA) sequence, a ribonucleic acid (RNA) transcripts sequence, a genetic sequence, and an expression product in a biological sample;
• the method comprising:
• a computer vision component configured to analyze at least one of:
• the dental image, the dental image landmark, the image, and the image landmark;
• implementing the method by a memory configured to store instructions associated with an aggregator and implementing by a processor coupled to the computer vision component and the memory, the method implemented by the processor executing instructions associated with the aggregator, wherein
• the method includes:
• an image processing engine configured to receive at least one of:
• the dental image, the dental image landmark, the image, and the image landmark
• the method may execute an instruction in any order;
• match and identify a level at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the ribonucleic acid (RNA) transcripts sequence, the genetic sequence, and the expression product in the biological sample to at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and provide to a bioinformatics dataset;
• match and identify at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the ribonucleic acid (RNA) transcripts sequence, the genetic sequence, and the expression product in the biological sample to one or more gene groups selected from at least one of:
• a cellular organization gene group, a basal epithelia gene group, a stress response gene group, an androgen gene group, a stromal response gene group, a proliferation gene group, a dataset to at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and provide to a bioinformatics dataset;
• calculating quantitative score for the individual by weighting level of at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the ribonucleic acid (RNA) transcripts sequence, the genetic sequence, and expression product in a biological sample obtained from the individual to at least one of:
• the dental image, the dental image landmark, the image, and the image landmark and provide to the bioinformatics dataset;
• correlate at least one of:
• the dental image, the dental image landmark, the image, and the image landmark of the individual with at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the ribonucleic acid (RNA) transcripts sequence, the genetic sequence, and the expression product in the biological sample obtained from the individual and provide to the bioinformatics dataset;
• predicting likelihood of clinical genomic outcome of the individual based on a quantitative score from at least one of:
• the dental image, the dental image landmark, the image, and the image landmark of the individual with at least one of:
• the deoxyribonucleic acid (DNA) sequence, the ribonucleic acid (RNA) sequence, the ribonucleic acid (RNA) transcripts sequence, the genetic sequence, and the expression product in the biological sample obtained from the individual and provide to the bioinformatics dataset;
• process a transaction of at least one of:
• an exchange, a transfer, a buy, a sell with at least one of:
• the dental image, the dental image landmark, the image, the image landmark, the dental treatment recommendation, the dental product recommendation, the likelihood of a clinical genomic outcome of the individual, and the bioinformatics dataset, over a communication network, wherein the communication network includes at least one of:
• the internet, an intranet, an extranet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, a transaction platform and provide to at least one of:
• a dental professional, a health care professional, an expert, the individual, an e-commerce organization, a researcher, a manufacturer, a business, an application software, a system software, a client device an artificial intelligence system, and processing device.