Patent Application Titled “Dento-Craniofacial Clinical Cognitive Diagnosis And Treatment System And Method” Published Online (USPTO 20190295710)

2019 OCT 11 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Kusnoto, Budi (Chicago, IL); Kaboudan, Ahmed (Cairo, EG); Bourauel, Christoph Peter (Bonn, DE); Mohamed, Sameh Mohamed Talaat Taha (Cairo, EG), filed on March 15, 2019, was made available online on September 26, 2019.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Starting the mid nighties of the last century, a huge amount of dento-craniofacial visual assets (herein referred as ‘DCVA’) have been continuously accumulating within many different repositories, ranging from universities, government agencies, insurance companies, research facilities, publishers, down to as small as polyclinics. Major World Wide Web search engines, like Google.RTM., have also given access to a large amount of DCVA through their web search engines.

“One major problem facing the owners and users of these DCVA repositories is the complete absence of a system capable of parsing the many repositories, automatically recognizing the content, automatically building labels and metadata, and then optionally saving metadata in a query-ready computer formats to be used to respond to intelligent interrogations from all types of users. Another major problem is that all web search engines, like Google.RTM., once applied to highly specialized data like DCVA fail badly by returning completely irrelevant images to the searched phrases.

“Proper DCVA search results are required for many aspects of dentistry, including providing information to insurance companies for pre-approval of expensive dental procedures. Dental Insurance treatment authorization for orthodontics cases are generally based on human inspection and evaluation of each DCVA individual case. This human dependent process is subjective and a very slow and costly procedure, prone to human error and bias.

“Anonymization of patients DCVA in order to conform to privacy standards and legislations, for the large amount of digital assets, is a very slow and costly procedure, but is generally required for research, analytics, business intelligence, machine learning and others. Decomposing composite DCVA into labeled individual visual assets, as part of data preparation for any required procedure is also a very slow and costly procedure. Further, selecting the proper individual visual assets and composing them in a new composite image (visual asset), conforming to standards, is a very slow procedure.

“The multiple artificial intelligence systems and methods in the present system and method are aimed to solve all the forgoing problems.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “This present system and method provide artificial intelligence systems and methods for automatic identification, localization, recognition, understanding, labelling, analyzing, assessing, deciding and planning related to dento-craniofacial visual assets (‘DCVA’) for creating a report deriving an outcome for patient treatment and consultation. The reports may be physical reports in one embodiment.

“In one embodiment, the Dental Classifier portion of the present system and method includes the steps of automatically: (1) typing DCVA such as, for example, as x-rays and clinical images of a patient, into a computer; (2) categorizing each discovered type of asset, relative to its nature (I.E.: intra-oral, extra-oral, etc.); (3) classifying items pertaining to each type and category (I.E.: bitewing, panoramic, etc.); (4) auto-correcting the orientation of the relevant asset according to standards; (5) recognizing anatomical modifiers (I.E.: upper, lower, right, left.) of the patient; (6) auto-generating of accurate metadata relative to each asset; and finally (7) saving metadata to different types of query-ready formats for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Search Engine Filter Booster portion of the present system and method includes the steps of: (1) integrating and boosting generic web search engines; (2) automatically filtering results returned from generic search engines, like the Google.RTM. search engine, and presenting only the proper results to the end user; and (3) installing the results of the search locally to accurately query any existing DCVA repositories for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Smart Decomposer portion of the present system and method may include the steps of: (1) recognizing composite images of a patient; (2) discovering the constituent images presented in the composite image; (3) extracting (decomposing) each individual image from the composite image; and (4) saving each extracted images in its proper folder, using the proper identification for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Smart Composer portion of the present system and method may include the steps of: (1) locating the proper image views within any computer folders; (2) creating a new composite image containing the proper views according to the required type of composite image; and (3) saving the newly created composite image in its proper folder, using the proper identification for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Smart Anonymizer portion of the present system and method may include the steps of: (1) recognize textual and facial identifiers in DCVA; (2) discarding the recognized identifiers; (3) creating a new asset, free from any textual or facial identifier; and (4) saving the newly created anonymized asset in its proper folder, using the proper identification for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Dental Insurance Treatment Auto-Authorizer portion of the present system and method may include the steps of: (1) accepting patient lateral cephalometric x-ray; (2) accepting patient panoramic x-ray; (3) accepting patient composite image including five or eight clinical views; (4) feeding each type of presented patient assets to the proper present system and method AI Engine (I.E.-(i) Landmarks localizer, (ii) Ectopic Eruption Discoverer (iii) Dental Arch Inspector); (5) analyzing the proper asset and produce the proper sections of the HLD Score Sheet; (6) consolidating all results in a single detailed report; (7) generating a summary report listing ‘Accepted’ and ‘Rejected cases; (8) providing the information via downloadable or non-downloadable versions; and (9) saving the consolidate report in its proper folder, using the proper identification for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Landmarks Localizer portion of the present system and method may include the steps of: (1) recognizing and differentiating between right Lateral Cephalometric x-rays (standard view) and left view; (2) correcting the left view, by mirroring it, to the standard right view; (3) discovering and localizing major cephalometric landmarks related to dental insurance; (4) marking (drawing) each localized landmark; (5) performing the proper quantitative analysis on the localized points; (6) generating the HLD score report relative to the findings; (7) consolidating the results with the output results of other system and method engines to produce the final ‘Acceptance/Rejection’ report; and (8) saving the generated report in its proper folder, using the proper identification, along with the marked x-rays for creating a report for patient treatment and consultation.

“In another embodiment of the present system and method, the Ectopic Eruption Discoverer portion of the present system and method may include the steps of: (1) recognizing panoramic x-rays; (2) analyzing the panoramic x-ray and localizing (i) Ectopic Eruptions (ii) Impactions (iii) Mixed Dentition; (3) marking (drawing) the area for each localized occurrence; (4) labelling each panoramic x-ray with the proper metadata; and (5) saving the generated report in its proper folder, using the proper identification, along with the marked x-rays for creating a report for patient treatment and consultation.

“In one embodiment, the present system and method does not rely on dental arch inspector analysis.

“For a more complete understanding of the above listed features and advantages of the present system and method, reference should be made to the detailed description and the drawings.”

The claims supplied by the inventors are:

“1) A method for automatically recognizing, classifying and processing dento-craniofacial visual assets by a processor-based machine using machine learning steps to provide results for treatment of a patient in a report, comprising: receiving dento-craniofacial visual asset images from a machine; inserting the visual asset images obtained by the machine into a search engine wherein the search engine recognizes, classifies and labels each of the visual asset images; wherein the processor-based machine compares the visual assets images inserted into the machine to a database of existing visual asset images already in the database of the machine; and providing a report for a treatment analysis based on the comparison between the inserted visual asset images and the existing visual asset images already in the database.

“2) The method of claim 1 wherein the method is capable of recognizing, classifying and labeling the dento-craniofacial visual assets as one of: i) X-Rays, clinical images, or miscellaneous visual assets; and further one of: ii) intra-oral x-rays, extra-oral x-rays, intra-oral clinical images, or an extra-oral clinical image; and further one of: iii) a panoramic x-ray, left cephalometric x-ray, right cephalometric x-ray, upper periapical x-ray, lower periapical x-ray, bitewing x-ray, upper arch occlusal x-ray, lower arch occlusal x-ray, right patient profile, facial profile, facial profile smiling, left occlusal clinical images, right occlusal clinical images, front occlusal clinical images, upper arch clinical image, lower arch clinical image or miscellaneous visual asset image.

“3) The method of claim 1 further comprising the step of: discriminating dental from non-dento-craniofacial visual asset image; and recognizing and rejecting all non-relevant assets during the recognizing and classification.

“4) The method of claim 1 further comprising the step of: creating metadata from the processor-based machine and saving all created metadata, including saving a comma separated value, a relational database and a NoSQL database wherein the saved metadata is used to respond to a complex query and a pattern match.

“5) A deep learning method for searching visual big-data and generating a report for analysis, comprising the step of: searching a dento-craniofacial visual asset repository using accepted dental terminology to return only a searched for visual asset; discriminating between an upper arch and a lower arch and teeth assets; and further discriminating between a right side visual asset and a left side visual asset.

“6) The deep learning method for searching visual big-data and generating a report for analysis of claim 5, further comprising the step of: enhancing or boosting a generic web search engine search: and transparently submitting a searched phrase or term inserted into the generic web search engine and then filtering any returned results to eliminate all assets non-relevant to the search phrase or term.

“7) The deep learning method for searching visual big-data and generating a report for analysis of claim 5, further comprising the step of: anonymizing an dento-craniofacial visual asset by removing all textual demographics and facial identifiers incidences.

“8) The deep learning method for searching visual big-data and generating a report for analysis of claim 5, further comprising the step of: generating an auto-authorization report for a dental insurance company by auto-generating a detailed quantitative and qualitative Handicapping Labio-Lingual Deviation Index score index report based on auto-analysis of lateral cephalometric x-rays, panoramic x-rays and clinical images.

“9) The deep learning method for searching visual big-data and generating a report for analysis of claim 5, further comprising the step of: detecting and localizing a cephalometric landmark and generating a required cephalometric measures in mm.

“10) The deep learning method for searching visual big-data and generating a report for analysis of claim 5, further comprising the step of: recognizing and discovering an ectopic eruption of a tooth, an impacted tooth and a mixed dentition teeth in a panoramic x-ray.”

For more information, see this patent application: Kusnoto, Budi; Kaboudan, Ahmed; Bourauel, Christoph Peter; Mohamed, Sameh Mohamed Talaat Taha. Dento-Craniofacial Clinical Cognitive Diagnosis And Treatment System And Method. Filed March 15, 2019 and posted September 26, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190295710%22.PGNR.&OS=DN/20190295710&RS=DN/20190295710

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