Destructive malocclusion diagnostic analysis using scanner and software

Abstract

A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes acquiring 3D scans of the patient's maxillary and mandibular arches, and acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically generating a treatment plan recommendation, and a recommended treatment plan projection and a non-treatment projection based, at least in part, on the acquired 3D scans of the patient's maxillary and mandibular arches, and the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically displaying a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

Description

DESTRUCTIVE MALOCCLUSION DIAGNOSTIC ANALYSIS USING SCANNER AND SOFTWARE BACKGROUND OF INVENTION

[0001] Unlike periodontal disease and dental caries, which are considered two of the three primary pillars of oral health, no method currently exists for the static, immediate detection, documentation, and classification of occlusal disease. In this regard, occlusal disease is the third pillar of oral health that adversely impacts the dental and systemic health of over two-thirds of the adult US population. A vast majority of adults who exhibit signs and symptoms of occlusal disease are never adequately screened or diagnosed in general dental offices, despite the fact that patients typically visit the dentist twice a year for examination and hygiene cleanings.

[0002] Occlusal assessments are frequently overlooked by dentists and hygienists during these visits due to time limitations and a lack of a diagnostic system for screening, documenting, and storing occlusal data into the patient’s digital chart of record. As such, there is demand for an occlusal disease diagnostic and treatment planning platform capable of facilitating enhanced clinical care and better administrative efficiencies to identify and improve oral care outcomes. BRIEF SUMMARY OF THE INVENTION

[0003] According to an aspect of the present disclosure, a method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes a step (A) of acquiring 3D scans of the patient's maxillary and mandibular arches, and a step (B) of acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes a step (C) of automatically generating (i) a treatment plan recommendation, and (ii) a recommended treatment plan projection and a non-treatment projection based, at least in part, on (a) the acquired 3D scans of the patient's maxillary and mandibular arches, and (b) the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes a step (D) automatically displaying (i) a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and (ii) a rendering of the patient's simulated dental arch N years in thefuture based on the automatically generated non-treatment projection, where N is greater than 1.

[0004] The foregoing and other features of the invention are hereinafter more fully described below, the following description setting forth in detail certain illustrative embodiments of the invention, these being indicative, however, of but a few of the various ways in which the principles of the present invention may be employed. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] With reference to the accompanying drawing figures, please note that:

[0006] FIG.1 is an exemplary operating environment of a diagnostic system.

[0007] FIG. 2 is an exemplary process flow for providing a treatment plan recommendation to a patient using the diagnostic system.

[0008] FIG.3 is a clinical dashboard displayed by the diagnostic system.

[0009] FIG.4 depicts Table 1, indicating the Smith Knight Tooth Wear Index (TWI).

[0010] FIG. 5 depicts an initial occlusal evaluation form that provides information to the diagnostic system.

[0011] FIG. 6 depicts Table 2, where TWI index values of a patient have been transposed to weights.

[0012] FIG. 7 depicts an electronic orthodontic insurance claim form filled by the diagnostic system.

[0013] FIG. 8 depicts Table 3, listing maximum tooth movement values used as a reference in the diagnostic system.

[0014] FIG.9A depicts a table showing Symptoms level scores.

[0015] FIG. 9B depicts a table where patient conditions are recorded and totaled in terms of levels in the diagnostic system.

[0016] FIG.10 depicts Table 6, which provides criteria for occlusal designation in the diagnostic system.

[0017] FIG. 11 depicts Arch Form and Function categories input to the diagnostic system.

[0018] FIG.12 depicts Table 7, which categorizes a number of signs exhibited by a patient for determining a diagnostic level associated with the patient.

[0019] FIG. 13 depicts Table 8, which determines a prognosis in the diagnostic system based on cross factoring a clinical symptom level and staging of the patient.

[0020] FIG. 14 depicts Table 9, which describes potential treatment plan recommendations based on corresponding criteria in the diagnostic system.

[0021] FIG. 15 depicts the clinical dashboard with a cursor hovering over a first element of culled images.

[0022] FIG.16 depicts the clinical dashboard with a callout corresponding to the first element of culled images.

[0023] FIG. 17 depicts the clinical dashboard with a cursor hovering over a second element of culled images.

[0024] FIG. 18 depicts the clinical dashboard with a callout corresponding to the second element of culled images.

[0025] FIG. 19 is an illustration of a computer-readable medium or computer- readable device including processor-executable instructions configured to embody one or more of the provisions set forth herein, according to one aspect. DETAILED DESCRIPTION OF THE INVENTION

[0026] The systems and methods disclosed herein are configured to detect occlusal disease in a patient and recommend treatment to the patient. In this regard, sensors provided in the discloses systems may be employed to image and scan the patient’s teeth, dental arches, and surrounding soft tissue for acquiring occlusal assessment data of the patent's teeth and dental arches. A computing device operably connected to the sensors may generate a treatment plan recommendation, and display projections of the treatment plan recommendation as compared to non-treatment. DEFINITIONS

[0001] The following includes definitions of selected terms employed herein. The definitions include various examples and / or forms of components that fall within the scope of a term and that may be used for implementation. The examples are not intended to be limiting. Furthermore, the components discussed herein, may be combined, omitted, or organized with other components or into different architectures.

[0002] "Bus," as used herein, refers to an interconnected architecture that is operably connected to other computer components inside a computer or between computers. The bus may transfer data between the computer components. The bus may be a memory bus, a memory processor, a peripheral bus, an external bus, a crossbar switch, and / or a local bus, among others. The bus may also interconnect with components inside a device using protocols such as Controller Area network (CAN), Local Interconnect network (LIN), among others.

[0003] "Component," as used herein, refers to a computer-related entity (e.g., hardware, firmware, instructions in execution, combinations thereof). Computer components may include, for example, a process running on a processor, a processor, an object, an executable, a thread of execution, and a computer. A computer component(s) may reside within a process and / or thread. A computer component may be localized on one computer and / or may be distributed between multiple computers.

[0004] "Computer communication," as used herein, refers to a communication between two or more communicating devices (e.g., computer, personal digital assistant, cellular telephone, network device) and may be, for example, a network transfer, a data transfer, a file transfer, an applet transfer, an email, a hypertext transfer protocol (HTTP) transfer, and so on. A computer communication may occur across any type of wired or wireless system and / or network having any type of configuration, for example, a local area network (LAN), a personal area network (PAN), a wireless personal area network (WPAN), a wireless network (WAN), a wide area network (WAN), a metropolitan area network (MAN), a virtual private network (VPN), a cellular network, a token ring network, a point-to-point network, an ad hoc network, a mobile ad hoc network, among others.

[0005] Computer communication may utilize any type of wired, wireless, or network communication protocol including, but not limited to, Ethernet (e.g., IEEE 802.3), WiFi (e.g., IEEE 802.11), communications access for land mobiles (CALM), WiMax, Bluetooth, Zigbee, ultra-wideband (UWAB), multiple-input and multiple-output (MIMO), telecommunications and / or cellular network communication (e.g., SMS, MMS, 3G, 4G, LTE, 5G, GSM, CDMA, WAVE, CAT-M, LoRa), satellite, dedicated short range communication (DSRC), among others.

[0006] “Communication interface” as used herein may include input and / or output devices for receiving input and / or devices for outputting data. The input and / or output may be for controlling different features, components, and systems. Specifically, the term “input device” includes, but is not limited to: keyboard, microphones, pointing and selection devices, cameras, imaging devices, video cards, displays, push buttons, rotary knobs, and the like. The term “input device” additionally includes graphical input controls that take place within a user interface which may be displayed by various types of mechanisms such as software and hardware-based controls, interfaces, touch screens, touch pads or plug and play devices. An “output device” includes, but is not limited to, display devices, and other devices for outputting information and functions.

[0007] "Computer-readable medium," as used herein, refers to a non-transitory medium that stores instructions and / or data. A computer-readable medium may take forms, including, but not limited to, non-volatile media, and volatile media. Non-volatile media may include, for example, optical disks, magnetic disks, and so on. Volatile media may include, for example, semiconductor memories, dynamic memory, and so on. Common forms of a computer-readable medium may include, but are not limited to, a floppy disk, a flexible disk, a hard disk, a magnetic tape, other magnetic medium, an ASIC, a CD, other optical medium, a RAM, a ROM, a memory chip or card, a memory stick, and other media from which a computer, a processor or other electronic device may read.

[0008] "Database," as used herein, is used to refer to a table. In other examples, "database" may be used to refer to a set of tables. In still other examples, "database" may refer to a set of data stores and methods for accessing and / or manipulating those data stores. In one embodiment, a database may be stored, for example, at a disk, data store, and / or a memory. A database may be stored locally or remotely and accessed via a network.

[0009] "Data store," as used herein may be, for example, a magnetic disk drive, a solid-state disk drive, a floppy disk drive, a tape drive, a Zip drive, a flash memory card, and / or a memory stick. Furthermore, the disk may be a CD-ROM (compact disk ROM), a CD recordable drive (CD-R drive), a CD rewritable drive (CD-RW drive), and / or adigital video ROM drive (DVD ROM). The disk may store an operating system that controls or allocates resources of a computing device.

[0010] “Display," as used herein may include, but is not limited to, LED display panels, LCD display panels, CRT display, touch screen displays, among others, that often display information. The display may receive input (e.g., touch input, keyboard input, input from various other input devices, etc.) from a user. The display may be accessible through various devices, for example, though a remote system. The display may also be physically located on a portable device or mobility device.

[0011] "Logic circuitry," as used herein, includes, but is not limited to, hardware, firmware, a non-transitory computer readable medium that stores instructions, instructions in execution on a machine, and / or to cause (e.g., execute) an action(s) from another logic circuitry, module, method and / or system. Logic circuitry may include and / or be a part of a processor controlled by an algorithm, a discrete logic (e.g., ASIC), an analog circuit, a digital circuit, a programmed logic device, a memory device containing instructions, and so on. Logic may include one or more gates, combinations of gates, or other circuit components. Where multiple logics are described, it may be possible to incorporate the multiple logics into one physical logic. Similarly, where a single logic is described, it may be possible to distribute that single logic between multiple physical logics.

[0012] “Memory," as used herein may include volatile memory and / or nonvolatile memory. Non-volatile memory may include, for example, ROM (read only memory), PROM (programmable read only memory), EPROM (erasable PROM), and EEPROM (electrically erasable PROM). Volatile memory may include, for example, RAM (random access memory), synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), and direct RAM bus RAM (DRRAM). The memory may store an operating system that controls or allocates resources of a computing device.

[0013] “Module,” as used herein, includes, but is not limited to, non-transitory computer readable medium that stores instructions, instructions in execution on a machine, hardware, firmware, software in execution on a machine, and / or combinations of each to perform a function(s) or an action(s), and / or to cause a function or actionfrom another module, method, and / or system. A module may also include logic, a software-controlled microprocessor, a discrete logic circuit, an analog circuit, a digital circuit, a programmed logic device, a memory device containing executing instructions, logic gates, a combination of gates, and / or other circuit components. Multiple modules may be combined into one module and single modules may be distributed among multiple modules.

[0014] "Operable connection," or a connection by which entities are "operably connected," is one in which signals, physical communications, and / or logical communications may be sent and / or received. An operable connection may include a wireless interface, firmware interface, a physical interface, a data interface, and / or an electrical interface.

[0015] “Portable device,” as used herein, is a computing device typically having a display screen with user input (e.g., touch, keyboard) and a processor for computing. Portable devices include, but are not limited to, handheld devices, mobile devices, smart phones, laptops, tablets, e-readers, smart speakers. In some embodiments, a "portable device" could refer to a remote device that includes a processor for computing and / or a communication interface for receiving and transmitting data remotely.

[0016] "Processor," as used herein, processes signals and performs general computing and arithmetic functions. Signals processed by the processor may include digital signals, data signals, computer instructions, processor instructions, messages, a bit, a bit stream, that may be received, transmitted and / or detected. Generally, the processor may be a variety of various processors including multiple single and multicore processors and co-processors and other multiple single and multicore processor and co-processor architectures. The processor may include logic circuitry to execute actions and / or algorithms. The processor may also include any number of modules for performing instructions, tasks, or executables.

[0017] “User” as used herein may be a biological being, such as humans (e.g., adults, children, infants, etc.).

[0018] A "wearable computing device," as used herein can include, but is not limited to, a computing device component (e.g., a processor) with circuitry that can be worn or attached to user. In other words, a wearable computing device is a computer that issubsumed into the personal space of a user. Wearable computing devices can include a display and can include various sensors for sensing and determining various parameters of a user. For example, location, motion, and physiological parameters, among others. Exemplary wearable computing devices can include, but are not limited to, watches, glasses, clothing, gloves, hats, helmets, visors, shirts, jewelry, rings, earrings necklaces, armbands, leashes, collars, shoes, earbuds, headphones and personal wellness devices.

[0019] SYSTEM OVERVIEW Referring now to the drawings, the drawings are for purposes of illustrating one or more exemplary embodiments and not for purposes of limiting the same. FIG. 1 is an exemplary component diagram of an operating environment 100 of a diagnostic system 102 including a computer 104, imaging sensors 110, and a display 112.

[0027] Each of the computer 104, the imaging sensors 110, and the display 112 may be interconnected by a bus 114. The components of the operating environment 100, as well as the components of other systems, hardware architectures, and software architectures discussed herein, may be combined, omitted, or organized into different architectures for various embodiments.

[0028] The imaging sensors 110 include a camera 120 and a scanner 122 that each generate image data of a patient 124, and transmit the image data to the computer 104. The camera 120 generates two-dimensional (2D) images of the patient’s mouth, including teeth, smile, and soft tissues. The scanner 122 is a handheld device that generates three-dimensional (3D) images of the patient’s mouth, including teeth, smile, and soft tissues. The camera 120 and the scanner 122 are each operated by a user 126, such as a clinician, for generating data indicating a condition of the patient 124.

[0029] The imaging sensors 110 transmit generated images of the patient 124 to the computer 104. The computer 104 may combine the generated images from the imaging sensors 110 to produce composite images of the patient 124.

[0030] The imaging sensors 110 may include a combination of optical, infrared, or other cameras for generating the image data. The diagnostic system 102 may further include light detection and ranging (LiDAR) systems, position sensors, proximitysensors, and a variety of other sensors and sensor combinations similar to those found in known systems, including systems provided in diagnostic dentistry for detecting occlusive disease, and therefore will not be described in detail.

[0031] The computer 104 is implemented as a part of the diagnostic system 102, and connected to a cloud computing platform 130 via a network 132. The computer 104 may be capable of providing wired or wireless computer communications utilizing various protocols to send and receive electronic signals internally to and from components of the operating environment 100. Additionally, the computer 104 may be operably connected for internal computer communication via the bus 114 (e.g., a Controller Area Network (CAN) or a Local Interconnect Network (LIN) protocol bus) to facilitate data input and output between the computer 104 and the components of the operating environment 100.

[0032] The computer 104 includes a processor 134, a memory 140, a data store 142, and a communication interface 144, which are each operably connected for computer communication via the bus 114. The communication interface 144 provides software and hardware to facilitate data input and output between the components of the computer 104 and other components, networks, and data sources described herein.

[0033] FIG. 2 depicts a computer-implemented method 200 for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease. For simplicity, the method 200 will be described as a sequence of blocks, but the elements of the method 200 may be organized into different architectures, elements, stages, and / or processes. The method 200 includes providing data inputs 202 to the computer 104 for processing by a software platform that includes a deep learning neural network.

[0034] In this regard, at block 204, the method 200 includes a step (A) of acquiring 3D scans of the patient's maxillary and mandibular arches using the scanner 122. In an embodiment, the scanner 122 is an intra-oral digital camera unit that is a dual purpose instrument for capturing a series of digital still images and video images in one functional mode and full arch stereolithography (STL) scans in another mode. As such, the scanner 122 is a handheld device used to acquire 3D scans of the patient 124, including the maxillary and mandibular arches, and is also used to acquire the 2D intra-oral images and the 2D extra-oral images of the patient 124, including an existing smile of the patient 124.

[0035] The scanner 122 houses a driver software that is connected to the software platform that provides an interface for imaging data processing at the computer 104 to take place. At block 210, the method 200 includes processing the acquired 3D scans of the maxillary and mandibular arches of the patient 124 using dental CAD software. The dental CAD software may include an artificial intelligence trained dataset model created from a database containing data from scanned maxillary and mandibular arches of different individuals. In an embodiment, the database contains data from scanned maxillary and mandibular arches of more than 500 different individuals between 15 and 70 years of age. With this construction, the artificial intelligence trained dataset model may be applied to a relatively broad variety of patients, including the patient 124, for processing maxillary and mandibular arches.

[0036] At block 212, the dental CAD software generates 3D models of the STL scans, delivered to the computer 104 as STL files. At block 214, the method 200 includes 3D image segmentation, sizing, and classification of the 3D models generated at block 212.

[0037] At block 216, the method includes 3D image categorization and annotation of the output from block 214. In an embodiment, an exocad® dental software module model creator executed by the computer 104 enables creation of 3D full maxillary and mandibular dental arch models rendered from the STL scans generated at block 204. Exocad OrthoTMis an exemplary exocad® dental software module model creator, and enables articulation of 3D models generated from bite registration scans. Annotations are assigned to varying visual perspectives provided for a clinical dashboard 220 depicted in FIG.3.

[0038] As shown in FIG. 3, the clinical dashboard 220 is an Occlusal Disease Diagnosis and Treatment Evaluation clinical dashboard that represents the display for clinical outputs of the computer 104. The clinical dashboard 220 is accessed via the software platform application on the computer 104. The clinical dashboard 220 may be a permanent part of the clinical data of record associated with the patient 124. At eachsubsequent visit where the data inputs 202 are registered, the clinical dashboard 220 is updated accordingly.

[0039] FIG. 4 depicts Table 1, indicating the Smith Knight Tooth Wear Index (TWI), which is designed to measure multifactorial tooth wear. The 3D models are ideally suited because all four surfaces are scored for wear regardless of etiology. As a diagnostic sign, tooth wear is given extra weight as it is directly correlated with Destructive Malocclusion. Each individual tooth in the mouth of the patient 124 is assessed in view of the TWI. The numerical total of all the individual tooth scores for the upper and lower anterior teeth are added together and divided by the number of anterior teeth in the mouth in order to reach an average anterior incisal edge chipping and wear score. The numerical total of all the individual tooth scores for the upper and lower posterior teeth are added together and divided by the number of posterior teeth in the mouth in order to reach an average posterior cusp wear score. These scores, ranging from 1 to 4 then become designated weights where a score of zero indicates no wear. The designated weights are applied to a diagnostic signs table.

[0040] Referring back to FIG. 2, at block 222, the method 200 includes a step (B) acquiring occlusal assessment data of the patient's 124 teeth and dental arches. More specifically, the method 200 includes filling an initial occlusal evaluation form 224 depicted in FIG. 5 with respect to the patient 124. With reference to FIG. 5, the initial occlusal evaluation form 224 is an editable form that is accessed via the software platform. The initial occlusal evaluation form 224 may be filled out by the user 126 or auxiliary team member.

[0041] Upon completion, information from the initial occlusal evaluation form 224 is automatically uploaded to the software platform at the computer 104 for processing. With this construction, the method 200 facilitates a static documentation, assessment and classification of signs, symptoms and overall occlusal status of the patient 124. The method may be employed as a system for routine documentation of occlusal status of the patient 124 for daily use. Utilizing the initial occlusal evaluation form 224, users have a platform for recording observed signs and symptoms of occlusal disease that becomes an integral part of a clinical chart of record of the patient 124.

[0042] The initial occlusal evaluation form 224 may serve as a diagnostic foundation, in conjunction with other diagnostic tools such as radiographs, 2D intra-oral camera digital images and video, and 3D scanned images in order to instantly assess an occlusal condition of the patient 124 so that therapeutic measures can be properly evaluated and discussed with the patient 124. Such therapeutic measures may include guiding the patient 124 through a step-by-step process and are designed to document the occlusal condition thoroughly and systematically in a manner which facilitates meaningful discussion of the occlusal condition with the patient 124. The initial occlusal evaluation form 224 enables the neural network to process detailed data crucial for generating predictive outcomes produced onto the clinical dashboard. In an embodiment, the neural network is developed using a V7 Labs training platform, and enables a broad range of applications including image segmentation, sizing, classification, categorization, annotation, dataset management, model management, video annotation, workflows, and document processing.

[0043] The occlusal assessment data is acquired on the initial occlusal evaluation form 224 manually by the user 126 pursuant to prompts. The prompts are provided via a form, optionally the initial occlusal evaluation form 224. The prompts and the form are displayed electronically as the occlusal assessment data is acquired from the patient 124. The prompts are for assessment of one or more of tooth inclination, maxillary arch width, maxillary arch form, functional movement, jaw parameters, occlusal classification, orthodontic crowding, clinical symptoms, and clinical signs of the patient 124.

[0044] Regarding the prompts for the initial occlusal evaluation form 224, the tooth inclination assessment includes determining whether the tooth inclination is ideal or lingual inclined. The maxillary arch width assessment includes determining whether the maxillary arch width is less than 34 mm, within a range of 34-40 mm, or greater than 40 mm. The maxillary arch form assessment includes determining whether the maxillary arch form is ideal, omega-shaped or V-shaped. The functional movement assessment includes determining whether there is anterior guidance, canine guidance or group function, bilateral posterior stops, and / or balancing side interferences.

[0045] With continued reference to FIG.5, the jaw parameters assessment includes determining whether the patient has an overbite, whether the patient has an overjet, themaximum opening of the jaw, and / or a left and right maximum sideshift. The overbite determination is made to indicate a value of less than 20%, within a range of 20-50%, or greater than 50%. The overjet determination is made to indicate a value of less than 4 mm, within a range of 4-8 mm, or greater than 8 mm. The maximum opening of the jaw is determined to indicate a value of less than 20 mm, within a range of 20-40 mm, or greater than 40 mm. The left and right maximum sideshift is determined to indicate a value within a range of 0-5 mm, within a range of 6-10 mm, or over 10 mm.

[0046] The occlusal classification assessment includes determining whether the occlusal classification is Class I, Class II or Class III. The orthodontic crowding assessment includes determining whether the orthodontic crowding of upper and lower arches, respectively, is less than 1 mm, within a range of 1-3 mm, within a range of 3-6 mm, or greater than 6 mm. The clinical symptoms assessment includes assigning levels, from 0-3, for one or more of food caught in between teeth; tongue, lip or cheek biting; speech changing or lisping; dry mouth or mouth breathing; localized tooth sensitivity; generalized tooth sensitivity; cervical dentin sensitivity; temporomandibular joint or muscle discomfort; and temporomandibular discomfort upon opening. The clinical signs assessment includes determining whether one or more of gingival inflammation; tipping; rotations; spacing; temporomandibular joint clicking, popping or crepitus; incisal chipping or wear; posterior cusp wear; exposed dentin; enamel stress cracks; tooth mobility; bone loss secondary to periodontal disease; abstractions; and fremitus.

[0047] Referring back to FIG. 2, at block 230, the method 200 includes assigning a weight value to the occlusal assessment data acquired manually pursuant to prompts, except clinical symptoms, as part of an automatic generation of the treatment plan recommendation described in further detail below. Pursuant to predefined criteria, higher weight values are assigned to the occlusal assessment data, except clinical symptoms, that contributes to occlusal disease and destructive malocclusion. FIG. 6 depicts Table 2, where TWI index values of the patient 124 have been transposed to weights.

[0048] In this regard, the weight values are summed to determine whether the patient has normal occlusion or destructive malocclusion. The determination ofdestructive malocclusion includes assignment of a stage designation automatically selected from Early Stage I, Moderate Stage II and Advanced Stage III based on the cumulative weight values. Also, the clinical signs assessment data acquired manually pursuant to prompts includes a numerical level assigned pursuant to predefined criteria. As part of the automatic generation of the treatment plan recommendation a clinical symptoms level is determined based, at least in part, upon an aggregate score of the numerical levels assigned pursuant to the predefined criteria.

[0049] Referring back to FIG.2, at block 232, the method 200 includes a step (D) of acquiring one or more 2D intra-oral images of the teeth and soft tissues of the mouth of the patient 124 using the camera 120. The method 200 also includes a step (E) of acquiring one or more 2D extra-oral images of the existing smile of the patient 124 using the camera 120.

[0050] At block 234, the method 200 includes processing the one or more 2D images acquired at block 232 using a 2D AI initial training dataset model. The 2D AI initial training dataset model is built upon dental arches scanned from a clear aligner database of over 500 patients ranging in age from 15 to 70. In this manner, the clear aligner database is employed by the 2D AI initial training dataset model to establish a baseline for identification of the following for each tooth, each arch and total mouth with respect to volumetric analysis and tooth movements. The volumetric analysis performed by the 2D AI initial training dataset model determines anterior incisal edge volumetric wear, posterior cuspal volumetric wear, and cervical erosion and abfractions. The tooth movements determined by the 2D AI initial training dataset model indicate at least one of rotation, facial crown torque, anterior buccal movement, posterior buccal movement, angulation medial, anterior mesial movement, posterior mesial movement, anterior lingual movement, posterior lingual movement, anterior extrusion, posterior extrusion, anterior intrusion, posterior intrusion, distal angulation, anterior distal movement, and posterior distal movement of teeth of the patient 124.

[0051] In an embodiment, the computer 104 automatically processes the 3D scans of the patient's 124 maxillary and mandibular arches to generate the volumetric analysis of the patient's 124 teeth. In the embodiment, the volumetric analysis includes adetermination of one or more of anterior incisal edge volumetric wear, posterior cusp volumetric wear, and cervical erosion and abfractions of the patient's 124 teeth.

[0052] At block 240, the method 200 includes 2D segmentation, sizing, and classification of teeth of the patient 124 based on the 2D images processed at block 234. In this regard, the teeth of the patient 124 indicated in the 2D images are partitioned into multiple image segments that simplify the representation of the image for relatively effective analysis by the computer 104.

[0053] At block 242, the method 200 includes 2D image categorization and annotation of the 2D images processed at block 240. In this regard, the 2D intra-oral camera images are sorted into distinct categories by the 2D AI initial training dataset model based upon the above mentioned volumetric analysis and tooth movements. The 2D AI initial training dataset model selects a series of a plurality, e.g. six, still images that are separated from an initial set of images within a given patient data set and inserted into their appropriately labeled category within the clinical dashboard 220.

[0054] At block 244, the method 200 includes processing the 2D images from block 242, the 3D scans from block 216, and the weight values from block 230 with a deep learning neural network for rendering clinical dashboard outputs. The deep learning neural network is an artificial intelligence deep neural network (AINN) that processes the 2D images, the initial occlusal evaluation form, the dataset models, the 3D scanned modeling software, the 2D imaging software and the weighted assignments using mathematical modeling to transform the data for rendering the clinical dashboard outputs. The AINN is a computer program supported on the computer 104 with other software described herein for performing the method 200.

[0055] The AINN outputs processed data indicating a condition of the patient 124 to an orthodontic claims processor. At block 250 of the method 200, the orthodontic claims processor prepares the processed data indicating a condition of the patient 124 for application to an electronic dental insurance orthodontic assessment form 252 depicted in FIG.7.

[0056] Referring back to FIG. 2, at block 254 of the method 200, the AINN automatically fills the dental insurance orthodontic assessment form 252 based on the processed data indicating a condition of the patient 124. In this regard, The AINN siftsthrough the 3D scanned images of the patient 124 to sort out and render numerical designations of teeth and areas of the dentition required to enable completion of the dental insurance orthodontic assessment form 252, which becomes a part of a permanent dental record of the patient 124. The dental insurance orthodontic assessment form 252 can be printed for the patient 124 to view as well as submitted electronically to an insurance company associated with the patient 124 for use as a predetermination or for final claims submission. In this manner, the method 200 includes automatically outputting the dental insurance orthodontic assessment form 252 as an insurance claim submission form. In an embodiment, the AINN utilizes rendered 3D pre- treatment models derived from 3D scanned images, clear aligner smile design software such as dentOne web-based software, and the initial occlusal evaluation form 224 to produce numerical values and teeth designations in each dental arch to complete the dental insurance orthodontic assessment form 252.

[0057] The AINN outputs processed information indicating the condition of the patient 124 to the clinical dashboard 220. In this regard, at block 260 the method 200 includes culling six images from the acquired 2D images with the AINN. As shown in FIG.3, the culled images 262 are selected for presenting a variety of views of the mouth of the patient 124 on the display 112, as part of the clinical dashboard 220. The culled images 262 include a fist image 264 showing an anterior view, a second image 270 showing an upper occlusal view, a third image 272 showing an lower incisal view, a fourth image 274 showing a lower occlusal view, a fifth image 280 showing a right buccal view, and a sixth image 282 showing a left buccal view of the teeth of the patient 124.

[0058] Referring back to FIG. 2, at block 284, the method 200 includes providing a 2D smile simulation before treatment. In this regard, as shown in FIG. 3, the method 200 includes the AINN selecting and displaying a seventh image 290 with the culled images 262 on the display 112. The seventh image 290 represents a full non-retracted smile of the patient 124 taken from a frontal perspective using the camera 120.

[0059] Referring back to FIG. 2, at block 292, the method 200 includes providing a 2D smile simulation after performing recommended treatment. In this regard, as shown in FIG. 3, the method 200 includes a dental imaging software supported on thecomputer 104, such as DTS Pro smile simulation software, automatically generating an after smile simulation 294 of the patient 124 from a predetermined set of criteria for tooth morphology, size and color that are matched to teeth shapes of the patient 124. The computer 104 displays the after smile simulation 294 of the patient 124 on the display 112 with the culled images 262 and the seventh image 290.

[0060] Referring back to FIG. 2, at block 300, the method 200 includes generating and displaying a 3D orthodontic before simulation of the patient 124. In this regard, as shown in FIG. 3, the method 200 includes generating 3D full maxillary and mandibular first dental arch models 302 of the patient 124. The first dental arch models 302 are rendered from the STL scans generated by the scanner 122 using the a dental CAD software supported by the computer 104. The first dental arch models 302 may additionally or alternatively be rendered by the AINN without departing from the scope of the present disclosure.

[0061] Individual teeth present in the 3D virtual full maxillary and mandibular dental arch models generated by the computer 104, including the first dental arch models 302 are automatically evaluated and assigned a numerical value for tooth wear pursuant to predefined assessment criteria. The numerical values for all upper and lower anterior teeth of the patient 124 are added together and divided by the total number of anterior teeth to derive an average anterior edge chipping and wear score. The numerical values for all upper and lower posterior teeth of the patient 124 are added together and divided by the total number of posterior teeth to derive an average posterior cusp wear score. The average anterior edge chipping and wear score and / or the average posterior cusp wear score is selected from 0, 1, 2, 3, or 4 pursuant to predefined criteria.

[0062] Referring back to FIG. 2, at block 304, the method 200 includes generating and displaying a 3D orthodontic after simulation of the patient 124 with recommended treatment. In this regard, as shown in FIG.3, the method 200 includes generating and displaying 3D full maxillary and mandibular second dental arch models 310 of the patient 124. The second dental arch models 310 are rendered by the computer 104 as a simulation after performing recommended treatment.

[0063] Referring back to FIG. 2, at block 312, the method 200 includes generating and displaying a 3D orthodontic after simulation of the patient 124 without treatment. Inthis regard, as shown in FIG.3, the method 200 includes generating and displaying 3D full maxillary and mandibular third dental arch models 314 of the patient 124. The third dental arch models 314 are rendered by the computer 104 as a simulation after time has passed without treatment.

[0064] Referring back to FIG. 2, at block 316, the method 200 also includes generating and displaying a 2D untreated smile simulation. In this regard, as shown in FIG. 3, the method 200 includes generating and displaying the 2D untreated smile simulation 320 after time has passed, indicating a smile of the patient 124 if left untreated. In this regard, a dental imaging software supported on the computer 104, such as DTS Pro smile simulation software, automatically generates the untreated smile simulation 320 of the patient 124 from a predetermined set of criteria for tooth morphology, size and color that are matched to teeth shapes of the patient 124. The computer 104 displays the untreated smile simulation 320 of the patient 124 on the display 112 with the culled images 262, the seventh image 290, the after smile simulation 294, the first dental arch models 302, the second dental arch models 310, and the third dental arch models 314.

[0065] Notably, mesial physiologic drift is a natural phenomenon that occurs in the dentition over time as a result of gradual contraction of periodontal ligament fibers that surround tooth roots, and attrition in between teeth from chewing and grinding. Physiologic drift has been shown to range from .04 to .7 mm per year in the aggregate, per dental arch. In an embodiment, the model for dental crowding may assume .35 mm of mesial migration per year and the simulated non-treatment places the arches projected at 10 years out. As teeth migrate mesially they begin to succumb to various movements that occur due to being constrained within the dental arch form and size. A clear aligner software system such as SureSmile® is utilized as a dataset model establishing parameters for each specific movement. FIG. 8 depicts Table 3, listing maximum tooth movement values used as a reference for projecting the movements expected to occur over a 10 year period in absence of treatment for malocclusion.

[0066] Referring back to FIG. 2, at block 322, the method 200 includes determining an occlusal classification of the patient 124. Dental occlusal classification of the patient 124 is determined is based on how the maxillary and mandibular first molars come intocontact with one another when closing the teeth. The exocad® dental software module model creator contains occlusal classification parameters based upon Angle’s classification system, derived from the 3D STL models generated from the scanner 122. With this construction, the method 200 includes automatically populating an occlusal classification 324 of the patient 124 on the clinical dashboard 220, as shown in FIG.3.

[0067] As such, the method 200 includes determining a classification level of occlusal disease clinical symptoms associated with the patient 124, where the classification level of occlusal disease clinical symptoms is determined automatically. In an embodiment, the method 200 further includes determining a classification of occlusal disease staging associated with the patient 124, where the classification of occlusal disease staging is determined automatically. In an embodiment, the method 200 further includes determining a classification of occlusal disease prognosis without treatment associated with the patient 124, where the classification of occlusal disease prognosis without treatment is determined automatically.

[0068] Referring back to FIG.2, at block 330, the method 200 includes determining a symptomatic level of the patient 124. In this regard, as shown in FIG. 5, patient symptoms, pain, and temporomandibular joint (TMJ) levels are recorded onto the initial occlusal evaluation form 224 manually. Once acquired, the patient symptoms, pain, and TMJ levels may be displayed on the clinical dashboard 220 by the AINN. As such, as shown in FIG. 3, the AINN populates a final clinical symptom level 332 for the patient 124 to be recorded on the clinical dashboard 220.

[0069] FIGS. 9A and 9B depict tables where a clinical symptom level is determined by a total aggregate score of the patient symptoms, pain and TMJ recorded at block 330. As shown in FIG.9B, conditions of the patient 124 are recorded with a level value indicated in the table. The level values from table shown in FIG. 9B are totaled for a Symptom level score in the table shown in FIG.9A.

[0070] Referring back to FIG. 2, at block 334, the method 200 includes transposing clinical signs 340 (FIG. 3) recorded on the initial occlusal evaluation form 224 to the clinical dashboard 220. At block 342, the method 200 includes determining an occlusal designation 344 (FIG. 3) of the patient 124 that may be one of normal occlusion, malocclusion, and destructive malocclusion. FIG. 10 depicts Table 6, which providescriteria for establishing a designation of normal occlusion as derived from Arch Form and Function categories shown in FIG.11.

[0071] With reference to FIG. 10, the occlusal designation 344 of the patient 124 is determined to be malocclusion if they exhibit at least one of overjet, lower arch crowding, and upper arch crowding as described in Table 6, yet exhibit no signs or symptoms. The occlusal designation 344 of the patient 124 is determined to be destructive malocclusion if the patient 124 exceeds any of the arch form and function parameters indicative of malocclusion, or if the patient 124 exhibits any signs or clinical symptoms at level 1 or above. The AINN derives one of the three designations for the patient 124, and displays the designation as the occlusal designation 344 on the clinical dashboard 220 as shown in FIG.3.

[0072] In this manner, the AINN automatically generates the occlusal designation 344 as a diagnosis of occlusal disease designation of the patient 124 selected from the group consisting of (a) normal; (b) malocclusion; and (c) destructive malocclusion. The AINN generates the occlusal designation 344 based on the 3D scans of the maxillary and mandibular arches of the patient 124 acquired at block 204, taken by the scanner 122. The AINN also generates the occlusal designation 344 based on the occlusal assessment data of the patient's 124 teeth and dental arches acquired at block 222.

[0073] Referring back to FIG.2, at block 350, the method 200 includes determining a diagnostic level 352 (FIG.3) of the patient 124. In this regard, FIG.12 depicts Table 7, which categorizes a number of signs exhibited by the patient 124 as one of normal occlusion, early stage I destructive malocclusion, moderate stage II destructive malocclusion, and advanced stage III destructive malocclusion. The AINN determines one of the categories for the patient 124, and displays the determined category as the diagnostic level 352 on the clinical dashboard 220 as shown in FIG.3.

[0074] Referring back to FIG.2, at block 354, the method 200 includes determining a prognosis 360 (FIG. 3) of occlusal disease associated with the patent 124. With reference to FIG. 13, the prognosis 360 of occlusal disease associated with the patient 124 is determined by cross factoring the clinical symptom level 332 against the staging as depicted in Table 8. In an embodiment, the computer 104 automatically determinesthe prognosis using the AINN, and populates the prognosis 360 onto the clinical dashboard 220 shown on FIG.3.

[0075] Referring back to FIG.2, at block 362, the method 200 includes determining a treatment plan recommendation 364 and specialist referral 370 (FIG. 3) for the patient 124. FIG. 14 depicts Table 9, which describes potential treat plan recommendations based on corresponding criteria.

[0076] The treatment plan recommendation 364 and the specialist referral 370 are artificial intelligence directed predictions that a doctor and the patient 124 may consider. The treatment plan recommendation 364 and the specialist referral 370 are enabled by the various criteria designations described in Table 9, and calculated by the AINN to derive the treatment plan recommendation 364 and the specialist referral 370 for the patient 124. In this manner, the treatment plan recommendation 364 is automatically generated using an artificial intelligence trained decision tree model supported by the AINN on the computer 104. Also, the artificial intelligence trained decision tree model includes one or more treatment plans and one or more corresponding criteria as set forth in Table 9. As shown in FIG. 3, the computer 104 displays the treatment plan recommendation 364 and the specialist referral 370 on the clinical dashboard 220.

[0077] With continued reference to FIG. 3, the method 200 includes generating the second dental arch models 310 as a recommended treatment plan projection, and generating the third dental arch models 314 as a non-treatment projection. In an embodiment, the AINN develops the second dental arch models 310 and the third dental arch models 314 based on (a) the 3D scans of the patient's 124 maxillary and mandibular arches acquired at block 204, and (b) the occlusal assessment data of the patient's 124 teeth and dental arches acquired at block 222.

[0078] The AINN automatically displays the second dental arch models 310 as a rendering of the patient's 124 simulated dental arch N years in the future based on executing the treatment plan recommendation 364. The AINN also automatically displays the third dental arch models 314 as a rendering of the patient's 124 simulated dental arch N years in the future without executing the treatment plan recommendation 364, where N is greater than 1 year. In an embodiment, N is greater than 4 and less than 11 years.

[0079] Using the AINN, the rendering of the patient's 124 simulated dental arch N years in the future based on the automatically generated non-treatment projection is generated, at least in part, using an artificial intelligence trained dataset model for predicting enamel wear of each of the patient's teeth, individually. In this regard, the AINN assigns a tooth wear score to each of the patient's teeth individually. The tooth wear score is derived from the trained dataset model, which utilizes a tooth wear index for scoring tooth wear.

[0080] In an embodiment, the AINN processes 3D scans of the patient's 124 maxillary and mandibular arches, including 3D scans of the patient's bite, and the AINN utilizes tooth direction parameters with antagonistic, standalone and synergistic maximal values that establish parameters for each specific movement. The tooth direction parameters include one or more parameters and one or more corresponding maximal values selected from the group consisting of the maximum tooth movement values indicated in Table 3.

[0081] The method 200 also includes automatically displaying the after smile simulation 294 as a rendering of the patient's simulated 2D smile N' years in the future based on executing the automatically generated treatment plan recommendation 364. The method 200 also includes automatically displaying the untreated smile simulation 320 as a rendering of the patient's 124 simulated 2D smile N' years in the future without executing the treatment plan recommendation 364. In an embodiment, N' is greater than 1 year.

[0082] The after smile simulation 294 and the untreated smile simulation 320 are simulated 2D smiles generated automatically by the AINN based on the 2D intra-oral images of the patient's 124 teeth and soft tissue of the mouth acquired at block 232. The after smile simulation 294 and the untreated smile simulation 320 are also generated based on the 2D extra-oral images of the patient's 124 existing smile acquired at block 232. The after smile simulation 294 and the untreated smile simulation 320 are also generated based on the occlusal assessment data of the patient's 124 teeth and dental arches.

[0083] The after smile simulation 294 and the untreated smile simulation 320 are generated automatically by superimposing modified images of the patient's 124individual teeth acquired at block 232 onto a virtual smile generated from a 3D model of tooth alignment and position without treatment N years in the future. The modified images include automatic adjustments to degrade tooth color for non-treatment N years into the future. In an embodiment, N' is greater than 4 and less than 11 years.

[0084] FIGS.15 – 18 depict use of the clinical dashboard 220 as a user interface on the display 112. With reference to FIGS. 15 and 17, the display 112 is provided on a monitor 372 operatively connected to the computer 104, and includes a controllable cursor 374 that may be actuated by the patient 124 or the user 126 to hover over various elements of the clinical dashboard 220, including the culled images 262. In this manner, images from the 2D images of the patient's 124 teeth and soft tissues of the mouth acquired at block 232 are displayed to the patient 124 on the monitor 372, and accessible via the cursor 374. Also, the cursor 374 is controlled to hover over an individual tooth in the acquired 2D images of the patient's 124 teeth, including the culled images 262.

[0085] Referring back to FIG. 2, at block 376, the method 200 also includes generating and displaying annotated callouts indicating the condition of the patient 124 on the clinical dashboard 220. In this regard, as shown in FIGS. 16 and 18, a callout 380 either automatically appears or is selected to appear that includes annotations identifying clinical findings for the individual tooth. The individual tooth indicated in the callout 380 corresponds to one of the culled images 262 displayed on the monitor 372, as actuated by the cursor 374.

[0086] The clinical findings provided in the callout 380 include one or more selected from the group consisting of enamel wear / chipping, gum recession, abfractions, crowding, spacing, rotations, tipping and torque. The clinical findings provided in the callout 380 are generated using one or more artificial intelligence trained models, such as the AINN, for generating predictive outcomes of the respective clinical findings.

[0087] Still another aspect involves a non-transitory computer-readable medium including processor-executable instructions configured to implement one aspect of the techniques presented herein. An aspect of a computer-readable medium or a computer- readable device devised in these ways is illustrated in FIG. 19, where an implementation 400 includes a computer-readable medium 402, such as a CD-R, DVD-R, flash drive, a platter of a hard disk drive, etc., on which is encoded computer- readable data 404. This encoded computer-readable data 404, such as binary data including a plurality of zero’s and one’s as shown in 404, in turn includes a set of processor-executable computer instructions 410 configured to operate according to one or more of the principles set forth herein. In this implementation 400, the processor- executable computer instructions 410 may be configured to perform a method 412, such as the method 200 of FIG. 2. In another aspect, the processor-executable computer instructions 410 may be configured to implement a system, such as the operating environment 100 of FIG. 1. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein.

[0088] As used in this application, the terms "component”, "module," "system", "interface", and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processing unit, an object, an executable, a thread of execution, a program, or a computer. By way of illustration, both an application running on a controller and the controller may be a component. One or more components residing within a process or thread of execution and a component may be localized on one computer or distributed between two or more computers.

[0089] Further, the claimed subject matter is implemented as a method, apparatus, or article of manufacture using standard programming or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer- readable device, carrier, or media. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.

[0090] The term “computer readable media” includes communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” includes asignal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0091] Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter of the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example aspects. Various operations of aspects are provided herein. The order in which one or more or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated based on this description. Further, not all operations may necessarily be present in each aspect provided herein.

[0092] As used in this application, "or" is intended to mean an inclusive "or" rather than an exclusive "or". Further, an inclusive “or” may include any combination thereof (e.g., A, B, or any combination thereof). In addition, "a" and "an" as used in this application are generally construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. Additionally, at least one of A and B and / or the like generally means A or B or both A and B. Further, to the extent that "includes", "having", "has", "with", or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising”.

[0093] Further, unless specified otherwise, “first”, “second”, or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first channel and a second channel generally correspond to channel A and channel B or two different or two identical channels or the same channel. Additionally, “comprising”, “comprises”, “including”, “includes”, or the like generally means comprising or including, but not limited to.

[0094] Additional advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and illustrative examples shown and described herein. Accordingly, variousmodifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents.

Claims

What is claimed is:

1. A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease, the method comprising steps: (A) acquiring 3D scans of the patient's maxillary and mandibular arches; (B) acquiring occlusal assessment data of the patient's teeth and dental arches; (C) automatically generating (i) a treatment plan recommendation, and (ii) a recommended treatment plan projection and a non-treatment projection based, at least in part, on (a) the acquired 3D scans of the patient's maxillary and mandibular arches, and (b) the acquired occlusal assessment data of the patient's teeth and dental arches; and (D) automatically displaying (i) a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and (ii) a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

2. The method according to claim 1, wherein N is greater than 4 and less than 11.

3. The method according to claims 1 or 2, wherein the acquired 3D scans of the maxillary and mandibular arches are processed using an artificial intelligence trained dataset model created from a database containing data from scanned maxillary and mandibular arches of different individuals.

4. The method according to claim 3, wherein the database contains data from scanned maxillary and mandibular arches of more than 500 different individuals.

5. The method according to claim 4, wherein the more than 500 different individuals are between 15 and 70 years of age.

6. The method according to any of claims 1 to 5, wherein the occlusal assessment data is acquired manually pursuant to prompts.

7. The method according to claim 6, wherein the prompts are provided via a form.

8. The method according to claim 7, wherein the prompts and form are displayed electronically as the occlusal assessment data is acquired.

9. The method according to any of claims 4 to 8, wherein the prompts are for assessment of one or more of the following: tooth inclination; maxillary arch width; maxillary arch form; functional movement; jaw parameters; occlusal classification; orthodontic crowding; clinical symptoms; and clinical signs.

10. The method according to claim 9, wherein the tooth inclination assessment comprises determining whether the tooth inclination is ideal or lingual inclined.

11. The method according to claim 9, wherein the maxillary arch width assessment comprises determining whether the maxillary arch width is: <34 mm; 34-40 mm; or >40 mm.

12. The method according to claim 9, wherein the maxillary arch form assessment comprises determining whether the maxillary arch form is ideal, omega- shaped or V-shaped.

13. The method according to claim 9, wherein the functional movement assessment comprises determining whether there is: anterior guidance; canine guidance or group function; bilateral posterior stops; and / or balancing side interferences.

14. The method according to claim 9, wherein the jaw parameters assessment comprises determining whether the patient has an overbite, whether the patient has an overjet, the maximum opening of the jaw, and / or left and right maximum sideshift.

15. The method according to claim 14, wherein the overbite determination is: <20%; 20-50%; or >50%.

16. The method according to claim 14, wherein the overjet determination is: <4 mm; 4-8 mm; or >8 mm.

17. The method according to claim 14, wherein the maximum opening of the jaw is determined to be: <20 mm; 20-40 mm; >40 mm.

18. The method according to claim 14, wherein the left and right maximum sideshift is determined to be: 0-5 mm; 6-10 mm; or over 10 mm.

19. The method according to claim 9, wherein the occlusal classification assessment comprises determining whether the occlusal classification is Class I, Class II or Class III.

20. The method according to claim 9, wherein the orthodontic crowding assessment comprises determining whether the orthodontic crowding of upper and lower arches, respectively, is: <1 mm; 1-3 mm; >3-6 mm; or >6 mm.

21. The method according to claim 9, wherein the clinical symptoms assessment comprises assigning levels, from 0-3, for one or more of the following: food caught in between teeth; tongue, lip or cheek biting; speech changing or lisping; dry mouth or mouth breathing; localized tooth sensitivity;generalized tooth sensitivity; cervical dentin sensitivity; temporomandibular joint or muscle discomfort; and temporomandibular discomfort upon opening.

22. The method according to claim 9, wherein the clinical signs assessment comprises determining whether one or more of the following is present in the patient: gingival inflammation; tipping; rotations; spacing; temporomandibular joint clicking, popping or crepitus; incisal chipping or wear; posterior cusp wear; exposed dentin; enamel stress cracks; tooth mobility; bone loss secondary to periodontal disease; abstractions; and fremitus.

23. The method according to claim 1, wherein the method further comprises automatically generating a diagnosis of occlusal disease designation based, at least in part, on: (a) the acquired 3D scans of the patient's maxillary and mandibular arches; and (b) the acquired occlusal assessment data of the patient's teeth and dental arches.

24. The method according to claim 23, wherein the diagnosis of occlusal disease designation is selected from the group consisting of:(a) normal; (b) malocclusion; and (c) destructive malocclusion.

25. The method according to claim 1, wherein the method further comprises determining a classification level of occlusal disease clinical symptoms.

26. The method according to claim 25, wherein the classification level of occlusal disease clinical symptoms is determined automatically.

27. The method according to claim 1, wherein the method further comprises determining a classification of occlusal disease staging.

28. The method according to claim 27, wherein the classification of occlusal disease staging is determined automatically.

29. The method according to claim 1, wherein the method further comprises determining a classification of occlusal disease prognosis without treatment.

30. The method according to claim 29, wherein the classification of occlusal disease prognosis without treatment is determined automatically.

31. The method according to claim 1, wherein the method further comprises automatically outputting an insurance claim submission form.

32. The method according to claim 1, wherein the method further comprises: (E) acquiring one or more 2D intra-oral images of the patient's teeth and soft tissues of the mouth; (F) acquiring one or more 2D extra-oral images of the patient's existing smile; and (G) automatically displaying(i) a rendering of the patient's simulated 2D smile N' years in the future based on the automatically generated recommended treatment plan projection, and (ii) a rendering of the patient's simulated 2D smile N' years in the future based on the automatically generated non-treatment projection, where N' is greater than 1, and wherein the simulated 2D smiles from steps (G)(i) and (G)(ii) are generated automatically based, at least in part, on (a) the acquired one or more 2D intra-oral images of the patient's teeth and soft tissue of the mouth; (b) the acquired on or more 2D extra-oral images of the patient's existing smile, and (c) the acquired occlusal assessment data of the patient's teeth and dental arches.

33. The method according to claim 32, wherein a handheld device is used to acquire the 3D scans of the patient's maxillary and mandibular arches.

34. The method according to claim 33, wherein the handheld device is also used to acquire the one or more 2D intra-oral images of the patient's teeth and soft tissues of the mouth and to acquire the one or more 2D extra-oral images of the patient's existing smile.

35. The method according to claim 32, wherein N' is greater than 4 and less than 11.

36. The method according to claim 1, wherein the rendering of the patient's simulated dental arch N years in the future based on the automatically generated non- treatment projection is generated, at least in part, using an artificial intelligence trained dataset model for predicting movement of each of the patient's teeth, individually.

37. The method according to claim 36, wherein one or more 3D scans of the patient's maxillary and mandibular arches includes 3D scans of the patient's bite, and wherein the trained dataset model for predicting movement of each of the patient's teeth, individually, utilizes tooth direction parameters with antagonistic, standalone and synergistic maximal values that establish parameters for each specific movement.

38. The method according to claim 37, wherein the tooth direction parameters include one or more parameters selected from the group consisting of rotation, facial crown torque, lingual crown torque, anterior buccal movement, posterior buccal movement, angulation mesial, anterior mesial movement, posterior mesial movement, anterior lingual movement, posterior lingual movement, anterior extrusion, posterior extrusion, anterior intrusion, posterior intrusion, distal angulation, anterior distal movement, and posterior distal movement.

39. The method according to claim 1, wherein the rendering of the patient's simulated dental arch N years in the future based on the automatically generated non- treatment projection is generated, at least in part, using an artificial intelligence trained dataset model for predicting enamel wear of each of the patient's teeth, individually.

40. The method according to claim 39, wherein each of the patient's teeth, individually, is assigned a tooth wear score derived from the trained dataset model, said trained data set model utilizing a tooth wear index for scoring tooth wear.

41. The method according to claim 23, wherein the diagnosis of occlusal disease designation is selected from the group consisting of: (a) malocclusion; and (b) destructive malocclusion; and wherein the treatment plan recommendation is automatically generated using an artificial intelligence trained decision tree model.

42. The method according to claim 41, wherein the artificial intelligence trained decision tree model includes one or more treatment plans, and the one or more treatment plans include no treatment, a single arch orthodontic, a dual arch orthodontic, an occlusal guard, a retainer, an orthodontist referral, a periodontist referral, and a temporomandibular joint specialist referral.

43. The method according to claim 32, wherein the simulated 2D smile from step (G)(ii) is generated automatically, at least in part, by superimposing modified images of the patient's individual teeth acquired in the one or more 2D intra-oral images of the patient's teeth and soft tissues of the mouth onto a virtual smile generated from a 3D model of tooth alignment and position without treatment N years in the future, wherein said modified images include automatic adjustments to degrade tooth color for non-treatment N years into the future.

44. The method according to claim 32, wherein one or more 3D scans of the patient's maxillary and mandibular arches are processed automatically to generate a volumetric analysis of the patient's teeth.

45. The method according to claim 44, wherein the volumetric analysis includes a determination of one or more of: anterior incisal edge volumetric wear; posterior cusp volumetric wear; and cervical erosion and abfractions.

46. The method according to claim 1, wherein one or more 3D scans of the patient's maxillary and mandibular arches are processed automatically to generate 3D virtual full maxillary and mandibular dental arch models.

47. The method according to claim 46, wherein individual teeth present in the 3D virtual full maxillary and mandibular dental arch models are automatically evaluatedand assigned a numerical value for tooth wear pursuant to predefined assessment criteria.

48. The method according to claim 47, wherein the numerical values for all upper and lower anterior teeth are added together and divided by a total number of anterior teeth to derive an average anterior edge chipping and wear score.

49. The method according to claim 48, wherein the numerical values for all upper and lower posterior teeth are added together and divided by a total number of posterior teeth to derive an average posterior cusp wear score.

50. The method according to claim 49, wherein the average anterior edge chipping and wear score and / or the average posterior cusp wear score is selected from 0, 1, 2, 3, or 4 pursuant to predefined criteria.

51. The method according to claim 6, wherein, as part of the automatic generation of the treatment plan recommendation, the occlusal assessment data acquired manually pursuant to prompts, except clinical symptoms, is assigned a weight value, and wherein, pursuant to predefined criteria, higher weight values are assigned to the occlusal assessment data, except clinical symptoms, that contributes to occlusal disease and destructive malocclusion.

52. The method according to claim 51, wherein the weight values are summed to determine whether the patient has normal occlusion or destructive malocclusion.

53. The method according to claim 52, wherein the determination of destructive malocclusion includes assignment of a stage designation automatically selected from Early Stage I, Moderate Stage II and Advanced Stage II based on cumulative weight values.

54. The method according to claim 53, wherein, the clinical signs assessment data acquired manually pursuant to prompts includes a numerical level assigned pursuant to predefined criteria, and wherein as part of the automatic generation of the treatment plan recommendation a clinical symptoms level is determined based, at least in part, upon an aggregate score of the numerical levels assigned pursuant to the predefined criteria.

55. The method according to claim 32, wherein images from the acquired one or more 2D images of the patient's teeth and soft tissues of the mouth are displayed to the patient on a monitor that includes a controllable cursor, and wherein, when the cursor is controlled to hover over an individual tooth in the acquired one or more 2D images of the patient's teeth, a callout either automatically appears or is selected to appear that includes annotations identifying clinical findings for the individual tooth.

56. The method according to claim 55, wherein the clinical findings include one or more selected from the group consisting of enamel wear / chipping, gum recession, abfractions, crowding, spacing, rotations, tipping and torque.

57. The method according to claim 56, wherein the clinical findings are generated using one or more artificial intelligence trained models for generating predictive outcomes of the respective clinical findings.

58. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out step (C) of the method of claim 1.

59. The computer program according to claim 58, wherein the program further comprises instructions, when the program is executed by a computer, causes the computer to carry out step (D) of the method of claim 1.

Images