Method, system and computer readable storage medium for determining occlusal parameters

Abstract

A method, system and computer-readable storage medium for determining articulator parameters. A dentist can quickly and inexpensively visualize a 3D model of a patient's face and jaws using a mobile device such as a smartphone, where depth information is detected using a depth sensor. Articulator parameters can then be obtained for treatment planning based on an analysis of the correct function of the patient's teeth.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims the benefit of and priority to U.S. Application No. 16 / 431,830, filed on June 5, 2019, which is incorporated herein by reference for all purposes. Technical Field

[0003] The present application generally relates to a method, system and computer-readable storage medium for determining bite parameters, and more specifically, to a method, system and computer-readable storage medium for determining bite parameters of a patient using a 3D face detection method. Background Art

[0004] The dentist may use an articulator having upper and lower components to which maxillary and mandibular casts may be attached to reproduce the static relationship of the patient's maxilla to the patient's mandible. This may be used to study individual teeth and complete dental arches for diagnosis and / or treatment planning, as well as to allow adjustment of fixed and removable prostheses and dental restorations.

[0005] The facebow may be used to set up an articulator in which the movement of the patient's temporomandibular joint may be determined. In this context, the facebow may be positioned in the ear to transfer any hinge axis position by reference using an approximation of the patient's ear. The facebow may also be aligned with the patient's face by a mouthpiece. A fixed frame with an adjustable pivot joint may be used to support the facebow; and a positively driven positioning mechanism is used to adjust the pivot joint to provide a desired facebow position and appearance.

[0006] The face bow allows the relationship between the maxillary arch and the temporomandibular joint to be transferred to the cast. It records the relationship of the maxilla to the external auditory canal in the hinge axis and facilitates the mounting of the maxillary cast on the articulator.

[0007] The patient can also perform various chewing movements (lateral, forward / backward movement of the jaw, etc.), allowing the dentist to read certain values ​​on the facebow for use in, for example, a commercial articulator.

[0008] This requires a lot of time and is an unpleasant process for the patient. It would therefore be useful to quickly and automatically determine the occlusal parameters without using a physical facebow or a physical articulator.

[0009] U.S. Patent No. 9336336B2 discloses the use of facial detection for a "smile" design application. In this article, a dental restoration can be designed for a patient by providing one or more 2D images, wherein at least one 2D image includes at least one facial feature; a 3D virtual model of a portion of the patient's mouth is provided and one of the 2D images is arranged relative to the 3D virtual model. Both the 3D virtual model and the 2D image are visualized in a 3D space; and the restoration is modeled on the 3D virtual model so that it fits the facial feature of at least one 2D image.

[0010] US Patent No. US9642686B1 discloses a method and system for recording characteristics of a patient's bite arch using a portable computing device. In this context, a virtual face bow can be used to record characteristics of a patient's bite arch using a facial alignment image with a set of crosshairs. The recorded characteristics can then be used to replicate the alignment of the patient's bite arch with a maxillary cast in a laboratory holder, which can then be moved to an articulator to produce a custom denture for the patient.

[0011] PCT Application No. WO201367606 discloses the use of digital elements to articulate a model into a physical articulator, wherein a physical study model is arranged in the articulator and the orientation of the canine line and the occlusal plane is determined on the study model.

[0012] U.S. Patent Application No. 8706672B2 discloses a computer-assisted method for creating customized teeth using facial analysis, comprising obtaining data about an area to be treated and data about a patient's face, analyzing the data to determine attributes of at least the patient's face, and creating a modified tooth setting using a set of stored rules that utilize the determined facial attributes.

[0013] These methods either do not exploit depth information or use X-ray images which may not be ideal. Summary of the invention

[0014]

[0013] Existing limitations associated with the foregoing, as well as other limitations, may be overcome by methods, systems, and computer-readable storage media for determining occlusal parameters.

[0015] In one aspect of the present invention, a method for determining occlusal parameters may be provided, the method comprising: receiving an image of a patient's face; determining fixed and / or moving points in the patient's face based on the received image; and calculating articulator parameters based on the determined fixed and / or moving points.

[0016] In another aspect of the present invention, the method may further include one or more of the following steps: (i) wherein the articulator parameters are selected from the group consisting of: (1) sides of the Bonwill triangle, (2) intercondylar distance, (3) Balkwill angle, (4) sagittal condylar path inclination, (5) Bennett angle, (6) initial Bennett movement, and (7) Spee curve, (ii) further including reconstructing a 3D model of the patient's face based on the received image, (iii) further including superimposing a scan of the patient's oral cavity on the 3D model, (iv) wherein the determining step is achieved by feature analysis, (v) wherein the feature analysis includes determining fixed and / or moving points using deep learning, and (vi) wherein the fixed point includes the position of the temporomandibular joint. (vii) wherein the calculating step is performed using geometric distances and / or angles measured using fixed and / or moving points, (viii) further comprising populating a virtual articulator with occlusal values ​​using the calculated articulator parameters, (ix) further comprising analyzing the functionality of the teeth based on the articulator parameters, (x) further comprising generating a restoration or treatment plan based on the articulator parameters.

[0017] In yet another aspect, the present invention may provide a computer-implemented method for determining occlusal parameters, the computer-implemented method comprising: training a deep neural network using one or more computing devices and a plurality of training images to map one or more fixed and / or moving points in at least a portion of each training image to one or more highest position probability values ​​of a position probability vector; receiving, by the one or more computing devices, an image of a patient's face including at least one jaw; using the trained neural network, identifying the fixed and / or moving points based on one or more output position probability values ​​of the deep neural network; and calculating articulator parameters based on the determined fixed and / or moving points.

[0018] In another aspect of the present invention, the computer-implemented method may also provide a method wherein the deep neural network is a convolutional neural network.

[0019] In yet another aspect of the present invention, a system for determining occlusal parameters may be provided, the system comprising at least one processor configured to perform the following steps: receiving an image of a patient's face; determining fixed and / or moving points in the patient's face based on the received image, and calculating articulator parameters based on the determined fixed and / or moving points.

[0020] The system may also provide one or more combinations of the following: (i) wherein the processor is further configured to select articulator parameters from the group consisting of: (1) sides of a Bonwill triangle, (2) intercondylar distance, (3) Balkwill angle, (4) sagittal condylar path inclination, (5) Bennett angle, (6) initial Bennett movement, and (7) Spee curve, (ii) wherein the processor is further configured to reconstruct a 3D model of the patient's face based on the received image, (iii) wherein the processor is further configured to superimpose a scan of the patient's oral cavity on the 3D model, (iv) wherein the processor is further configured to determine fixed and / or moving points by feature analysis, (v) wherein the feature analysis includes using deep learning to determine fixed and / or moving points, (vi) wherein the fixed point includes the position of the temporomandibular joint, (vii) wherein the processor is further configured to generate a restoration or treatment plan based on the articulator parameters.

[0021] In yet another aspect of the present invention, a non-transitory computer-readable storage medium for storing a program may be provided, which, when executed by a computer system, causes the computer system to perform a process comprising: receiving an image of a patient's face; determining fixed and / or moving points of the patient's face based on the received image; and calculating articulator parameters based on the determined fixed and / or moving points. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Example embodiments will be more fully understood from the detailed description and accompanying drawings given below, in which like elements are represented by like reference numerals, which are given by way of illustration only and thus do not limit the example embodiments herein, wherein:

[0023] Figure 1 is a perspective view of the patient's face, illustrating the relationship of the patient's face to the three spatial planes.

[0024] Figure 2 is a perspective view of a jaw illustrating a Bonwill triangle according to an exemplary embodiment of the present invention.

[0025] Figure 3a is a perspective view illustrating a jaw of Bennett's angle according to an exemplary embodiment of the present invention.

[0026] Figure 3b is a sketch illustrating the movement of a patient's mouth and lips according to an exemplary embodiment of the present invention.

[0027] Figure 4 is a side view of a jaw showing a Spee curve according to an exemplary embodiment of the present invention.

[0028] Figure 5 is a top view of a jaw showing the intercondylar distance according to an exemplary embodiment of the present invention.

[0029] Figure 6 is a side view of a jaw showing sagittal condylar path inclination in accordance with an embodiment of the present invention.

[0030] Figure 7 is a perspective view of a jaw illustrating a Balkwill angle according to an exemplary embodiment of the present invention.

[0031] Figure 8 is a flow chart describing a method according to an exemplary embodiment of the present invention.

[0032] Fig. 9 is a side view of a system illustrating fixed points and moving points according to an exemplary embodiment of the present invention.

[0033] Fig.10a is a side view of a jaw illustrating a first movement of a patient's oral cavity according to an exemplary embodiment of the present invention.

[0034] Fig.10b is a side view of a jaw illustrating a second movement of a patient's oral cavity according to an exemplary embodiment of the present invention.

[0035] Fig.11 is a block diagram illustrating a computer system according to an exemplary embodiment of the present invention.

[0036] Different drawings may have at least some reference numerals that may be the same to identify the same components, although a detailed description of every such component may not be provided below for every drawing. DETAILED DESCRIPTION

[0037] According to example aspects described herein, a method, system, and computer-readable storage medium for determining occlusal parameters may be provided.

[0038] Methods for determining occlusal parameters

[0039] Determination of the occlusal parameters for a particular patient, such as the intercondylar distance and the Bennett angle, is essential for the production of restorations. The various occlusal parameters described in more detail below provide important information about the natural and correct course of jaw movement during the chewing process. The information obtained allows the indication of the correct occlusion of the jaws and the position of the cusps and grooves in the teeth. This saves the dentist and the patient time and inconvenience caused by the fitting of the restoration at a later stage.

[0040] One object of the present invention is to relate parts of a patient's oral cavity, such as the maxillary arch, the mandibular arch and / or the temporomandibular joint, to the face in a spatial plane by using images captured by a depth sensor. This can be used for dentofacial analysis of the maxillary arch and the establishment of a functional relationship with the mandibular arch. The process of determining occlusal parameters can be digitized by using images captured by a depth sensor, thereby eliminating the need for complex structures such as a physical face bow.

[0041] Figure 1 The imaginary planes of the 3D reconstruction of the patient's face 10, preferably including the jaw, are illustrated, which are the sagittal plane S, the frontal plane F and the horizontal plane H. The sagittal plane S is an anatomical plane that can divide the patient's head into two parts. Therefore, the sagittal plane that divides the head into two halves is the mid-sagittal plane. The frontal plane F can be at right angles to the sagittal plane S and can divide the head into an anterior region and a posterior region. The horizontal plane H can also be at right angles to the sagittal plane and can divide the head into an upper region and a lower region.

[0042] The virtual articulator may enable the dentist to configure restorations and / or check the functionality of teeth based on the determined positions of virtual contact points between, for example, the upper and lower jaws of the patient's mouth. It may also enable the orthodontist to detect, inter alia, collisions between teeth as they are moved / displaced, thus allowing the detection of differences between a normal bite situation before any movement and a bite situation after the movement. For configuring the virtual articulator, patient-specific inputs (patient-specific bite parameters) may be obtained from an image of the patient's head, said image containing geometric information that may be used to determine the relative distances of the various parts of the patient's dentition to each other and to the temporomandibular joint.

[0043] The occlusal parameters may include, but are not limited to, (i) sides 11a (arms) of the Bonwill triangle 11, (ii) intercondylar distance 15 (base of the Bonwill triangle), (iii) Balkwill angle 17, (iv) sagittal condylar path inclination β, (v) Bennett angle α, (vi) initial Bennett shift (immediate left / right shift), and (vii) Spee curve 18. These parameters may be determined by tracking fixed points 20 and / or moving points 21 on the patient's face using a camera system 22 with a depth sensor 24 and determining geometric distances and angles between these points and designated portions of the patient's dentition, as described below.

[0044] Figure 2The Bonwill triangle 11 is illustrated, which is an equilateral triangle 13 connecting the contact point of the incisal edge of the mandibular central incisor 14 (or the midline of the mandibular residual ridge) to the midpoint of each condyle 13 and from one condyle 13 to the other condyle 13. Since it is an equilateral triangle, the lengths of the sides 11a can be approximately equal (typically 4 inches). Knowing the location or approximate location of the left and right temporomandibular joints 23 based on the anatomical feature extraction method discussed in this article, the sides of the Bonwill triangle 11 can be calculated.

[0045] Figure 3a The Bennett angle α is shown, which is the angle formed between the sagittal plane S and the path of the advanced condyle 13 when viewed in the horizontal plane H as the mandible moves laterally. Figure 3a and Figure 3b As the mandible moves from its initial position (M 0 )Left(M 1 ) moves, forming an angle α between plane S and the path of the advancing condyle 13a, and point p on condyle 13a moves from position P 1 Move to position P 2 The initial Bennett shift (also called immediate lateral shift) is the movement to the side to which both condyles may shift as the mandible begins to move laterally; that is, the entire mandible may move laterally parallel to the hinge axis before the nonworking condyle moves anteriorly, inferiorly, and medially. This lateral shift may be difficult to follow as an isolated movement, and it may be interpreted as evidence of occlusal damage (e.g., occlusal capsule or ligament strain).

[0046] Figure 4 The curve of Spee is shown, which is the curvature of the mandibular occlusal plane starting from the premolars, then the buccal cusps of the posterior teeth, continuing to the terminal molars.

[0047] Figure 5 The intercondylar distance 15 is shown, which is the distance between the patient's two temporomandibular joints 23 (ie, the distance between the condyles 13). Figure 6 is a diagram showing the sagittal condylar path inclination β, i.e., the condylar path (path PC followed by the condyle 13 in the temporomandibular joint 23) and the horizontal reference plane H 1 This may occur as the patient's mandible is protruded (moved forward).

[0048] Figure 7 The Balkwill angle 17 is shown, which is the angle between the Bonwill triangle 11 and the Balkwill triangle / occlusal triangle 16 defined by the horizontal planes of the mandibular dentition. Knowing these and other similar parameters of the virtual articulator without the use of a physical face bow or physical articulator can help reduce treatment time in the dentist's office.

[0049] Having described exemplary articulator parameters, reference will now be made to Figure 8-1 0 illustrates a process S1000 that can be adopted according to at least some example embodiments of the present invention. Process S1000 can start at step S100, where multiple images of a patient performing a predetermined masticatory / chewing movement can be captured. The images can be captured using a camera system 22 with a depth sensor 24. For example, a patient's face can be captured using a smart phone with a depth sensor (such as Lenovo Phab 2Pro using Google Tango technology) or an independent combination of color and depth cameras. The depth information can be used to reconstruct a 3D model of the face 10 including the jaw, as shown in step S200. In addition, a series of 2D images can be captured while the patient performs a predetermined chewing movement, and a corresponding 3D model of the face 10 can be reconstructed for each movement. The reconstruction can be done in real time or can be done when necessary and used for measurement. Alternatively, knowing how the 2D images are related to each other in terms of position and their scale, distance and angle, these images can be calculated without 3D reconstruction. In addition, the movement can be recorded and / or tracked by identifying relevant points and comparing them frame by frame. The images / records can be saved in a database. In an exemplary embodiment, images may be saved in a database and tagged for a specific bite position. Movements may include pushing the lower jaw (mandibular arch) in front of the upper jaw (maxillary arch) and moving the lower jaw side to side while the upper jaw is in contact with the lower jaw. Movements may be determined to correspond to the natural movement of the patient chewing and to allow for the collection of geometric distances and angles required for the bite.

[0050] In step S300, fixed points 20 and / or moving points 21 may be determined by feature analysis. This may include using natural markers of teeth and facial anatomy, preferably without using predetermined artificial markers. However, in some embodiments, predetermined artificial markers may be placed at predetermined positions on the face and / or oral cavity, and fixed points 20 and / or moving points may be determined by identifying predetermined artificial markers. These fixed points 20 and / or moving points 21 may be determined to help calculate various distances and angles. Fixed points 20 may help determine the orientation of the face during movement. Fixed points 20 and / or moving points 21 may preferably be points on the face or oral cavity that contain little or no soft tissue, such as, for example, points on the forehead 20a, points below the nose 20b, and points on either side of the head 20c where the temporomandibular joint 23 is anchored and where movement originates. However, some moving points 21 may contain soft tissue (e.g., lips).

[0051] Other natural markings may include bumps, fissures, the position of teeth and their relationship to each other, and artificial markings may include stickers, dots of color drawn with a pen, glued-on geometric shapes, etc.

[0052] The movement points 21 may indicate points at which displacement and / or maximum distance corresponding to a particular chewing movement may be measured. For example, as the patient moves the jaw to a desired position (depending on the occlusal parameter being measured), an image may be recorded with the jaw in a first position (e.g., closed jaw) and another image may be obtained with the jaw in a second position (e.g., moved jaw). 4 When moving the jaw, Fig. 9 As shown in , the moving point 21 moves from position 1 to position 2. Therefore, the distance between positions 1 and 2 can be obtained from the image. In addition, when the lower jaw moves away from the upper jaw in movement M3, as shown in Fig.10a and 10b As shown in , point P can then be identified and measured 3 and P 4 The displacement between 1 . Click P 3 and P 4 The points may be any point of interest and may not be limited to molars. For example, they may define the contact points of the incisal edges of the mandibular central incisors or the midline of the mandibular residual ridge. The points may also be located in similar locations in humans and thus may be determined by feature analysis / extraction.

[0053] In an embodiment where predetermined artificial markers are not used, the fixed points 20 and / or the moving points 21 can be determined by feature analysis of the anatomical structure. Feature analysis can include defining dental features that can be identified and used to detect larger structures. Features can include smaller points, edges, objects on an image, and / or curves or boundaries that define different areas of an image. In an exemplary embodiment of the present invention, feature analysis can include a machine / deep learning method, in which a computer can be trained to recognize fixed points 20 and moving points 21 based on previously classified facial images. For example, starting with multiple images of a face, a dentist can mark the pixels of the image to be used for training by marking areas of interest, such as fixed points 20 and / or moving points 21. The marking of the training image can be done digitally, for example, by setting points on the image corresponding to the points of interest. Using this set of labeled or classified images, a network architecture / deep neural network, such as a convolutional neural network (CNN), can be built and fed with labeled pictures, allowing the network to "learn" from it so that the network can generate network wiring that can classify new images on its own. After training, the network may be provided with previously unseen images and outputs, such as a position probability vector containing position probability values, where the highest position probability value may define the position of the fixed point 20 and / or the moving point 21 may be obtained, and corresponding feedback may be given so that the network may eventually classify the images, preferably operating on its own without human assistance. The images used for training may also be derived from or otherwise based on ongoing patient treatments, past patient treatments, outcomes of patient treatments, or other treatment information.

[0054] After obtaining the fixed points 20 and / or the moving points 21, the geometric distances and angles measured using the obtained fixed points 20 and / or the moving points 21 can be used to obtain the values ​​of the articulator parameters, as shown in step S400. Some occlusal parameters can be measured using only the fixed points 20 or a single image, while other occlusal parameters can be obtained using the fixed points 20 and the moving points 21 or multiple images. For example, in determining the moving points 21, the values ​​of the articulator parameters can be obtained using the fixed points 20 and / or the moving points 21. 3 Displacement during the period d 1 There may be multiple images depicting multiple positions of the mandible when the maximum displacement is d. The image with the maximum difference about the point of interest can be obtained and the Euclidean distance between these points can be calculated. Using the extracted or identified points representing the position of the temporomandibular joint 23 and the displacement d 1 and / or other fixed points 20 and moving points 21, Euclidean geometry using lines, angles and triangles or coordinates in space obtained from, for example, 3D reconstruction or images can be used to measure the values ​​of the occlusal parameters. Thus, the computer system 100 can be programmed to repeatedly perform these steps with the aid of an algorithm that combines deep learning feature extraction or simpler object recognition methods (such as gradient matching or template matching, etc.) with methods involving measuring relevant distances and angles.

[0055] Other systems and methods for dental measurements may include the systems and methods taught in U.S. Patent No US8126726B2 to Matov et al., entitled “System and method for facilitating automated dental measurements and diagnostics,” which is incorporated herein by reference in its entirety as if fully set forth herein.

[0056] After obtaining the values ​​of the occlusal parameters, the virtual articulator may be populated with the values, as shown in step S500. The populated virtual articulator may then be used to complete a treatment process, step S600, such as creating a restoration or analyzing the correct function of the patient's teeth 12. Furthermore, the obtained values ​​may be transmitted, for example wirelessly, to CAD / CAM software, where they may be integrated and used to calculate a functionally correct proposal for a restoration or other dental or orthodontic appliance.

[0057] Furthermore, the 3D reconstruction of the face 10 can be overlaid with an intraoral data record, such as a 3D measurement / scan of the patient's mouth taken using an intraoral scanner, in order to, for example, visualize a restoration or treatment plan and / or the results of a treatment plan. The overlay can be done by identifying common areas in the two data, such as teeth, and overlaying the data together along the common areas.

[0058] Preferably, the process S1000 can be completed in a short period of time (e.g., a few seconds to 2 minutes) compared to about 45 minutes when using a physical face bow and / or a physical virtual articulator. In addition, potentially higher accuracy can be achieved, and the process can also preferably significantly reduce the cost of bite and user errors, thereby leading to a higher application rate. In addition, the process can also make the patient feel comfortable, thereby increasing customer satisfaction with the dentist.

[0059] System for determining occlusal parameters

[0060] In the description Fig. 9 After System 1, we will now refer to Fig.11 , Fig.11 A block diagram of a computer system 100 that can be employed according to at least some example embodiments herein is shown. Although various embodiments may be described herein in terms of this exemplary computer system 100, after reading this description, it may become apparent to one or more persons skilled in the relevant art how to implement the present disclosure using other computer systems and / or architectures.

[0061] In one example embodiment, the computer system may include a camera system 22 that can operate under one of several depth sensing principles, including, for example, (i) structured light, (ii) time of flight (ToF), and / or (iii) stereo principles. For cameras that employ structured light, a known pattern can be projected onto a patient or the patient's head using a light source, and a receiver can detect deformations of the reflected pattern to calculate a depth map based on geometry. For cameras that employ the time of flight (ToF) principle, a light source can emit pulses, and a sensor can detect reflections of the pulses from the patient to record their flight time. Knowing this and the constant speed of light, the system can calculate how far away a point on the patient's head is. Alternatively, a modulated light source can be sent, and phase changes in the light reflected from the patient can be detected. For cameras that employ the stereo principle, multiple cameras can be placed in different positions to capture multiple images of the patient's head, and a depth map can be calculated based on geometry. This depth information can be used to track the patient's head during treatment.

[0062] The computer system 100 may also include at least one computer processor 122, a user interface 126, and an input unit 130. In an exemplary embodiment, the input unit 130 may be used by the dentist together with a display unit 128 (such as a monitor) to send instructions or requests about capturing facial images, reconstructing a 3D image of the face 10, determining fixed points 20 and moving points 21, and / or calculating articulator values. They may also be used for treatment planning, including, for example, the creation of restorations. In another exemplary embodiment of the present invention, the input unit 130 is a finger or stylus to be used on a touch screen interface (not shown). The input unit 130 may alternatively be a gesture / voice recognition device, a trackball, a mouse, or other input device (such as a keyboard or stylus). In one example, the display unit 128, the input unit 130, and the computer processor 122 may together form the user interface 126.

[0063] The computer processor 122 may include, for example, a central processing unit, a multi-processing unit, an application specific integrated circuit (“ASIC”), a field programmable gate array (“FPGA”), etc. The processor 122 may be connected to a communication infrastructure 124 (e.g., a communication bus or network). In embodiments herein, the processor 122 may receive a request for automatically capturing facial images, automatically storing images in a database, automatically reconstructing a 3D model of the face 10, automatically determining fixed points 20 and moving points 21, automatically calculating and filling in articulator parameters, and / or automatically generating a treatment plan, restoring, or analyzing the function of the teeth 12. The processor 122 may then load the instructions and execute the loaded instructions, such as using a database or artificial intelligence (AI) to obtain a treatment for display.

[0064] One or more steps / processes for determining articulator parameters may be stored in the form of computer-readable program instructions on a non-transitory storage device. To execute a process, the processor 122 loads the appropriate instructions stored on the storage device into memory and then executes the loaded instructions.

[0065] The computer system 100 may also include a main memory 132, which may be random access memory ("RAM"), and may also include a secondary memory 134. The secondary memory 134 may include, for example, a hard disk drive 136 and / or a removable storage drive 138 (e.g., a floppy disk drive, a tape drive, an optical disk drive, a flash drive, etc.). The removable storage drive 138 may read from and / or write to a removable storage unit 140 in a well-known manner. The removable storage unit 140 may be, for example, a floppy disk, a tape, an optical disk, a flash memory device, etc., which may be written to and read from by the removable storage drive 138. The removable storage unit 140 may include a non-transitory computer-readable storage medium that stores computer-executable software instructions and / or data.

[0066] In still further alternative embodiments, secondary storage 134 may include other computer-readable media that store computer-executable programs or other instructions to be loaded into computer system 100. Such devices may include removable storage units 144 and interfaces 142 (e.g., program cartridges and cartridge interfaces); removable memory chips (e.g., erasable programmable read-only memory (“EPROM”) or programmable read-only memory (“PROM”)) and associated memory sockets; and other removable storage units 144 and interfaces 142 that allow software and data to be transferred from removable storage units 144 to other parts of computer system 100.

[0067] The computer system 100 may also include a communications interface 146 that enables software and data to be transferred between the computer system 100 and external devices. Such an interface may include a modem, a network interface (e.g., an Ethernet card or a wireless interface), a communications port (e.g., a Universal Serial Bus ("USB") port, or a port), Personal Computer Memory Card International Association ("PCMCIA") interface, Etc. Software and data transferred via communication interface 146 may be in the form of signals, which may be electronic, electromagnetic, optical, or another type of signal that may be capable of being sent and / or received by communication interface 146. The signals may be provided to communication interface 146 via communication path 148 (e.g., channel). Communication path 148 may carry signals and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency ("RF") link, etc. Communication interface 146 may be used to transfer software or data or other information between computer system 100 and a remote server or cloud-based storage (not shown).

[0068] One or more computer programs or computer control logic may be stored in main memory 132 and / or secondary memory 134. Computer programs may also be received via communication interface 146. The computer programs may include computer executable instructions that, when executed by computer processor 122, cause computer system 100 to perform methods as described below.

[0069] In another embodiment, the software may be stored in a non-transitory computer readable storage medium and loaded into the main memory 132 and / or the secondary memory 134 of the computer system 100 using the removable storage device drive 138, the hard disk drive 136 and / or the communication interface 146. When the control logic (software) is executed by the processor 122, the computer system 100, and more generally, the system for determining articulator parameters, is caused to perform all or some of the methods described herein.

[0070] In another example embodiment, the computer system 100 may be a mobile device, such as a smart phone, having an application that may be engaged by a user to propose and visualize dental and orthodontic treatments.

[0071] Implementation of other hardware arrangements to perform the functions described herein will be apparent to persons skilled in the relevant art(s) in view of this description.

Claims

1. A method for determining occlusal parameters, the method comprising: receiving an image of a patient's face; determining fixed and / or moving points in an image of the patient's face for use in calculating occlusal parameters using a trained neural network trained based on a labeled training image dataset to identify points of interest corresponding to the one or more fixed and / or moving training points by mapping the one or more fixed and / or moving training points in the training image to one or more highest position probability values ​​of a position probability vector, the one or more highest position probability values ​​indicating the location of the fixed and / or moving training points; calculating a set of geometric distances and angles between the determined fixed and / or moving points; In response to the determining step, calculating articulator parameters based on geometric distances and / or angles measured using the determined fixed and / or moving points; as well as The virtual articulator is populated with occlusal values ​​using the calculated articulator parameters.

2. The method of claim 1, wherein the articulator parameters are selected from the group consisting of: (i) sides of the Bonwill triangle, (ii) intercondylar distance, (iii) Balkwill angle, (iv) sagittal condylar path inclination, (v) Bennett angle, (vi) initial Bennett shift, and (vii) Spee curve.

3. The method according to claim 1 also includes reconstructing a 3D model of the patient's face based on the received image.

4. The method of claim 1 further comprising superimposing a scan of the patient's oral cavity on the 3D model.

5. The method of claim 1, wherein the fixed point comprises the location of the temporomandibular joint.

6. The method of claim 1, further comprising analyzing the function of the teeth based on articulator parameters.

7. The method of claim 1, further comprising generating a restoration or a treatment plan based on the articulator parameters.

8. The method of claim 1, wherein the trained neural network is a convolutional neural network.

9. A system for determining occlusal parameters, the system comprising at least one processor configured to perform the following steps: receiving an image of a patient's face; determining fixed and / or moving points in an image of the patient's face for use in calculating occlusal parameters using a trained neural network trained based on a labeled training image dataset to identify points of interest corresponding to the one or more fixed and / or moving training points by mapping the one or more fixed and / or moving training points in the training image to one or more highest position probability values ​​of a position probability vector, the one or more highest position probability values ​​indicating the location of the fixed and / or moving training points; calculating a set of geometric distances and angles between the determined fixed and / or moving points; In response to the determining step, calculating articulator parameters based on geometric distances and / or angles measured using the determined fixed and / or moving points; as well as The virtual articulator is populated with occlusal values ​​using the calculated articulator parameters.

10. The system of claim 9, wherein the processor is further configured to select an articulator parameter from the group consisting of: (i) sides of a Bonwill triangle, (ii) intercondylar distance, (iii) Balkwill angle, (iv) sagittal condylar path inclination, (v) Bennett angle, (vi) initial Bennett shift, and (vii) Spee curve.

11. The system of claim 9, wherein the processor is further configured to reconstruct a 3D model of the patient's face based on the received images.

12. The system of claim 9, wherein the processor is further configured to superimpose a scan of the patient's oral cavity on the 3D model.

13. The system of claim 9, wherein the fixed point comprises the location of the temporomandibular joint.

14. The system of claim 9, wherein the processor is further configured to generate a restoration or a treatment plan based on the articulator parameters.

15. A non-transitory computer-readable storage medium storing a program, wherein when the program is executed by a computer system, the computer system performs a process comprising: receiving an image of a patient's face; determining fixed and / or moving points in an image of the patient's face for use in calculating occlusal parameters using a trained neural network trained based on a labeled training image dataset to identify points of interest corresponding to the one or more fixed and / or moving training points by mapping the one or more fixed and / or moving training points in the training image to one or more highest position probability values ​​of a position probability vector, the one or more highest position probability values ​​indicating the location of the fixed and / or moving training points; calculating a set of geometric distances and angles between the determined fixed and / or moving points; In response to the determining step, calculating articulator parameters based on geometric distances and / or angles measured using the determined fixed and / or moving points; as well as The virtual articulator is populated with occlusal values ​​using the calculated articulator parameters.

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