Intraoral scanning system to fill an intervention of a dental object

Abstract

An intraoral scanning system (100) configured to fill an intervention (502) of a dental object (202). The system (100) comprises an intraoral scanner configured to provide scanned three-dimensional model (204). The system further comprise one or more processing units (302) configured to identify the intervention (502) (manually / automatically) in the dental object (202) based on the scanned three-dimensional model (204), determine a size (volume, area size) of the intervention based on the scanned three-dimensional model (204), determine a volume of a resin to be applied into the intervention (502), and wherein the volume of the material is determined based on the size of the intervention (502), and determine a material type of the material based on at least the size of the intervention (502) or the volume of the resin.

Description

INTRAORAL SCANNING SYSTEM TO FILL AN INTERVENTION OF A DENTAL OBJECTTECHNICAL FIELD

[0001] This invention generally relates to the field of curing with color-changing composite and, in particular, to a method and system for filling an intervention of a dental object using a colorchanging composite.BACKGROUND

[0002] As technology is advancing, photopolymers find place in dental composites for restorative fillings, repair of chipped, discolored or misshaped teeth, as an alternative to veneers, and various other applications. The photopolymers have an ability to transition from liquid to solid state when exposed to a specific wavelength of ultraviolet (UV) light. The photopolymers are available in various shades to match the tooth color of patients. For example, typical dental clinics may store 9 - 16 photopolymer-based composite materials covering different VITA shadings to cover different tooth colors of patients.

[0003] Accordingly, recent developments in photopolymers show a proof-of-concept for color changing photopolymers. The photopolymers can not only be cured by exposure to light but also transition into colors before they are cured. Thus, the dentist needs to maintain the intensity of UV light and exposure time for the curing process, for obtaining the desired tooth color. During the photopolymerization process, the dentist focuses on achieving optimal curing of the photopolymers. The photopolymerization process is crucial to prevent under-curing, which can pose risks to the patient. Similarly, it is crucial to prevent overcuring, which may lead to a brittle and weaker restoration. As the photopolymer cures, it experiences shrinkage, necessitating methods such as incremental curing or specific techniques, to minimize shrinkage stress. Controlling all the variables involved in this process can be challenging, and automating certain aspects could provide valuable assistance.

[0004] Therefore, there is a need to replace various VITA shading photopolymer materials with the color-changing photopolymer - the color changing photopolymer have more nuanced shade gradients within the same restoration, specks and imperfections that can match the old tooth and / orremaining surrounding teeth. Further, there is a need for a method of filling an intervention of a dental object using a color changing composite for curing profile.SUMMARY

[0005] It is an objective of the present disclosure to provide an input interface, e.g., a button interface, for an intraoral scanner that addresses one or more disadvantages of the prior art. As an example, the intraoral scanner includes buttons that are distinguishable from each other, e.g., by their tactile characteristics and / or sense of touch.

[0006] According to an aspect, the present embodiments disclose a computer-implemented method of filling an intervention of a dental object. The computer-implemented method steps include receiving a scanned three-dimensional model of the dental object. Further, the method steps include identifying the intervention in the dental object based on the scanned three-dimensional model. The intervention may be determined manually or automatically. Further, the method steps include determining dimension of the intervention based on the scanned three-dimensional model. The dimension may be a size in terms of volume or area. Further, the method steps include determining volume of a resin to be applied into the intervention. The volume of the material is determined based on the size of the intervention. Further, the method step includes determining a material type of the material based on at least the size of the intervention or the volume of the resin. The material having the volume and the material type is applied to the intervention. That may be done by manually by the user, by an injector that has received the material type information and the volume.

[0007] In some additional, alternative, or selectively cumulative embodiments, the identifying of the intervention further comprises steps of determining point clouds of the dental object based on the scanned three-dimensional model. Furthermore, the identifying of the intervention further comprises steps of determining a three-dimensional (3D) finite element mesh and aligning the point clouds to the 3D finite element mesh. The 3D finite element mesh includes a plurality of finite elements, and wherein each of the plurality of finite elements includes multiple vertices. Furthermore, the identifying of the intervention further comprises steps of determining, based on the scanned three-dimensional model, one or more optical coefficients for each of the multiple vertices, and wherein the one or more optical coefficients correspond to an intervention represented by the aligned point clouds.

[0008] In some additional, alternative, or selectively cumulative embodiments, the intervention may be determined at least based on one or more optical coefficients via an intervention neural network. The intervention neural network may be trained on one or more scanned three-dimensional models of one or more dental objects. The intervention neural network may further determine one or more training optical coefficients for each of the one or more scanned three-dimensional models. Furthermore, the intervention neural network may determine which of the one or more training optical coefficients that corresponds to an intervention by annotating which of the one or more scanned three-dimensional model that includes an intervention. The intervention neural network may further determine whether the one or more optical coefficients correspond to an intervention based on a correlation of the one or more optical coefficients with the one or more training optical coefficients. The correlation of the one or more optical coefficients with the one or more training optical coefficients may be based on a loss function.

[0009] In some additional, alternative, or selectively cumulative embodiments, the dimension of the intervention may be determined by determining a first dental feature boundary of the identified intervention at least based on the determined one or more optical coefficients. Further the dimension of the intervention may include determining the size between a first part of the first dental feature boundary and a second part of the first dental feature boundary. In another embodiment, the dimension of the intervention may be determined by determining a first dental feature boundary of the identified intervention at least based on the determined one or more optical coefficients. Further the dimension of the intervention may include determining the size by fitting a shape object to a geometry of the first dental feature boundary, and wherein the size is determined based on the fitted shape object.

[0010] In some additional, alternative, or selectively cumulative embodiments, the size may be a volumetric measure of the fitted shape object. The volumetric measure corresponds to a volume of the intervention. The determination of the size may include taking multiple internal measurements of the first dental feature boundary or to the fitted shape object. The volumetric measurement may be determined based on the multiple internal measurements.

[0011] In some additional, alternative, or selectively cumulative embodiments, the determined volume of the material to be applied into the intervention may be 0.5-25 % larger than the size of the intervention. Further, macro-fill composites material may be suitable for the size of the intervention between 10 to 50 microns; micro-fill composites material may be suitable for the sizeof the intervention between 0.01 to 0.1 microns; hybrid composites material may be suitable for a size of the intervention between 0.6 to 1 microns; nano-fill composites material may be for a size of the intervention of below 0.1 microns; and bulk-fill composites material may be for a size of the intervention of above 10 microns.

[0012] In some additional, alternative, or selectively cumulative embodiments, a curing time of the resin to be applied into the intervention may be determined based on a predetermined intensity level, the volume of the material, and the type of the material.

[0013] In some additional, alternative, or selectively cumulative embodiments, a method is disclosed that may include steps of curing the applied resin into the intervention by a first light towards the applied resin, and wherein the first light includes a first wavelength.

[0014] In some additional, alternative, or selectively cumulative embodiments, the method comprises steps of receiving color scan data of the dental object, determining one or more colors and / or shade values of the intervention based on the received color scan data of an area at least partially around the intervention, and determining a color exposure time of the resin with a second light that includes a second wavelength.

[0015] According to another aspect, the present embodiments disclose an intraoral scanning system configured to fill an intervention of a dental object. The system comprises an intraoral scanner configured to provide scanned three-dimensional model, and one or more processing units. The one or more processing units may identify an intervention (manually / automatically) in the dental object based on the scanned three-dimensional model, determine a size (volume, area size) of the intervention based on the scanned three-dimensional model, determine a volume of a resin to be applied into the intervention (the volume of the material is determined based on the size of the intervention), and determine a material type of the material based on at least the size of the intervention or the volume of the resin.

[0016] In some additional, alternative, or selectively cumulative embodiments, a handpiece device or by the intraoral scanner may comprise a first light source. Further, a first light source may be configured to cure the applied resin by emitting a first light that includes a first wavelength towards the resin.

[0017] In some additional, alternative, or selectively cumulative embodiments, the handpiece device or by the intraoral scanner may comprise a second first light source configured to colorexposure the applied resin by emitting a second light that includes a second wavelength towards the resin

[0018] In some additional, alternative, or selectively cumulative embodiments, the one or more processing units may be further configured to identify the intervention by determining point clouds of the dental object based on the scanned three-dimensional model, and determine a three-dimensional (3D) finite element mesh and align the point clouds to the 3D finite element mesh. The 3D finite element mesh includes a plurality of element blocks. Each of the plurality of element blocks includes a plurality of vertices. Further, the one or more processing units may determine, based on the scanned three-dimensional model, one or more optical coefficients for each of the plurality of vertices, and wherein the one or more optical coefficients correspond to an intervention represented by the aligned point clouds.

[0019] In some additional, alternative, or selectively cumulative embodiments, the one or more processing units may be further configured to determine size of the intervention by determining a first dental feature boundary of the identified intervention at least based on the determined one or more optical coefficients. Further, determining the size of the intervention may include determining the size between the first part of the first dental feature boundary and a second part of the first dental feature boundary. In another embodiment, the one or more processing units may determine the size of the intervention by determining a first dental feature boundary of the identified intervention at least based on the determined one or more optical coefficients. Further, determining the size of the intervention may include determining the size by fitting a shape object to a geometry of the first dental feature boundary, and wherein the size is determined based on the fitted shape object.

[0020] In some additional, alternative, or selectively cumulative embodiments, the one or more processing units may be further configured to receive color scan data of the dental object. Further, the one or more colors and / or shade values of the intervention may be determined based on the received color scan data of an area at least partially around the intervention. Further, one or more processing units may determine color exposure time of the resin with a second light that includes a second wavelength.

[0021] In some additional, alternative, or selectively cumulative embodiments, the one or more processing units may be further configured to recommend one or more colors and / or shape values via an artificial intelligence / machine learning model. Furthermore, the one or more processingunits may generate the color exposure time of the second light to obtain the recommended color via the artificial intelligence / machine learning model.BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries 30 (e.g. boxes, groups of boxes, or other shapes) in the figures represent one example 6 of the boundaries. It may be that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Furthermore, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions are described with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles.

[0023] FIG. 1 is a view of a clinical dental setting, in accordance with an embodiment of the disclosure;

[0024] FIG. 2 is a flow diagram of a process of using an intraoral scanner for scanning a dental object, in accordance with an embodiment of the disclosure;

[0025] FIG. 3 is a sequence diagram that depicts a method of filling an intervention of the dental object, in accordance with an embodiment of the disclosure;

[0026] FIG. 4 illustrates a three-dimensional (3D) finite element mesh of the dental object, in accordance with an embodiment of the disclosure;

[0027] FIG. 5 illustrates the 3D finite element mesh along with an intervention on the dental object, in accordance with an embodiment of the disclosure;

[0028] FIG. 6 is a flowchart of a method of identifying intervention in the dental object, in accordance with an embodiment of the disclosure;

[0029] FIG. 7 is a flowchart of a method of training an intervention neural network, in accordance with an embodiment of the disclosure;

[0030] FIG. 8 is a schematic representation of a first dental feature boundary of the intervention of the dental object, in accordance with an embodiment of the disclosure;

[0031] FIG. 9 is a representation of a process of profiling the intervention of the dental object, in accordance with an embodiment of the disclosure;

[0032] FIG. 10 is a flowchart of a method to fill a resin in the intervention, in accordance with an embodiment of the disclosure; and

[0033] FIG. 11 illustrates a representation of a first light source and a second light source included in the intraoral scanner, in accordance with an embodiment of the disclosure;DETAILED DESCRIPTION

[0034] Example embodiments are described below with reference to the accompanying drawings. Unless otherwise expressly stated in the drawings, the sizes, positions, etc., of components, features, elements, etc., as well as any distances therebetween, are not necessarily to scale, and may be disproportionate and / or exaggerated for clarity.

[0035] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be recognized that the terms “comprise,” “comprises,” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless otherwise specified, a range of values, when recited, includes both the upper and lower limits of the range, as well as any sub-ranges therebetween. Unless indicated otherwise, terms such as “first,” “second,” etc., are only used to distinguish one element from another. For example, one element could be termed a “first element” and similarly, another element could be termed a “second element,” or vice versa. The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0036] Unless indicated otherwise, the terms “about,” “thereabout,” “substantially,” etc. mean that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and / or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art.

[0037] Spatially relative terms, such as “right,” left,” “below,” “beneath,” “lower,” “above,” and “upper,” and the like, may be used herein for ease of description to describe one element's or feature's relationship to another element or feature, as illustrated in the drawings. It should be recognized that the spatially relative terms are intended to encompass different orientations in addition to the orientation depicted in the figures. For example, if an object in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the term “below” can, for example, encompass both an orientation of above and below. An object may be otherwise oriented (e.g., rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein may be interpreted accordingly.

[0038] Unless clearly indicated otherwise, all connections and all operative connections may be direct or indirect. Similarly, unless clearly indicated otherwise, all connections and all operative connections may be rigid or non-rigid.

[0039] Like numbers refer to like elements throughout. Thus, the same or similar numbers may be described with reference to other drawings even if they are neither mentioned nor described in the corresponding drawing. Also, even elements that are not denoted by reference numbers may be described with reference to other drawings.

[0040] Many different forms and embodiments are possible without deviating from the spirit and teachings of this disclosure and so this disclosure should not be construed as limited to the example embodiments set forth herein. Rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will convey the scope of the disclosure to those skilled in the art.

[0041] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

[0042] Referring to FIG. 1, a clinical dental setting (or simply, a setting 100) is illustrated, in accordance with an embodiment. The setting 100 includes various equipment; for example, the setting 100 includes an intraoral scanning system 102 having an intraoral scanner 104. The setting100 accommodates a user 106, such as a dentist, and a patient 110 to be inspected by the user 106. FIG. 1 depicts the user 106 performing an intraoral scan on the patient 110. Moreover, the setting 100 includes various devices that enable the user to perform operations, such as scanning and / or monitoring of an oral cavity of the patient 110, referred to as an intraoral scan. The intraoral scanning system 102 may also include an external computing device 108 and a display coupled to the external computing device 108. The display may include a touch screen display, although other displays known presently or developed in the future may be used. In some embodiments, the external computing device 108 may be located remotely to the setting 100. Further, in some embodiments, the display may be located locally to the setting 100, such as in the same room as the intraoral scanner 104. The external computing device 108 may also include additional devices, such as a mouse, a joystick, a keyboard, and the like.

[0043] Referring to FIG. 2, a flow diagram of a process of scanning a dental object 202 by the intraoral scanner 104 scanning is illustrated. The intraoral scanner 104 may be a handheld intraoral scanner for obtaining images of the dental object 202 of the patient 110 and / or capturing precise three-dimensional (3D) models 204 of the patient’s oral cavity. The dental object 202 within the patient’s oral cavity may include the patient’s teeth, gums, and surrounding tissues. The 3D models 204, as captured, may be utilized to determine profile of the oral cavity, for the patient 110. In an embodiment, the intraoral scanner 104 may transmit visible light and infrared light and obtain reflected light signals from the oral cavity. The intraoral scanner 104 may filter the light signals to obtain the 3D models 204. In an exemplary embodiment, the intraoral scanner 104 and the external computing device 108 may include corresponding processors and transceivers that may allow communication between the intraoral scanner 104 and the external computing device 108. In an exemplary embodiment, the intraoral scanner 104 and the external computing device 108 may communicate wirelessly for transmission of the 3D models 204 of the dental object 202.

[0044] Referring to FIG. 3, a sequence diagram that depicts a method 300 of filling an intervention of the dental object 202 is illustrated, in accordance with an embodiment. FIG. 3 is explained in conjunction with elements of FIG. 1 and FIG. 2. The intraoral scanning system 100 as depicted in FIG. 1 and FIG. 2, may be configured to implement the method 300 shown in FIG. 3. As shown in FIG. 3, the intraoral scanner 104 may be in communication with one or more processing units 302. The one or more processing units 302 may be integrated within the intraoral scanning system 100. In an exemplary embodiment, the one or more processing units 302 may be implementedindividually, and may be positioned between the intraoral scanner 104 and the external computing device 108. In another exemplary embodiment, the one or more processing units 302 may be implemented within the intraoral scanner 104. In yet another exemplary embodiment, the one or more processing units 302 may be implemented in the external computing device 108. Further, the sequence diagram depicts operations performed by the intraoral scanner 104 and the one or more processing units 302 for filling the intervention of the dental object 202.

[0045] At step S302, the intraoral scanner 104 generates the 3D model 204 by scanning the dental object 202. For example, the intraoral scanner 104 may emit the combination of visible light and infrared light. The reflection of the visible light and infrared light may be captured by the intraoral scanner 104. The intraoral scanner 104 may be configured to filter the captured light and generate the scanned 3D model 204. In an embodiment, the scanned 3D model 204 of the dental object 202 may be transmitted to one or more processing units 302, at step S304. In an exemplary embodiment, the scanned 3D model 204 of the dental object 202 may be transmitted wirelessly or may be transmitted via a wired connection to the one or more processing units 302.

[0046] At step S306, the one or more processing units 302 may receive the scanned 3D model 204 of the dental object 202. The one or more processing units 302 may be configured to compile the 3D models 204 of the dental object 202, received at different points in time. In an exemplary embodiment, the one or more processing units 302 may compile the received 3D models 204 to thereby generate and represent a mesh (not shown) corresponding to the 3D models 204. The mesh may be a combination of a plurality of data points (not shown) on the surface of the scanned 3D models 204 of the dental object 202. It may be noted that the plurality of data points may be generated using third party software or tools.

[0047] At step S308, the one or more processing units 302 may identify an intervention in the scanned 3D model 204 of the dental object 202. The intervention, for example, may be a caries, a crack, or a missing part of a tooth. The intervention may be identified either via a manual selection or may be selected automatically via the one or more processing units 302. In an embodiment, the intervention in the scanned 3D model 204 of the dental object 202 may be determined using an intervention neural network. For example, the intervention neural network may be pre-trained to identify the intervention in the scanned 3D model 204 of the dental object 202. The one or more processing units 302 may implement the intervention neural network, in order to identify the intervention in the scanned 3D model 204.

[0048] At step S310, the one or more processing units 302 may determine the size of the intervention in the scanned 3D model 204 of the dental object 202. The size may be the volumetric measure of a fitting shape object in the intervention. The size of the intervention may be characterized at least based on either volume or area of the intervention in the scanned 3D model 204 of the dental object 202. For example, the size of the intervention may be characterized at least based on the volume, if the depth of the intervention cannot be filled with a single coat of a resin. In another example, the size of the intervention may be characterized at least based on the area, if the depth of the intervention may be filled via a single coat of a resin. In yet another example, the size of the intervention may be characterized in combination with volume and area to fill the intervention with the resin.

[0049] At step S312, the one or more processing units 302 may determine the volume of the resin to be applied on the intervention in the scanned 3D model 204 of the dental object 202. The volume of the intervention may be determined based on the size of the intervention in the scanned 3D model 204 of the dental object 202. It may be noted that the resin may shrink when exposed to light during a curing process. In an exemplary embodiment, the volume of the resin may be determined to compensate for any possible shrinking, to thereby cover the entire size of the intervention. For example, the volume of the resin may be 0.5-25 % larger than the size of the intervention.

[0050] At step S314, the one or more processing units 302 may determine a material type of the resin based at least on the size of the intervention or the volume of the resin on the intervention in the scanned 3D model 204 of the dental object 202. The material type may be selected at least based on the size of the intervention. For example, a macro-fill composite may be suitable for the intervention between 10 to 50 microns, and a micro-fill composite may be suitable for intervention between 0.01 to 0.1 microns. Hybrid composites may be suitable for the intervention between 0.6 to 1 microns. Nano-fill composites may be suitable for intervention size less than 0.1 microns. Bulk-fill composites may be suitable for intervention size more than 10 microns.

[0051] Referring to FIG. 4, a rendering of a 3D finite element mesh 402 of the dental object 202 is illustrated, in accordance with some embodiments. The 3D finite element mesh 402 may be rendered and used to determine a 3D profile of the dental object 202. For example, the dental object 202 may be divided into a plurality of element blocks 404 in the 3D finite element mesh 402. Each of the plurality of element blocks 404 may be a rectangle, a triangle or a parabolictriangular element etc. As depicted in FIG. 4, the element block 404 may be a triangle having a plurality of vertices - al(xl, yl, zl), a2 (x2, y2, z2), and a3(x3, y3, z3). In order to render the 3D finite element mesh 402, the element blocks 404 may be connected to each other via the vertices of adjacent element blocks 404. In an exemplary embodiment, the density of the element blocks 404 may be increased or decreased to reduce the time taken for computing or rendering the 3D finite element mesh 402. The properties of each element block 404 may be defined at least based on the material of the dental object 202. Further, the plurality of vertices of the element block 404 may have one or more optical coefficients (not shown). The one or more optical coefficients may include absorption and / or scattering coefficients of the dental object in relations to the emitted light.

[0052] In an embodiment, the 3D model 204 of the dental object 202 may be represented as a cluster of a plurality of point clouds. Each of the plurality of point clouds may be a discrete set of data points representing the 3D model 204. A point cloud may be a collection of data points in 3D space, representing the surface of the dental object, with each point cloud having X, Y, and Z coordinates, and other data like color or intensity. The plurality of point clouds may be aligned with the element block 404 in the 3D finite element mesh 402.

[0053] Referring to FIG. 5, a rendering of the 3D finite element mesh 402 along with an intervention 502 on the dental object 202 is illustrated, in accordance with an embodiment. FIG. 5 is explained in conjunction with elements of FIG. 4. The intervention 502 in the 3D model 204 of the dental object 202 may be represented using one or more optical coefficients. The plurality of point clouds may be aligned with the one or more optical coefficients of the intervention 502 in the 3D finite element mesh 402.

[0054] Referring to FIG. 6, a flowchart of a method 600 of identifying intervention in the dental object 202 is illustrated, in accordance with an embodiment. FIG. 6 is explained in conjunction with elements of FIG. 4 and FIG. 5. In an embodiment, the method 600 includes steps for identifying the intervention via one or more optical coefficients corresponding to the intervention 502 represented by the aligned point clouds.

[0055] At step S602, the plurality of point clouds of the dental object 202 may be determined based on the scanned 3D model 204. The point cloud may include coordinate information to represent the scanned 3D model 204. Further, density of the point cloud may be increased ordecreased to accurately map the 3D model 204 of the dental object 202. In an embodiment, one or more processing units 302 may represent an output on the external computing device 108.

[0056] At step S604, the 3D finite element mesh 402 may be determined and the point clouds may be aligned to the 3D finite element mesh 402. The one or more processing units 302 may be configured to align the plurality of point clouds to the 3D finite element mesh 402. The 3D finite element mesh 402 may include the plurality of element blocks 404, as shown in FIG. 4. Each of the plurality of element blocks 404 may include plurality of vertices. In an exemplary embodiment, the one or more point clouds may be aligned to one or more element block 404.

[0057] At step S606, one or more optical coefficients corresponding to the plurality of vertices mya be determined. The one or more optical coefficients may correspond to the intervention 502 represented by the aligned point clouds. For example, the optical coefficient of the intervention 502 may reflect different intensity of light as compared to other optical coefficients for aligned point clouds of the dental object 202.

[0058] Referring to FIG. 7, a flowchart of a method 700 of training an intervention neural network is illustrated, in accordance with an embodiment. FIG. 7 is explained in conjunction with elements of FIG. 6. In an embodiment, the method 700 includes the steps to train the intervention neural network for determining the interventions 502 at least based on one or more optical coefficients. The neural network may be specifically trained to identify the interventions 502 in the dental object 202.

[0059] At step S702, one or more scanned 3D models 204 of the dental object 202 may be received. In an embodiment, the scanned 3D model 204 may be generated by the intraoral scanner 104. The scanned 3D model 204 may be stored on a server (not shown). In an exemplary embodiment, the one or more processing units 302 may be configured to process the scanned 3D model 204. In another embodiment, the scanned 3D model 204 may be processed on the server.

[0060] At step S704, one or more training optical coefficients may be determined for each of the one or more scanned 3D models 204. The one or more optical coefficients may correspond to both the intervention 502 and non-intervention sections in the scanned 3D model 204. For example, with reference to FIG. 4, the intervention neural network may be configured to identify the optical coefficient of the element block 404 in the 3D finite element mesh 402.

[0061] At step S706, the one or more training optical coefficients that correspond to the intervention 502 may be determined, by annotating the one or more scanned 3D model 204 thatincludes the intervention 502. The intervention neural network may be trained to determine the intervention and non-intervention optical coefficients. For example, whether the one or more optical coefficients correspond to an intervention may be determined based on a correlation of the one or more optical coefficients with the one or more training optical coefficients. For example, the optical coefficient may be determined to correspond to the intervention 502, when the correlation is close to the training optical coefficients. Further, the correlation of the one or more optical coefficients with the one or more training optical coefficients may be based on a loss function. For example, the optical coefficients may correspond to the intervention 502 if the correlation is close to one. The optical coefficients may correspond to non-intervention sections if the correlation is close to zero.

[0062] Referring to FIG. 8, a schematic representation of a first dental feature boundary 802 of the intervention 502 of the dental object 202 is illustrated, in accordance with an embodiment. The intervention 502 may be located in any region of the dental object 202. The first dental feature boundary 802 may be determined by the size of the intervention 502. The featured boundary may be of any shape. Thus, the size of the first dental feature boundary 802 may be calculated by determining the size of the intervention. Further, the size may be determined at least based on the first dental feature boundary 802 of the identified intervention. For example, the intervention 502 may be elliptical as shown in FIG. 8. The surface of the ellipse may be considered as the first dental feature boundary 802. Thus, the size of the intervention 502 may be calculated based on the distance between a first part of the first dental feature boundary 802 and a second part of the first dental feature boundary 802. In an embodiment, the first dental feature boundary 802 be determined at least based on one or more optical coefficients. The first dental feature boundary 802 may be determined due to the change in the optical coefficients in the intervention. Thus, the change in the one or more optical coefficients may be represented on the 3D finite element mesh 402.

[0063] In some embodiments, the size of the intervention 502 may be determined using a shape object 804 corresponding to the first dental feature boundary 802. The shape object 804 may be a soft or malleable material that may take the shape of the intervention 502, when the shape object 804 is pressed against the intervention 502. The size of the intervention 502 may be calculated based on a geometry of the shape object 804.

[0064] Further, the size of the intervention 502 may correspond to a volumetric measure of the fitted shape object 804. Thus, the volumetric measure of the shape object 804 may correspond to the volume of the intervention 502. In some cases, the intervention 502 may be irregular in shape that may require multiple point cloud for an internal measurement of the first dental feature boundary 802 or taking multiple measurements using the fitted shape object 804. Furthermore, the volumetric measurement may be determined at least based on the multiple internal measurements.

[0065] Referring to FIG. 9, a representation of a process of profiling the intervention 502 of the dental object 202 is illustrated, in accordance with an embodiment. The intraoral scanner 104 may transmit the scanned 3D model 204 via one or more processing units 302. In an embodiment, the user 106 holding the intraoral scanner 104 may mark the dental object 202 or the tooth number of the dental object 202. For example, the marking of the dental object 202 may be done via a button, or voice command etc. In an embodiment, the intraoral scanner 104 may unlock a specific filling or curing workflow. For example, the curing workflow may include a set of predefined parameters such as intensity of light, wavelength, and time for curing the material. The curing workflow may be stored on the server which may be accessed by the one or more processing units 302. The one or more processing units 302 may segment which dental object 202 on the 3D model corresponding to a set number of the dental objects 202 in the curing workflow. The curing workflow may be transferred between the intraoral scanner 104 and the server via communicating means such as Bluetooth, Wi-Fi, pod connectivity, chair integration, proximity detection or Wired data transfer.

[0066] In some embodiments, the intraoral scanner 104 may assist in determining a curing profile. The inputs from the intraoral scanner 104 may be processed by one or more processing units 302, to determine the size and volume of the intervention 502 in the dental object 202 via the intervention neural network. The curing profile and procedure may be generated for filling the intervention 502. For example, the curing profile and procedure may be fetched from a predetermined database, a set of pre-defined rules, or through machine learning, etc.

[0067] In some embodiments, the curing workflow may be transferred to a curing device (not shown). The curing device may be connected to the server to access the 3D model generated by the intraoral scanner 104. The curing workflow may load on the curing device via the one or more processing units 302. In another embodiment, the curing device may include a camera (not shown)that is able to accurately record color and automatically generate a curing recipe directly on the curing device.

[0068] Referring to FIG. 10, a flowchart of a method 1000 of filling resin in the intervention 502 is illustrated, in accordance with an embodiment. FIG. 10 is explained in conjunction with elements of FIG. 8 and FIG. 9. In an embodiment, the method 1000 includes the steps to fill the resin in the intervention at least based on the size of the intervention 502.

[0069] At step SI 002, the size and volume of the intervention 502 may be determined. In an embodiment, the scanned 3D model 204 may be generated via the intraoral scanner 104. The scanned 3D model 204 may be analyzed by the one or more processing units 302 using the finite element mesh 402. The one or more processing units 302 may be configured to determine the one or more coefficients. The one or more coefficients may be configured to determine first dental boundary 802. The first dental boundary 802 may be used to determine the size of the intervention 502.

[0070] At step SI 004, a type of filling required for the intervention 502 may be determined. Since the intervention 502 may vary in size or volume, therefore, the intervention 502 may require one or more filling to obtain an optimal curing profile. For example, if the filling material is applied in a single filling session in a voluminous intervention 502, the filling material may develop stress due to shrinkage, upon solidifying during the curing process. Thus, the one or more processing units 502 may be configured to choose optimal procedure based on the size and volume of the interventions 502. Thus, at step SI 004, a check is performed to determine whether a single filling is required or multiple fillings will be required. If it is determined that a single filling will be required, the method 1000 may follow steps SI 006 - SI 010. However, if it is determined that multiple fillings will be required, the method 1000 may follow steps SI 012 - SI 022.

[0071] At step SI 006, with respect to a single filling, a filling material for the intervention 502 may be selected. The one or more processing units 302 may select the filling material. The one or more processing units 302 may access the filling material from the predetermined database implemented on the server. For example, the filling material may be selected based at least on a shrinkage coefficient of the selected material.

[0072] At step SI 008, an amount of the filling material for the intervention 502 may be selected. The one or more processing units 302 may access the shrinkage coefficient in the filling material form the predetermined database on the server. The shrinkage coefficient may be the change involume that may occur after the curing process of the filling material. The amount of the filling material may be selected in combination with the volume required to fill the intervention 502 and shrinkage coefficient.

[0073] At step SI 010, a curing time of the resin may be determined based on an intensity level. The one or more processing units 302 may be configured to determine the curing time of the resin to be applied into the intervention 502 based on a predetermined intensity level, the volume of the material, and the type of the filling material. It may be noted that the predetermined intensity level, the volume of the filling material, and the type of the filling material may be directly dependent on each other. In an embodiment, the one or more processing units 302 is configured to fetch the optimal curing time from the predetermined database.

[0074] At step SI 012, with respect to multiple fillings, a first filling material for the intervention 502 may be selected. The one or more processing units 302 may select the first filling material. The one or more processing units 302 may access the first filling material from the predetermined database on the server. For example, the first filling material may be selected based at least on the shrinkage coefficient of the selected material.

[0075] At step SI 014, the amount of the first filling material for the intervention 502 may be selected. The one or more processing units 302 may access the shrinkage coefficient in the first filling material form the predetermined database on the server. The shrinkage coefficient may be the change (i.e. reduction) in volume that may occur after the curing process of the first filling material. The amount of the first filling material may be selected in combination with the volume required to fill the intervention 502 and the shrinkage coefficient.

[0076] At step SI 016, a first curing time of the resin may be determined based on an intensity level. The one or more processing units 302 may be configured to determine the first curing time of the resin to be applied into the intervention 502 based on the predetermined intensity level, the volume of the first filling material, and the type of the first filling material. It may be noted that the predetermined intensity level, the volume of the material, and the type of the filling material may be directly dependent on each other. In an embodiment, the one or more processing units 302 is configured to fetch the optimal curing time from the predetermined database.

[0077] At step SI 018, a second filling material for the intervention 502 may be selected. The one or more processing units 302 may select the second filling material. The second filling material may be same as the first filling material. The one or more processing units 302 may access thesecond filling material form the predetermined database on the server. For example, the second filling material may be selected based at least on the shrinkage coefficient of the selected material.

[0078] At step SI 020, the amount of the second filling material for the intervention 502 may be selected. The one or more processing units 302 may access a shrinkage coefficient of the second filling material from the predetermined database on the server. The shrinkage coefficient may be the change in volume that mya occur after the curing process of the second filling material. The amount of the second filling material may be selected in combination with the volume required to fill the intervention 502 and shrinkage coefficient.

[0079] At step SI 022, a second curing time of the resin may be determined based on an intensity level. The one or more processing units 302 may be configured to determine the second curing time of the resin to be applied into the intervention 502 based on a predetermined intensity level, the volume of the second filling material, and the type of the second filling material. It may be noted that the predetermined intensity level, the volume of the filling material, and the type of the filling material may be directly dependent on each other. In an embodiment, the one or more processing units 302 is configured to fetch the optimal curing time from the predetermined database. It may be noted that the above steps may be repeated until the intervention 502 of the dental object 202 is completely filled and cured.

[0080] In an embodiment, the one or more processing units 302 may be further configured to recommend one or more colors and / or shape values using an artificial intelligence / machine learning model. Furthermore, the one or more processing units 302 may generate the color exposure time of the second light to obtain the recommended color using the artificial intelligence / machine learning model.

[0081] Referring to FIG.11, a first light source 1102 and a second light source 1104 included in the intraoral scanner 104 are illustrated, in accordance with an embodiment. The intraoral scanner 104 may include the first light source 1102. The first light source 1102 may be arranged within and located at a tip of the intraoral scanner 104. The tip may be inserted partly or fully into the patient’s mouth during curing process. The light emitted from the first light source 1102 may pass through a window (not shown) provided on the intraoral scanner 104. The first light source 1102 may use Tungsten Halogen lamp, light-emitting diodes (LED), plasma arcs, or lasers. The first light source 1102 may be configured to cure the applied resin by emitting a first light that includes a first wavelength towards the resin. The first light may harden the resin via cross-linking.

[0082] The intraoral scanner 104 may further include the second light source 1104. In an embodiment, the second light source 1104 may be positioned adjacent to the first light source 1102. Further, the second light source 1104 may be configured to change the color by emitting a second light that includes a second wavelength towards the resin. In some embodiments, the intraoral scanner 104 receives a color scanned 3D model 204 of the dental object 202. The one or more color or shade values may be determined for an area at least partially around the intervention 502. The one or more processing units 302 may determine the color exposure time of the resin with the second light.

[0083] In another embodiment, an external handpiece (not shown) may include the first light source 1102 and the second light source 1104. The external handpiece with the first light source 1102 may be configured to cure the applied resin by emitting a first light that includes a first wavelength towards the resin. The first light may harden the resin via cross-linking. The external handpiece with the second light source 1104 may be configured to change the color by emitting a second light that includes a second wavelength towards the resin.

[0084] In some embodiments, the one or more processing units 302 may be configured to store the pre-determined curing profile and the color charts. The user 106 may adjust, refine, or change color of the curing profile of the intervention 502, as per the requirement. Subsequently, the user 106 may generate the curing profile on the scanned 3D model 204. Further, the external computing device 108 may be configured to display the optimal curing profile and the color on the display of the external computing device 108.

[0085] It should be understood that the foregoing description is only illustrative of the aspects of the disclosed embodiments. Various alternatives and modifications can be devised by those skilled in the art without departing from the aspects of the disclosed embodiments.

[0086] Items1. A computer-implemented method (300) configured for filling an intervention (502) of a dental object (202), wherein the computer-implemented method (100) comprising:receiving a scanned three-dimensional model (204) of the dental object (202), identifying the intervention (502) in the dental object (202) based on the scanned three- dimensional model (204),determining a size of the intervention (502) based on the scanned three-dimensional model (204),determining a volume of a resin to be applied into the intervention (502), and wherein the volume of the material is determined based on the size of the intervention (502), anddetermining a material type of the material is based on at least the size of the intervention (502) or the volume of the resin, wherein the material having the material type and the volume is filled into the intervention.The computer-implemented method (300) according to item 1, wherein the identifying of the intervention (502) is provided by:determining point clouds of the dental object (202) based on the scanned three- dimensional model (204),determining a three-dimensional (3D) finite element mesh (402) and align the point clouds to the 3D finite element mesh (402), and wherein the 3D finite element mesh (402) includes a plurality of element blocks (404), and wherein each of the plurality of element blocks (402) includes a plurality of vertices, anddetermining, based on the scanned three-dimensional model (204), one or more optical coefficients for each of the plurality of vertices, and wherein the one or more optical coefficients correspond to an intervention represented by the aligned point clouds.The computer-implemented method (300) according to item 2, determining, by an intervention neural network, the interventions (502) based on the one or more optical coefficients.The computer-implemented method (300) according to item 3, comprising training of the intervention neural network configured for:receiving one or more scanned three-dimensional model (204) of one or more dental objects (202),determining one or more training optical coefficients for each of the one or more scanned three-dimensional model (204),determining which of the one or more training optical coefficients that corresponds to an intervention by annotating which of the one or more scanned three-dimensional model (204) that includes the intervention (502), andwherein the intervention neural network is further configured for:determining whether the one or more optical coefficients correspond to an intervention (502) based on a correlation of the one or more optical coefficients with the one or more training optical coefficients.The computer- implemented method (300) according to item 4, wherein the correlation of the one or more optical coefficients with the one or more training optical coefficients is based on a loss function.The computer-implemented method (300) according to any of the previous items, wherein the determining of the size of the intervention (502) including:determining a first dental feature boundary (802) of the identified intervention based on the determined one or more optical coefficients, anddetermining the size between a first part of the first dental feature boundary (802) and a second part of the first dental feature boundary (802).The computer-implemented method (300) according to any of items 1 to 5, wherein the determining of the size of the intervention (502) including:determining the first dental feature boundary (802) of the identified intervention based on the determined one or more optical coefficients, anddetermining the size by fitting a shape object (804) to a geometry of the first dental feature boundary (802), and wherein the size is determined based on the fitted shape object (804).The computer-implemented method (300) according to item 7, wherein the size is a volumetric measure of the fitted shape object (802), and wherein the volumetric measure corresponds to a volume of the intervention (502), wherein the determining of the size includes multiple internal measurements of the first dental feature boundary (802) or to thefited shape object (804), and wherein volumetric measurement is determined based on the multiple internal measurements.The computer-implemented method (300) according to any of the previous items, wherein the determining of the volume of the material to be applied into the intervention (502) is 0.5-25 % larger than the size of the intervention.The computer-implemented method (300) according to any of the previous items, wherein the material type includes:macro-fill composites suitable for the size of the intervention (502) between 10 to 50 microns,micro-fill composites suitable for the size of the intervention (502) between 0.01 to 0.1 microns,hybrid composites suitable for the size of the intervention (502) between 0.6 to 1 microns,nano-fill composites for the size of the intervention (502) of below 0.1 microns, and bulk-fill composites for the size of the intervention (502) of above 10 microns.The computer-implemented method (300) according to any of the previous items, wherein the intervention (502) is a caries, a crack, or a missing part of a tooth.The computer-implemented method (300) according to any of the previous items, further comprising determining a curing time of the resin to be applied into the intervention (502) based on a predetermined intensity level, the volume of the material and the type of the material.The computer-implemented method (300) according to any of the previous items, further comprising curing the applied resin into the intervention by a first light towards the applied resin, and wherein the first light includes a first wavelength.The computer-implemented method (300) according to any of the previous items, further comprising:receiving color scan data of the dental object (202),determining one or more colors and / or shade values of the intervention (502) based on the received color scan data of an area at least partially around the intervention (502), and determining a color exposure time of the resin with a second light that includes a second wavelength.An intraoral scanning system (100) configured to fill an intervention (502) of a dental object (202), wherein the system (100) comprises:an intraoral scanner (104) configured to provide scanned three-dimensional model (204), andone or more processing units (302) configured to:identify the intervention (502) in the dental object based on the scanned three-dimensional model (204),determine a size of the intervention (502) based on the scanned three-dimensional model (204),determine a volume of a resin to be applied into the intervention (502), and wherein the volume of the material is determined based on the size of the intervention (502), and determine a material type of the material based on at least the size of the intervention (502) or the volume of the resin.The intraoral scanning system (100) according to item 15, comprises a first light source (1102) configured to cure the applied resin by emitting a first light that includes a first wavelength towards the resin.The intraoral scanning system (100) according to item 16, wherein the first light source (1102) is comprised by a handpiece device or by the intraoral scanner (104).The intraoral scanning system (100) according to any items 16 to 17, comprises a second light source (1104) configured to color exposure the applied resin by emitting a second light that includes a second wavelength towards the resin.The intraoral scanning system (100) according to item 18, wherein the second light source (1104) is comprised by a handpiece device or by the intraoral scanner (104).The intraoral scanning system (100 according to any of items 15 to 19, wherein the one or more processing units (302) further configured to identify the intervention (502) by:determining point clouds of the dental object (202) based on the scanned three- dimensional model (204),determining a three-dimensional (3D) finite element mesh (402) and align the point clouds to the 3D finite element mesh (402), and wherein the 3D finite element mesh includes a plurality of element blocks (404), and wherein each of the plurality of element blocks (404) includes a plurality of vertices, anddetermine, based on the scanned three-dimensional model (204), one or more optical coefficients for each of the multiple vertices, and wherein the one or more optical coefficients correspond to the intervention (502) represented by the aligned point clouds.The intraoral scanning system (100) according to any of items 15 to 20, wherein the one or more processing units (302) further configured to determine of the size of the intervention (502) include:determine a first dental feature boundary (802) of the identified intervention (502) based on the determined one or more optical coefficients, anddetermine the size between a first part of the first dental feature boundary (802) and a second part of the first dental feature boundary (802).The intraoral scanning system (100) according to any of items 15 to 21, wherein the one or more processing units (302) further configured to determine of the size of the intervention (502) include:determine the first dental feature boundary (802) of the identified intervention (502) based on the determined one or more optical coefficients, anddetermine the size by fitting a shape object (804) to a geometry of the first dental feature boundary (802), and wherein the size is determined based on the fitted shape object (804).23. The intraoral scanning system (100) according to any of items 15 to 22, wherein the one or more processing units (302) further comprises:receive color scan data of the dental object (202),determine one or more colors and / or shade values of the intervention (502) based on the received color scan data of an area at least partially around the intervention (502), anddetermine a color exposure time of the resin with a second light that includes a second wavelength.24. The intraoral scanning system (100) according to any of items 15 to 23, wherein the one or more processing units (302) further comprises:recommend one or more colors and / or shape values via an artificial intelligence / a machine learning model, andgenerate the color exposure time of the second light to obtain the recommended color via the artificial intelligence / the machine learning model.

Claims

CLAIMS1. A computer-implemented method (300) comprising:receiving a scanned three-dimensional model (204) of the dental object (202), identifying the intervention (502) in the dental object (202) based on the scanned three- dimensional model (204),determining a size of the intervention (502) based on the scanned three-dimensional model (204),determining a volume of a resin to be applied into the intervention (502), and wherein the volume of the material is determined based on the size of the intervention (502), anddetermining a material type of the material is based on at least the size of the intervention (502) or the volume of the resin.

2. The computer-implemented method (300) according to claim 1, wherein the identifying of the intervention (502) is provided by:determining point clouds of the dental object (202) based on the scanned three- dimensional model (204),determining a three-dimensional (3D) finite element mesh (402) and align the point clouds to the 3D finite element mesh (402), and wherein the 3D finite element mesh (402) includes a plurality of element blocks (404), and wherein each of the plurality of element blocks (402) includes a plurality of vertices, anddetermining, based on the scanned three-dimensional model (204), one or more optical coefficients for each of the plurality of vertices, and wherein the one or more optical coefficients correspond to an intervention represented by the aligned point clouds.

3. The computer-implemented method (300) according to claim 2, determining, by an intervention neural network, the interventions (502) based on the one or more optical coefficients.26 of 324. The computer-implemented method (300) according to claim 3, comprising training of the intervention neural network configured for:receiving one or more scanned three-dimensional model (204) of one or more dental objects (202),determining one or more training optical coefficients for each of the one or more scanned three-dimensional model (204),determining which of the one or more training optical coefficients that corresponds to an intervention by annotating which of the one or more scanned three-dimensional model (204) that includes the intervention (502), andwherein the intervention neural network is further configured for:determining whether the one or more optical coefficients correspond to an intervention (502) based on a correlation of the one or more optical coefficients with the one or more training optical coefficients.

5. The computer-implemented method (300) according to claim 4, wherein the correlation of the one or more optical coefficients with the one or more training optical coefficients is based on a loss function.

6. The computer-implemented method (300) according to any of the previous claims, wherein the determining of the size of the intervention (502) including: determining a first dental feature boundary (802) of the identified intervention based on the determined one or more optical coefficients, anddetermining the size between a first part of the first dental feature boundary (802) and a second part of the first dental feature boundary (802).

7. The computer-implemented method (300) according to any of claims 1 to 5, wherein the determining of the size of the intervention (502) including:determining the first dental feature boundary (802) of the identified intervention based on the determined one or more optical coefficients, anddetermining the size by fitting a shape object (804) to a geometry of the first dental feature boundary (802), and wherein the size is determined based on the fitted shape object (804).

8. The computer-implemented method (300) according to claim 7, wherein the size is a volumetric measure of the fitted shape object (802), and wherein the volumetric measure corresponds to a volume of the intervention (502), wherein the determining of the size includes multiple internal measurements of the first dental feature boundary (802) or to the fitted shape object (804), and wherein volumetric measurement is determined based on the multiple internal measurements.

9. The computer-implemented method (300) according to any of the previous claims, wherein the determining of the volume of the material to be applied into the intervention (502) is 0.5-25 % larger than the size of the intervention.

10. The computer-implemented method (300) according to any of the previous claims, wherein the material type includes:macro-fill composites suitable for the size of the intervention (502) between 10 to 50 microns,micro-fill composites suitable for the size of the intervention (502) between 0.01 to 0.1 microns,hybrid composites suitable for the size of the intervention (502) between 0.6 to 1 microns,nano-fill composites for the size of the intervention (502) of below 0.1 microns, and bulk-fill composites for the size of the intervention (502) of above 10 microns.

11. The computer-implemented method (300) according to any of the previous claims, wherein the intervention (502) is a caries, a crack, or a missing part of a tooth.

12. The computer-implemented method (300) according to any of the previous claims, further comprising determining a curing time of the resin to be applied into theintervention (502) based on a predetermined intensity level, the volume of the material and the type of the material.

13. The computer-implemented method (300) according to any of the previous claims, further comprising curing the applied resin into the intervention by a first light towards the applied resin, and wherein the first light includes a first wavelength.

14. The computer-implemented method (300) according to any of the previous claims, further comprising:receiving color scan data of the dental object (202),determining one or more colors and / or shade values of the intervention (502) based on the received color scan data of an area at least partially around the intervention (502), and determining a color exposure time of the resin with a second light that includes a second wavelength.

15. An intraoral scanning system (100) comprises:an intraoral scanner (104) configured to provide scanned three-dimensional model (204), andone or more processing units (302) configured to:identify the intervention (502) in the dental object based on the scanned three-dimensional model (204),determine a size of the intervention (502) based on the scanned three-dimensional model (204),determine a volume of a resin to be applied into the intervention (502), and wherein the volume of the material is determined based on the size of the intervention (502), and determine a material type of the material based on at least the size of the intervention (502) or the volume of the resin.

16. The intraoral scanning system (100) according to claim 15, comprises a first light source (1102) configured to cure the applied resin by emitting a first light that includes a first wavelength towards the resin.29 of 3217. The intraoral scanning system (100) according to claim 16, wherein the first light source (1102) is comprised by a handpiece device or by the intraoral scanner (104).

18. The intraoral scanning system (100) according to any claims 16 to 17, comprises a second light source (1104) configured to color exposure the applied resin by emitting a second light that includes a second wavelength towards the resin.

19. The intraoral scanning system (100) according to claim 18, wherein the second light source (1104) is comprised by a handpiece device or by the intraoral scanner (104).

20. The intraoral scanning system (100 according to any of claims 15 to 19, wherein the one or more processing units (302) further configured to identify the intervention (502) by:determining point clouds of the dental object (202) based on the scanned three- dimensional model (204),determining a three-dimensional (3D) finite element mesh (402) and align the point clouds to the 3D finite element mesh (402), and wherein the 3D finite element mesh includes a plurality of element blocks (404), and wherein each of the plurality of element blocks (404) includes a plurality of vertices, anddetermine, based on the scanned three-dimensional model (204), one or more optical coefficients for each of the multiple vertices, and wherein the one or more optical coefficients correspond to the intervention (502) represented by the aligned point clouds.

21. The intraoral scanning system (100) according to any of claims 15 to 20, wherein the one or more processing units (302) further configured to determine of the size of the intervention (502) include:determine a first dental feature boundary (802) of the identified intervention (502) based on the determined one or more optical coefficients, and30 of 32determine the size between a first part of the first dental feature boundary (802) and a second part of the first dental feature boundary (802).

22. The intraoral scanning system (100) according to any of claims 15 to 21, wherein the one or more processing units (302) further configured to determine of the size of the intervention (502) include:determine the first dental feature boundary (802) of the identified intervention (502) based on the determined one or more optical coefficients, anddetermine the size by fitting a shape object (804) to a geometry of the first dental feature boundary (802), and wherein the size is determined based on the fitted shape object (804).

23. The intraoral scanning system (100) according to any of claims 15 to 22, wherein the one or more processing units (302) further comprises:receive color scan data of the dental object (202),determine one or more colors and / or shade values of the intervention (502) based on the received color scan data of an area at least partially around the intervention (502), anddetermine a color exposure time of the resin with a second light that includes a second wavelength.

24. The intraoral scanning system (100) according to any of claims 15 to 23, wherein the one or more processing units (302) further comprises:recommend one or more colors and / or shape values via an artificial intelligence / a machine learning model, andgenerate the color exposure time of the second light to obtain the recommended color via the artificial intelligence / the machine learning model.31 of 32

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