Method for estimating the three-dimensional structure of teeth

Abstract

The device comprises an elongated body, comprising: a) a distal end having a face portion; b) a proximal end having a wireless electrical connection to a power source; and c) a shaft connecting the distal end and the proximal end, the shaft having a central shaft axis passing through the shaft and extending from the distal end to the proximal end, the face portion comprising: 1) at least one optical sensor having a field of view and configured to capture an image, the optical sensor having a central optical axis perpendicular to the field of view, the central optical axis being perpendicular to the shaft central axis; and 2) a plurality of illumination sources oriented to illuminate the field of view.

Description

[Technical Field]

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 403,827, filed September 5, 2022, the contents of which are incorporated herein by reference in their entirety. [Background technology]

[0002] In dentistry, it is essential to capture three-dimensional dental features and make replicas of the oral cavity using methods such as dental impressions and intraoral scans. These three-dimensional dental features help dentists make diagnoses and develop treatment plans. Capturing these features is also required for the manufacture of orthodontic aligners, braces, and dental prostheses. Summary of the Invention

[0003] In some embodiments, the invention provides a device comprising an elongate body, the device comprising: a) a distal end having a face; b) a proximal end having a wireless electrical connection to a power source; and c) a shaft connecting the distal end and the proximal end, the shaft having a central shaft axis passing through the shaft and extending from the distal end to the proximal end, the face comprising: 1) at least one optical sensor having a field of view and configured to capture an image, the optical sensor having a central optical axis perpendicular to the field of view, the central optical axis being perpendicular to the shaft central axis; and 2) a plurality of illumination sources oriented to illuminate the field of view.

[0004] In some embodiments, the invention provides a method comprising: a) obtaining a set of two-dimensional images of teeth with an optical sensor; and b) generating a three-dimensional representation of the teeth based at least in part on the set of two-dimensional images of the teeth.

[0005] In some embodiments, the invention provides a kit comprising: a) the device described above; and b) instructions describing how to use the device in connection with the method described above.

[0006] In some embodiments, the present invention provides the use of the above-described apparatus for carrying out the above-described method. [Brief explanation of the drawings]

[0007] [Figure 1] 1 shows a schematic diagram of a system for generating a three-dimensional representation of a subject's teeth from two-dimensional images. [Figure 2] 1 shows a tip equipped with an intraoral camera or camera system for acquiring images of teeth as described herein. [Figure 3] 1 illustrates an intraoral camera or camera system as described herein. [Figure 4] 1 illustrates one embodiment of a two-dimensional image obtained by the methods described herein. [Figure 5] 1 illustrates one embodiment of a two-dimensional image represented in pixels. [Figure 6] 1 illustrates a schematic diagram of an example workflow for a trained machine learning model described herein. DETAILED DESCRIPTION OF THE INVENTION

[0008] The manufacture of orthodontic aligners, braces, and dental prostheses requires the acquisition of accurate three-dimensional representations of dental features. Impressions of the teeth for generating such three-dimensional models can be acquired by a user or an orthodontist using a dental impression kit. Alternatively, the user's mouth can be scanned using a three-dimensional scanner. However, these methods of acquiring the information necessary to generate a three-dimensional model of a user's teeth are time-consuming, prone to user error, and require specialized equipment.

[0009] 3D scanners often rely on sophisticated sensors, such as optical sensors with high-speed global shutters. These sensors often include a pattern projector and rely on electric motors to drive part of the optical system. These sensors also feature high-quality optics. This increased complexity in 3D scanner design can result in large devices that are difficult to access in all areas of the oral cavity. Scanning with such 3D scanners can require up to 50,000 images. Such extensive scanning requires high data bandwidth for data transmission. These scanners often require frequent and time-consuming calibration after each use. This increases the cost of the device itself and the labor and specialized skills required to operate it, reducing the practicality of this method.

[0010] The present disclosure provides methods, systems, algorithms, computer programs, kits, devices, and computer-executable code for generating three-dimensional (3D) representations of a user's dental features from two-dimensional (2D) images. These methods, systems, algorithms, computer programs, kits, devices, and computer-executable code can be used to generate three-dimensional representations of a user's dental features from two-dimensional images acquired by an intraoral camera or camera system. These methods, systems, algorithms, computer programs, kits, devices, and computer-executable code can use trained machine learning models to generate the three-dimensional representations of a user's dental features.

[0011] In some embodiments, the three-dimensional representation is characterized by its accuracy. Accuracy refers to the degree of agreement between the test results and a defined reference value. For example, the test results can be observed, calculated, or estimated, while the reference value can be a true value. In some embodiments, the three-dimensional reconstruction of the tooth shape represents the test results, and the reference value represents the true value for the actual tooth measurements.

[0012] In some embodiments, the precision of the three-dimensional representation is about 1-1000 μm, characterized by high precision and accuracy, hi some embodiments, precision is measured as the maximum point-to-point distance between the test result and the reference value.

[0013] In some embodiments, the methods, systems, algorithms, computer programs, kits, devices, and computer-executable code described herein can generate, directly or through various intermediate processing steps, a three-dimensional representation of a tooth or teeth for fabrication of dental components such as orthodontic devices, braces, prostheses, hardware, or appliances. In some embodiments, surface data is obtained from a dental model, such as a dental prosthesis, to ensure a fit using a previous scan of the dentition prepared for the prosthesis, such as the tooth surfaces corresponding to the prosthesis.

[0014] In some embodiments, the methods, systems, algorithms, computer programs, kits, devices, and computer executable codes described herein can be used to generate a three-dimensional representation of a user's teeth or dentition from two-dimensional images using a trained machine learning model that includes a training set consisting of two-dimensional images of the teeth and predetermined three-dimensional structures of reference teeth. In some embodiments, the reference teeth are real teeth. In some embodiments, the reference teeth are artificially generated teeth. In some embodiments, the artificially generated teeth are generated from a mold. In some embodiments, the artificially generated teeth are virtually generated.

[0015] In some embodiments, the methods, systems, algorithms, computer programs, kits, devices, and computer executable code described herein enable the generation of three-dimensional representations using a single optical sensor, which in some embodiments does not need to be calibrated prior to use.

[0016] In some embodiments, the methods, systems, algorithms, computer programs, kits, apparatus, and computer executable code described herein include multiple optical sensors.

[0017] In some embodiments, the methods, systems, algorithms, computer programs, kits, devices, and computer executable codes described herein enable the generation of a three-dimensional representation using a single image. In some embodiments, the devices described herein generate a three-dimensional representation using multiple images, e.g., three images.

[0018] (Model Generation System) In some embodiments, the methods, systems, algorithms, computer programs, kits, devices, and computer executable codes described herein may be used to generate a three-dimensional representation of a subject in need thereof, including an adult or a child.

[0019] In some embodiments, the device includes an elongate body, the device comprising: a) a distal end having a face; b) a proximal end having a wireless electrical connection to a power source; and c) a shaft connecting the distal and proximal ends. The shaft has a central shaft axis extending from the distal end to the proximal end. The face comprises: 1) a first optical sensor having a first field of view, the first optical sensor having a first central axis perpendicular to the first field of view, the first central axis being perpendicular to the central axis of the shaft; 2) a second optical sensor having a second field of view, the second optical sensor having a second central axis perpendicular to the second field of view, the second central axis being perpendicular to the central axis of the shaft and parallel to the first central axis, the first field of view overlapping the second field of view; and 3) a plurality of illumination sources oriented to illuminate the first field of view and the second field of view.

[0020] In some embodiments, the first optical sensor is configured to capture a first two-dimensional image, and the second optical sensor is configured to capture a second two-dimensional image. In some embodiments, the first two-dimensional image and the second two-dimensional image do not overlap. In some embodiments, the first two-dimensional image and the second two-dimensional image partially overlap and do not have identical boundaries. In some embodiments, the first optical sensor and the second optical sensor have the same focal length. In some embodiments, the first optical sensor and the second optical sensor have different focal lengths. In some embodiments, the first focal length is greater than the focal length of the second optical sensor. In some embodiments, the focal length of the second optical sensor is greater than the focal length of the first optical sensor.

[0021] In some embodiments, the computing unit generates the three-dimensional representation of the teeth based, at least in part, on the image of the teeth obtained by the first optical sensor and the image of the teeth obtained by the second optical sensor. In some embodiments, the computing unit generates the three-dimensional representation of the teeth based, at least in part, on comparing the image of the teeth obtained by the first optical sensor and the image of the teeth obtained by the second optical sensor with a plurality of training images, where each training image is associated with a predetermined three-dimensional structure. In some embodiments, the device described herein may be an imaging device. In some embodiments, the device described herein may be an intraoral camera.

[0022] In some embodiments, the methods described herein include a) exposing teeth to an optical sensor; b) acquiring a set of two-dimensional images of the teeth with the optical sensor; and c) generating a three-dimensional representation of the teeth based at least in part on the set of two-dimensional images of the teeth.

[0023] 1 shows a schematic diagram of a system 100 for generating a three-dimensional representation of a subject's teeth from two-dimensional images. The system 100 comprises a device 107, a computing unit 104 and a display 105.

[0024] The device 107 has an elongate body with a distal end 102 having a face, a proximal end 103 having a wireless connection to a power source, and a shaft 106 connecting the distal and proximal ends. In some embodiments, the shaft has a central shaft axis running through the shaft from the distal end to the proximal end.

[0025] The distal end 102, which includes a face portion, includes an intraoral camera that captures two-dimensional images of one or more target teeth 101. The proximal end 103 includes a connection to a computing unit 104. The connection allows communication between the device 107 and the computing unit 104.

[0026] The elongated body of the device 107 may have a profile 108. The profile may have a bulge between the distal end 102 and the proximal end 103. The bulge may include a grip. The profile 108 may include at least one user input device, such as a button, lever, dial, thumbwheel, or switch, that allows a user to control the system 100, for example, to start and stop the image acquisition process. Examples of user input devices include, but are not limited to, a start scan control, a pause control, a stop control, a clear control, a save control, a recall control, and a light control. The face with the intraoral camera may be located at the distal end 102 of the probe, and the user input device may be located at the proximal end 103 of the probe.

[0027] In some embodiments, the shaft 106 may be a hand-held, freely positionable probe.

[0028] In some embodiments, the bulge portion has a maximum bulge width, the distal portion has a distal maximum width, and the proximal portion has a proximal maximum width, the maximum bulge width being greater than the distal maximum width, and the maximum bulge width being greater than the proximal maximum width.

[0029] In some embodiments, the dental feature of interest 101 comprises a tooth, multiple teeth, a portion of a tooth, a tooth structure, the upper dentition, the lower dentition, one or more features of a tooth, or any structure typically present in the oral cavity, hi some embodiments, the structure comprises the gums, tongue, or cheek.

[0030] In some embodiments, device 107 includes a distance maintaining mechanism that assists the user in maintaining a constant distance from the teeth while capturing images with device 107, allowing tip 102 to capture clear images while maintaining a fixed distance from the teeth.

[0031] In some embodiments, the device 107 comprises a mirror. In some embodiments, the mirror is located on the tip 102.

[0032] The tip 102 has a face portion that includes an intraoral camera. The intraoral camera is capable of acquiring a set of two-dimensional images of a target tooth or dentition during an image acquisition process. In some embodiments, the intraoral camera acquires the set of two-dimensional images in a continuous process. In some embodiments, the intraoral camera acquires the set of two-dimensional images in a discrete process.

[0033] During the image acquisition process, the intraoral camera acquires a set of two-dimensional images with sufficient spatial resolution and accuracy to generate a three-dimensional model.

[0034] In some embodiments, the set of two-dimensional images includes a two-dimensional image of a single tooth.

[0035] In some embodiments, the set of two-dimensional images includes at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 two-dimensional images of teeth.

[0036] In some embodiments, the set of two-dimensional images includes at most 3, at most 4, at most 5, at most 6, at most 7, at most 8, at most 9, or at most 10 two-dimensional images of teeth.

[0037] In some embodiments, the set of two-dimensional images for each tooth is obtained from an occlusal view, a mesial view, a distal view, a buccal view, a lingual view, or a combination thereof.

[0038] In some embodiments, the set of two-dimensional images includes at most 1, at most 2, at most 3, at most 4, or at most 5 occlusal images of the teeth.

[0039] In some embodiments, the set of two-dimensional images includes at most 1, at most 2, at most 3, at most 4, or at most 5 mesial images of teeth.

[0040] In some embodiments, the set of two-dimensional images includes at most 1, at most 2, at most 3, at most 4, or at most 5 distal images of the teeth.

[0041] In some embodiments, the set of two-dimensional images includes at most 1, at most 2, at most 3, at most 4, or at most 5 buccal images of teeth.

[0042] In some embodiments, the set of two-dimensional images includes at most 1, at most 2, at most 3, at most 4, or at most 5 lingual images of the teeth.

[0043] In some embodiments, each two-dimensional image in the set of two-dimensional images overlaps with at least one other two-dimensional image.

[0044] In some embodiments, each two-dimensional image in the set of two-dimensional images overlaps with at least one, at least two, at least three, at least four, or at least five other two-dimensional images.

[0045] In some embodiments, the overlap may range from about 10% to about 90%. For example, the overlap may be at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, or at least about 90%.

[0046] Examples of overlap include, but are not limited to, tooth size, tooth shape, tooth position, tooth orientation, crown size, crown shape, gingival position, gingival shape or contour, tooth-gingival interface position, and adjacent region position.

[0047] In some embodiments, the set of two-dimensional images includes two-dimensional images of a plurality of teeth, hi some embodiments, the set of two-dimensional images includes two-dimensional images of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32 teeth.

[0048] In some embodiments, a set of two-dimensional images containing multiple teeth may range from about 3 to about 10,000 or more two-dimensional images. For example, the set of two-dimensional images may be approximately 1, approximately 2, approximately 3, approximately 4, approximately 5, approximately 6, approximately 7, approximately 8, approximately 9, approximately 10, approximately 60, approximately 110, approximately 160, approximately 210, approximately 260, approximately 310, approximately 360, approximately 410, approximately 460, approximately 510, approximately 560, approximately 610, approximately 660, approximately 710, approximately 760, approximately 810, approximately 860, approximately 910, approximately 960, approximately 1,010, approximately 1,060, approximately 1,110, approximately 1,160, approximately 1,210, approximately 1,260, approximately 1,310, approximately 1,360, Approximately 1,410 sheets, approximately 1,460 sheets, approximately 1,510 sheets, approximately 1,560 sheets, approximately 1,610 sheets, approximately 1,660 sheets, approximately 1,710 sheets, approximately 1,760 sheets, approximately 1,810 sheets, approximately 1,860 sheets, approximately 1,910 sheets, approximately 1,960 sheets, approximately 2,010 sheets, approximately 2,060 sheets, approximately 2,110 sheets, approximately 2,160 sheets, approximately 2,210 sheets, approximately 2,260 sheets, approximately 2,310 sheets, approximately 2,360 sheets, approximately 2,410 sheets, approximately 2,460 sheets, approximately 2,510 sheets, approximately 2,560 sheets, approximately 2,610 sheets, approximately 2,660 sheets, approximately 2,710 sheets, approximately 2,760 sheets, approximately 2,810 sheets, approximately 2,860 sheets, approximately 2,910 sheets, approximately 2,960 sheets, approximately 3,010 sheets, approximately 3,060 sheets, approximately 3,110 sheets, approximately 3,160 sheets, approximately 3,210 sheets, approximately 3,260 sheets, approximately 3,310 sheets, approximately 3,360 sheets, approximately 3,410 sheets, approximately 3,460 sheets, approximately 3,510 sheets, approximately 3,560 sheets, approximately 3,610 sheets, approximately 3,660 sheets, approximately 3,710 sheets, approximately 3,760 sheets, approximately 3,810 sheets, approximately 3,860 sheets, approximately 3,910 sheets, approximately 3,960 sheets, approximately 4,010 sheets, approximately 4,060 sheets, approximately 4,110 sheets, approximately 4,160 sheets, approximately 4,210 sheets, approximately 4,260 sheets, approximately 4 ,310 sheets, approx. 4,360 sheets, approx. 4,410 sheets, approx. 4,460 sheets, approx. 4,510 sheets, approx. 4,560 sheets, approx. 4,610 sheets, approx. 4,660 sheets, approx. 4,710 sheets, approx. 4,760 sheets, approx. 4,810 sheets, approx. 4,860 sheets, approx. 4,910 sheets, approx. 4,960 sheets, approx. 5,010 sheets, approx. 5,060 sheets, approx. 5,110 sheets, approx. 5,160 sheets, approx. 5,210 sheets, approx. 5,260 sheets, approx. 5,310 sheets, approx. 5,360 sheets, approx. 5,410 sheets, approx. 5,460 sheets, approx. 5,510 sheets, approx. 5,560 sheets, approx. 5,610 sheets, approx. 5,660 sheets, approx. 5,710 sheets, approx.760 sheets, approx. 5,810 sheets, approx. 5,860 sheets, approx. 5,910 sheets, approx. 5,960 sheets, approx. 6,010 sheets, approx. 6,060 sheets, approx. 6,110 sheets, approx. 6,160 sheets, approx. 6,210 sheets, approx. 6,260 sheets, approx. 6,310 sheets, approx. 6,360 sheets, approx. 6,410 sheets, approx. 6,460 sheets, approx. 6,510 sheets, approx. 6,560 sheets, approx. 6,610 sheets, approx. 6,660 sheets, approx. 6,710 sheets, approx. 6,760 sheets, approx. 6,810 sheets, approx. 6,8 60 sheets, approx. 6,910 sheets, approx. 6,960 sheets, approx. 7,010 sheets, approx. 7,060 sheets, approx. 7,110 sheets, approx. 7,160 sheets, approx. 7,210 sheets, approx. 7,260 sheets, approx. 7,310 sheets, approx. 7,360 sheets, approx. 7,410 sheets, approx. 7,460 sheets, approx. 7,510 sheets, approx. 7,560 sheets, approx. 7,610 sheets, approx. 7,660 sheets, approx. 7,710 sheets, approx. 7,760 sheets, approx. 7,810 sheets, approx. 7,860 sheets, approx. 7,910 sheets, approx. 7,96 0, approx. 8,010, approx. 8,060, approx. 8,110, approx. 8,160, approx. 8,210, approx. 8,260, approx. 8,310, approx. 8,360, approx. 8,410, approx. 8,460, approx. 8,510, approx. 8,560, approx. 8,610, approx. 8,660, approx. 8,710, approx. 8,760, approx. 8,810, approx. 8,860, approx. 8,910, approx. 8,960, approx. 9,010, approx. 9,060 The image data may include about 9,110, about 9,160, about 9,210, about 9,260, about 9,310, about 9,360, about 9,410, about 9,460, about 9,510, about 9,560, about 9,610, about 9,660, about 9,710, about 9,760, about 9,810, about 9,860, about 9,910, about 9,960, or about 10,000 two-dimensional images of teeth.

[0049] In some embodiments, the set of two-dimensional images includes two-dimensional images of the same tooth.

[0050] In some embodiments, the set of two-dimensional images includes two-dimensional images of different teeth.

[0051] In some embodiments, the optical sensor may take between 5 seconds and 15 minutes to acquire a set of two-dimensional images. For example, the optical sensor may take about 5 seconds, about 10 seconds, about 15 seconds, about 20 seconds, about 25 seconds, about 30 seconds, about 1 minute, about 2 minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6 minutes, about 7 minutes, about 8 minutes, about 9 minutes, about 10 minutes, about 11 minutes, about 12 minutes, about 13 minutes, about 14 minutes, or about 15 minutes to acquire a set of two-dimensional images.

[0052] In some embodiments, a user may be trained to perform the image acquisition process. The frequency with which a user performs the image acquisition process may affect the time it takes to complete the image acquisition process. For example, if a user performs the image acquisition process frequently, the time it takes to complete the image acquisition process may be reduced.

[0053] In some embodiments, device 107 may have dimensions of about 210 mm x about 26 mm x about 28 mm. The length of device 107 may range from about 100 mm to about 400 mm. For example, the length of the device 107 may be about 100 mm, about 110 mm, about 120 mm, about 130 mm, about 140 mm, about 150 mm, about 160 mm, about 170 mm, about 180 mm, about 190 mm, about 200 mm, about 210 mm, about 220 mm, about 230 mm, about 240 mm, about 250 mm, about 260 mm, about 270 mm, about 280 mm, about 290 mm, about 300 mm, about 310 mm, about 320 mm, about 330 mm, about 340 mm, about 350 mm, about 360 mm, about 370 mm, about 380 mm, about 390 mm, or about 400 mm.

[0054] In some embodiments, the length of the elongate body of device 107 may range from about 100 mm to about 400 mm. For example, the length of the elongate body of device 107 may be about 100 mm, about 110 mm, about 120 mm, about 130 mm, about 140 mm, about 150 mm, about 160 mm, about 170 mm, about 180 mm, about 190 mm, about 200 mm, about 210 mm, about 220 mm, about 230 mm, about 240 mm, about 250 mm, about 260 mm, about 270 mm, about 280 mm, about 290 mm, about 300 mm, about 310 mm, about 320 mm, about 330 mm, about 340 mm, about 350 mm, about 360 mm, about 370 mm, about 380 mm, about 390 mm, or about 400 mm.

[0055] In some embodiments, the length of the elongate body of device 107 has a maximum length of about 200 mm to about 400 mm. For example, the maximum length of the elongate body of device 107 may be about 200 mm, about 210 mm, about 220 mm, about 230 mm, about 240 mm, about 250 mm, about 260 mm, about 270 mm, about 280 mm, about 290 mm, about 300 mm, about 310 mm, about 320 mm, about 330 mm, about 340 mm, about 350 mm, about 360 mm, about 370 mm, about 380 mm, about 390 mm, or about 400 mm.

[0056] In some embodiments, the maximum length of the elongate body of the device 107 may be about 200 mm or less, about 210 mm or less, about 220 mm or less, about 230 mm or less, about 240 mm or less, about 250 mm or less, about 260 mm or less, about 270 mm or less, about 280 mm or less, about 290 mm or less, about 300 mm or less, about 310 mm or less, about 320 mm or less, about 330 mm or less, about 340 mm or less, about 350 mm or less, about 360 mm or less, about 370 mm or less, about 380 mm or less, about 390 mm or less, or about 400 mm or less.

[0057] The width, or maximum width, of device 107 may range from about 20 mm to about 30 mm. For example, the width, or maximum width, of device 107 may be about 20 mm, about 21 mm, about 22 mm, about 23 mm, about 24 mm, about 25 mm, about 26 mm, about 27 mm, about 28 mm, about 29 mm, or about 30 mm.

[0058] The depth, or maximum depth, of device 107 may range from about 1 mm to about 50 mm. For example, the depth, or maximum depth, of device 107 may range from about 1 mm, about 2 mm, about 3 mm, about 4 mm, about 5 mm, about 6 mm, about 7 mm, about 8 mm, about 9 mm, about 10 mm, about 11 mm, about 12 mm, about 13 mm, about 14 mm, about 15 mm, about 16 mm, about 17 mm, about 18 mm, about 19 mm, about 20 mm, about 21 mm, about 22 mm, about 23 mm, about 24 mm, about 25 mm, about 26 mm, about 27 mm, about 28 mm, about 29 mm, about 30 mm, about 31 mm, about 32 mm, about 33 mm, about 34 mm, about 35 mm, about 36 mm, about 37 mm, about 38 mm, about 39 mm, about 40 mm, about 41 mm, about 42 mm, about 43 mm, about 44 mm, about 45 mm, about 46 mm, about 47 mm, about 48 mm, about 49 mm, about 50 mm, about 51 mm, about 52 mm, about 53 mm, about 54 mm, about 55 mm, about 56 mm, about 57 mm, about 58 mm, about 59 mm, about 60 mm, about 61 mm, about 62 mm, about 63 mm, about 64 mm, about 65 mm, about 66 mm, about 67 mm, about 68 mm, about 69 mm, about 70 mm, about 71 mm, about 72 mm, about 73 mm, about 74 mm, about 75 mm, about 76 mm, about 77 mm, about 78 mm, about 79 mm, mm, about 26 mm, about 27 mm, about 28 mm, about 29 mm, about 30 mm, about 31 mm, about 32 mm, about 33 mm, about 34 mm, about 35 mm, about 36 mm, about 37 mm, about 38 mm, about 39 mm, about 40 mm, about 41 mm, about 42 mm, about 43 mm, about 44 mm, about 45 mm, about 46 mm, about 47 mm, about 48 mm, about 49 mm, or about 50 mm.

[0059] The device 107 may be shaped and sized for intraoral image acquisition to be inserted into the oral cavity of a subject and pass over one or more intraoral structures (e.g., teeth) at an appropriate distance to acquire surface data from the subject's dentition, such as the teeth and gums. Examples of imaging device shapes include, but are not limited to, a cube, a sphere, a cylinder, a square, a rectangle, and a circle. The tip 102 may be sized to fit within a person's oral cavity.

[0060] In some embodiments, the width of tip 102 may be about 1 cm, about 2 cm, about 3 cm, about 4 cm, or about 5 cm. In some embodiments, the width of tip 102 may be about 1 cm or less, about 2 cm or less, about 3 cm or less, about 4 cm or less, or about 5 cm or less. In some embodiments, the width of tip 102 may range from 2 cm to 3 cm.

[0061] In some embodiments, the depth of tip 102 may be about 1 cm, about 2 cm, about 3 cm, about 4 cm, or about 5 cm. In some embodiments, the depth of tip 102 may be about 1 cm or less, about 2 cm or less, about 3 cm or less, about 4 cm or less, or about 5 cm or less. In some embodiments, the depth of tip 102 may range from 2 cm to 3 cm.

[0062] Examples of materials that can be used to manufacture the imaging device 107 include, but are not limited to, polyvinyl chloride, polyethylene, polypropylene, polystyrene, polyurethane, polyethylene terephthalate, polycarbonate, silicone, and combinations thereof. Other examples of materials that can be used to manufacture the device include, but are not limited to, steel, low carbon steel, medium carbon steel, high carbon steel, aluminum, brass, copper, lead, magnesium, nickel, titanium, zinc, and combinations thereof. Further examples of materials that can be used to manufacture the device include, but are not limited to, copper wire, aluminum wire, XHHW wire, THWN wire, and THHN wire.

[0063] Examples of chips that can be used to manufacture an imaging device include, but are not limited to, dynamic random access memory chips, microprocessors, application specific integrated circuits, digital signal processors, programmable memory chips, antennas, WiFi chips, and combinations thereof.

[0064] Examples of semiconductors that can be used to fabricate imaging devices include, but are not limited to, diamond, silicon, germanium, tin, silicon carbide, selenium, tellurium, boron nitride, zinc oxide, monovalent copper oxide, and combinations thereof.

[0065] In some embodiments, the mass of device 107 may range from about 1 gram to about 1,000 grams. For example, the total mass of device 107 may be about 1 gram, about 2 grams, about 3 grams, about 4 grams, about 5 grams, about 6 grams, about 7 grams, about 8 grams, about 9 grams, about 10 grams, about 15 grams, about 20 grams, about 25 grams, about 30 grams, about 35 grams, about 40 grams, about 45 grams, about 50 grams, about 60 grams, about 70 grams, about 80 grams, about 90 grams, about 100 grams, about 110 grams, about 120 grams, about 130 grams, about 140 grams, about 150 grams, about 160 grams, about 170 grams, about 180 grams, about 190 grams, about 200 grams, about 210 grams, about 220 grams, about 230 grams, about 240 grams, about 250 grams, about 260 grams, about 270 grams, about 280 grams, about 290 grams, about 300 grams, about 310 grams, about 320 grams, about 330 grams, about 340 grams, about 350 grams, about 360 grams, about 370 grams, about 380 grams, about 390 grams, about 400 grams, about 450 grams, about 400 grams, about 450 grams, about 500 grams, about 460 grams, about 470 grams, about 480 grams, about 490 grams, about 500 grams, about 510 grams, about 520 grams, about 530 grams, about 540 grams, about 550 grams, about 560 grams, about 570 grams, about 580 grams, about 590 grams, The total mass of the device 107 may be 20 grams, about 130 grams, about 140 grams, about 150 grams, about 200 grams, about 250 grams, about 300 grams, about 350 grams, about 400 grams, about 450 grams, about 500 grams, about 550 grams, about 600 grams, about 650 grams, about 700 grams, about 750 grams, about 800 grams, about 850 grams, about 900 grams, about 950 grams, or about 1,000 grams. In some embodiments, the total mass of the device 107 may be less than about 100 grams.

[0066] In some embodiments, the average power output of the device 107 is about 1 μW, about 2 μW, about 3 μW, about 4 μW, about 5 μW, about 6 μW, about 7 μW, about 8 μW, about 9 μW, about 10 μW, about 20 μW, about 30 μW, about 40 μW, about 50 μW, about 60 μW, about 70 μW, about 80 μW, about 90 μW, about 100 μW, about 200 μW, about 300 μW, about 400 μW, about 500 μW, about 600 μW W, about 700 μW, about 800 μW, about 900 μW, about 1 mW, about 2 mW, about 3 mW, about 4 mW, about 5 mW, about 6 mW, about 7 mW, about 8 mW, about 9 mW, about 10 mW, about 15 mW, about 20 mW, about 25 mW, about 30 mW, about 35 mW, about 40 mW, about 45 mW, about 50 mW, about 60 mW, about 70 mW, about 80 mW, about 90 mW, or about 100 mW.

[0067] In some embodiments, the devices described herein can be used repeatedly without overheating. In some embodiments, the devices described herein do not have a self-cooling mechanism. In some embodiments, the devices described herein do not have a fan. In some embodiments, the devices described herein do not have moving parts.

[0068] A set of two-dimensional images acquired by the distal end 102 having a face portion equipped with an intraoral camera during the image acquisition process may be transmitted via the proximal end 103 having a connection portion connecting to the computing unit 104 during the data transmission process.

[0069] The proximal end 103 with the connection may use any suitable communication link during the data transmission process. Examples of communication links include, but are not limited to, a wired connection or a wireless connection based on other suitable wireless standards, such as IEEE 802.11 (also known as Wireless Ethernet), Bluetooth®, or other suitable wireless standards using radio frequency, infrared, ultrasound, or other wireless communication mediums. Examples of computing units include, but are not limited to, personal computers such as portable PCs, slate and tablet PCs such as the Apple® Tablet and Samsung® Galaxy Tab, smartphones such as telephones, Apple® smartphones, Android-powered devices, Windows® Phones, and Blackberry®, smart watches such as the Apple® Watch, smart glasses such as Google® Glass, virtual reality devices, augmented reality devices, head-mounted devices such as VR headsets, or personal digital assistants.

[0070] The computing unit 104 generates a three-dimensional representation of the tooth or teeth based on the set of two-dimensional images acquired by the optical sensor, and can also generate control signals for the intraoral camera 102, which may include image acquisition commands as well as traditional camera controls such as focus and zoom.

[0071] In some embodiments, the computing unit 104 generates a three-dimensional representation of the teeth based at least in part on a set of two-dimensional images acquired by the optical sensor 201 .

[0072] In some embodiments, the computing unit 104 generates a three-dimensional representation of the teeth based at least in part on images of the teeth acquired by the optical sensor.

[0073] In some embodiments, the computing unit 104 generates the three-dimensional representation of the teeth based at least in part on comparing the image of the teeth acquired by the optical sensor with a reference image of a reference tooth, the reference image of the reference tooth being associated with a predetermined three-dimensional structure of the reference tooth.

[0074] In some embodiments, the computing unit 104 generates the three-dimensional representation of the teeth based at least in part on comparing the image of the teeth acquired by the optical sensor with a plurality of reference images, each of which is independent of a corresponding reference tooth and each reference image is associated with a predetermined three-dimensional structure of the corresponding reference tooth, and the computing unit 104 determines a reference image from the plurality of reference images that is most similar to the image of the teeth acquired by the optical sensor.

[0075] The display unit 105 displays the acquired two-dimensional images or the generated three-dimensional dental representation. The display unit 105 may include any display device suitable for rendering video or other rates at a level of detail corresponding to the acquired data or a rendered version of the acquired data. Examples of the display unit 105 include, but are not limited to, cathode ray tube displays, liquid crystal displays, light emitting diode displays, plasma displays, touchscreen displays, televisions, projectors, smartphones, smartwatches, tablets, and electronic glasses.

[0076] In some embodiments, the data transmission process is performed wirelessly, and can transmit data over a distance of about 1 meter, about 2 meters, about 3 meters, about 4 meters, about 5 meters, about 6 meters, about 7 meters, about 8 meters, about 9 meters, about 10 meters, about 11 meters, about 12 meters, about 13 meters, about 14 meters, about 15 meters, about 16 meters, about 17 meters, about 18 meters, about 19 meters, or about 20 meters.

[0077] The systems described herein can be used by a subject hourly, daily, weekly, monthly, yearly, occasionally, frequently, continuously, or chronically. The systems described herein can be used as needed depending on the subject's condition, based on the recommendation of a dentist or orthodontist, according to the subject's wishes, as needed to appropriately monitor the subject's condition, or for diagnostic or research purposes.

[0078] In some embodiments, the systems described herein can be used without calibration between uses. The systems described herein can be calibrated after at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten uses. The systems described herein can be used between different subjects without calibration. The systems described herein can be used between the same subject without calibration.

[0079] (intraoral camera) 2 shows a tip comprising an intraoral camera or camera system for acquiring images of teeth during an image acquisition process. The intraoral camera comprises at least one optical sensor 201, an optical lens 202, at least one illumination source 203, and a camera tip 204 surrounding the sensor.

[0080] In some embodiments, the optical lens 202 is a fixed-focus lens. The focal length of the fixed-focus lenses disclosed herein may range from about 18 mm to about 35 mm. Any suitable fixed-focus lens that meets this focal length range may be used. For example, the focal length of the fixed-focus lens may be about 18 mm, about 19 mm, about 20 mm, about 21 mm, about 22 mm, about 23 mm, about 24 mm, about 25 mm, about 26 mm, about 27 mm, about 28 mm, about 29 mm, about 30 mm, about 31 mm, about 32 mm, about 33 mm, about 34 mm, or about 35 mm.

[0081] In some embodiments, the intraoral camera comprises a camera tip 204. In some embodiments, the camera tip 204 surrounds at least one optical sensor 201.

[0082] The optical sensor 201 can capture an image or set of images of the teeth by detecting light waves passing through or reflecting off the target teeth during the image acquisition process. Any suitable optical sensor that detects and conveys information used to form the images can be used. Examples of optical sensors 201 include, but are not limited to, charge-coupled device (CCD) sensors, active pixel sensors (CMOS), and RGB sensors. In some embodiments, the frame rate of the RGB sensor can be between 5 and 60 Hz. For example, the frame rate can be between about 5 Hz, about 6 Hz, about 7 Hz, about 8 Hz, about 9 Hz, about 10 Hz, about 11 Hz, about 12 Hz, about 13 Hz, about 14 Hz, about 15 Hz, about 16 Hz, about 17 Hz, about 18 Hz, about 19 Hz, about 20 Hz, about 21 Hz, about 22 Hz, about 23 Hz, about 24 Hz, about 25 Hz, about 26 Hz, about 27 Hz, about 28 Hz, about 29 Hz, about 30 Hz, about 31 Hz, about 32 Hz, or about 33 Hz. The frequency may be about 33 Hz, about 34 Hz, about 35 Hz, about 36 Hz, about 37 Hz, about 38 Hz, about 39 Hz, about 40 Hz, about 41 Hz, about 42 Hz, about 43 Hz, about 44 Hz, about 45 Hz, about 46 Hz, about 47 Hz, about 48 Hz, about 49 Hz, about 50 Hz, about 51 Hz, about 52 Hz, about 53 Hz, about 54 Hz, about 55 Hz, about 56 Hz, about 57 Hz, about 58 Hz, about 59 Hz, or about 60 Hz.

[0083] Examples of image planes formed by optical sensor 201 include, but are not limited to, squares, circles, rectangles, and ellipses.

[0084] In some embodiments, the optical sensor 201 has a field of view, the optical sensor 201 has a central optical axis perpendicular to the field of view, the central optical axis being perpendicular to the shaft central axis, and the optical sensor 201 is configured to acquire an image or set of images.

[0085] In some embodiments, the optical sensor is an autofocus lens. In some embodiments, the autofocus lens has two or more positions. In some embodiments, the autofocus lens is fixed at a known position to mimic a fixed-focus lens. In some embodiments, the autofocus lens mimicking a fixed-focus lens has a focal length ranging from about 18 mm to about 35 mm. For example, the focal length of the fixed-focus lens may be about 18 mm, about 19 mm, about 20 mm, about 21 mm, about 22 mm, about 23 mm, about 24 mm, about 25 mm, about 26 mm, about 27 mm, about 28 mm, about 29 mm, about 30 mm, about 31 mm, about 32 mm, about 33 mm, about 34 mm, or about 35 mm.

[0086] In some embodiments, the tooth is an animal tooth present in an animal's oral cavity. In some embodiments, the tooth is a human tooth present in a human oral cavity. In some embodiments, the human oral cavity includes teeth adjacent to the human tooth. In some embodiments, the methods described herein further include exposing teeth adjacent to the human tooth to optical sensor 201.

[0087] In some embodiments, the human teeth are located in a dentition of the human oral cavity, and the methods described herein include exposing all teeth in the dentition to an optical sensor. In some embodiments, the methods described herein include exposing all teeth in the human oral cavity to an optical sensor.

[0088] In some embodiments, the methods described herein comprise exposing the tooth to an optical sensor, which comprises inserting the optical sensor into an oral cavity containing the tooth.

[0089] In some embodiments, the intraoral camera includes a first optical sensor and a second optical sensor. In some embodiments, the first optical sensor has a first field of view, the first optical sensor has a first central axis perpendicular to the first field of view, the first central axis being perpendicular to the shaft central axis. In some embodiments, the second optical sensor has a second field of view, the second optical sensor having a second field of view, the second optical sensor having a second central axis perpendicular to the second field of view, the second central axis being perpendicular to the shaft central axis, the second central axis being parallel to the first central axis. In some embodiments, the first field of view and the second field of view overlap.

[0090] In some embodiments, the first optical sensor is configured to capture a first two-dimensional image and the second optical sensor is configured to capture a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image overlap and do not have a coherent boundary.

[0091] The illumination source 203 assists in illuminating the subject's teeth during the image acquisition process. Any suitable light or lighting system can be used as the illumination source 203. In some embodiments, the illumination source generates visible or near-visible radiant energy. In some embodiments, the illumination source generates visible or near-visible radiant energy. Examples of illumination sources include, but are not limited to, incandescent lamps, fluorescent lamps, high intensity discharge (HID), solid state lighting (SSL), light emitting diodes (LEDs), laser diodes, organic LEDs, infrared, visible light, ultraviolet (UV) light, and other semiconductor light sources. The light or lighting system may emit light in the form of a strobe, flash, or spotlight. In some embodiments, the illumination source is oriented to illuminate the field of view.

[0092] In some embodiments, the device 107 includes a heated wire. In some embodiments, the heated wire is heated to prevent fogging of the intraoral camera. In some embodiments, the intraoral camera is heated by an illumination source such as an LED or a heated wire. In some embodiments, heating can prevent fogging of the image because the oral cavity is generally moist and warmer than room temperature.

[0093] In some embodiments, the optical sensor and the illumination source are the same. In some embodiments, the optical sensor and the illumination source are different.

[0094] In some embodiments, the intraoral camera includes at least one illumination source. In some embodiments, the intraoral camera includes multiple illumination sources. In some embodiments, the intraoral camera includes at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 illumination sources.

[0095] In some embodiments, the intraoral camera further comprises a shutter. In some embodiments, the shutter is a rolling shutter. In some embodiments, the rolling shutter is a mechanical rolling shutter or an electronic rolling shutter. In some embodiments, the shutter is not a global shutter.

[0096] FIG. 3 illustrates one embodiment of an intraoral camera or camera system described herein. The intraoral camera may include multiple illumination sources 302, 303. The illumination sources 302, 303 may be arranged in a pseudo-random pattern around an optical sensor 304. One or more illumination sources may be turned on 303 or off 302 at any time during the image acquisition process. Combinations of illumination sources may be turned on 303 or off 302 simultaneously during the image acquisition process. The illumination source on and off may be controlled by the profile 108 shown in FIG. 1. The optical sensor may be housed within the camera tip 301.

[0097] In some embodiments, a method of the image acquisition process described herein is provided, which includes (i) exposing a tooth of a subject to an optical sensor 401 and (ii) acquiring a set of two-dimensional images of the tooth with the optical sensor 401. During the image acquisition process, the tooth 405 is positioned within a focal length 403 of a fixed focus lens 406 of the optical sensor 401 described herein.

[0098] FIG. 4 illustrates one embodiment of restoring the scale of a captured two-dimensional image 400 using the methods described herein. If a tooth 401 is located within the focal length of a fixed-focus lens (i.e., between 18 and 36 mm), the tooth appears sharp in the captured two-dimensional image. If a tooth 402 is located further away than the focal length of the fixed-focus lens, the tooth 402 appears blurrier and darker compared to the tooth 401 captured from within the focal length. If a tooth 403 is located closer than the focal length of the fixed-focus lens, the tooth 403 appears blurrier and brighter compared to the tooth 401 captured from within the focal length. In some embodiments, the brightness of the tooth or teeth in the captured two-dimensional image may be used to determine the scale and depth of the tooth or teeth. In some embodiments, the shape of the shadow cast by the tooth or teeth in the captured two-dimensional image may be used to determine the scale and depth of the tooth or teeth. 404 shows the restored scale of the teeth 401 , 402 , 403 on the x-axis 405 , y-axis 406 and z-axis 407 .

[0099] In some embodiments, the way a lens depicts out-of-focus light points (i.e., blur) may be used to determine the scale of the teeth or teeth in the acquired two-dimensional image. In some embodiments, the scale of the teeth 401, 402, 403 is recovered by calibrating the relationship between distance, blur, and blur characteristics in the acquired two-dimensional image. In some embodiments, the scale of the teeth 401, 402, 403 is recovered by adding an additional calibrated pattern to the scene, such as a second camera or the pattern and size of an LED light reflection.

[0100] (Pre-trained machine learning model) In some embodiments, the computing unit 104 comprises an algorithm, which may be a trained machine learning model.

[0101] In some embodiments, the methods described herein comprise: (i) exposing the tooth to an optical sensor; (ii) acquiring a set of two-dimensional images of the teeth with an optical sensor; (iii) generating a three-dimensional representation of the teeth based at least in part on the set of two-dimensional images of the teeth. In some embodiments, generating the three-dimensional representation of the teeth comprises comparing the set of two-dimensional images of the teeth with a reference image of a reference tooth based at least in part on the set of two-dimensional images of the teeth. In some embodiments, the reference image of the reference tooth is associated with a predetermined three-dimensional structure of the reference tooth. In some embodiments, the method further comprises employing the predetermined three-dimensional structure of the reference tooth as the three-dimensional representation of the teeth. In some embodiments, the set of two-dimensional images of the teeth and the reference image of the reference tooth are acquired by the same optical sensor. In some embodiments, the set of two-dimensional images of the teeth and the reference image of the reference tooth are acquired by different optical sensors.

[0102] In some embodiments, generating a three-dimensional representation of a tooth based at least in part on a set of two-dimensional images of the tooth comprises: (i) comparing the set of two-dimensional images of the teeth with a plurality of reference images; (ii) determining, from the plurality of reference images, the reference image that is most similar to the set of two-dimensional images of the teeth; (iii) taking the predetermined three-dimensional structure of the reference tooth associated with the most similar reference image as the three-dimensional representation of the tooth, where each reference image is independently of a corresponding reference tooth and is associated with the predetermined three-dimensional structure of the corresponding reference tooth. In some embodiments, comparing the set of two-dimensional images of the teeth with a plurality of reference images includes using a trained machine learning model to match portions of the two-dimensional images of the teeth with portions of the reference images, the trained machine learning model being trained based on at least a subset of the plurality of reference images.

[0103] In some embodiments, the input for the trained machine learning model is a set of pixel-wise two-dimensional images. Figure 5 shows one embodiment of a pixel-wise two-dimensional image. 501 represents an individual pixel. The two-dimensional image includes an x-axis 502 and a y-axis 503.

[0104] FIG. 6 illustrates an example schematic of a workflow 600 of a trained machine learning model described herein.

[0105] The workflow 600 includes a training set that includes (1) a plurality of reference images, each independent of a corresponding reference tooth 601, and (2) a plurality of pairs of reference images, each associated with a predetermined three-dimensional structure of a corresponding reference tooth 602. In some embodiments, the predetermined three-dimensional structure of the corresponding reference tooth is in the form of a three-dimensional point cloud or a three-dimensional mesh.

[0106] The trained machine learning model can compare a set of two-dimensional images acquired by the optical sensor with a plurality of reference images 601 and determine a subset of two-dimensional images from the set of two-dimensional images acquired by the optical sensor that include teeth.

[0107] The trained machine learning model can then compare this subset of 2D images to multiple reference images 601 and multiple reference image pairs 602 to generate a predicted 3D point cloud 603. This predicted 3D point cloud 603 is then compared to the predetermined 3D structure of the reference teeth in 602, causing the trained machine learning algorithm to generate a consistent subset of predictions 604. Workflow 600 is repeated until a final subset of predictions 605 is generated, completing the full point cloud.

[0108] In some embodiments, computer code or manual adjustments are used to remove or filter two-dimensional images 606 that do not contain teeth or other structures typically present in the oral cavity, and to remove or filter irrelevant predictions 606 to generate filtered motion predictions as shown, completing the point cloud.

[0109] In some embodiments, the trained machine learning model is trained based on at least a subset of the plurality of reference images.

[0110] In some embodiments, the trained machine learning model may be used to determine the scale or depth of an object by the brightness of the acquired two-dimensional image, the pattern or size of the reflection of the illumination source in the two-dimensional image, the characteristics of the teeth in the two-dimensional image, the shape of the shadows in the two-dimensional image, or a combination thereof. In some embodiments, the machine learning algorithm can determine the scale of an object by calibrating the relationship between distance and blur in the acquired two-dimensional image.

[0111] In some embodiments, the trained machine learning algorithm includes a training set, a first portion of which is used to train a predictive model, and a second portion of which is used to validate the predictive model by testing the predictive ability of the model. There is no limitation on how the first and second portions of the training set are selected. The first and second portions may be dynamically selected for training and validation.

[0112] In some embodiments, the training set includes a plurality of reference images. The reference images may have a plurality of features associated with them, such that each training sample may be represented by a feature vector (generated based on the plurality of features) and an associated label. The feature vector may be a list of features, and each feature in the feature vector may be a measurable property of the reference image. The label may correspond to an output of the reference image, i.e., a desired classification or prediction.

[0113] In some embodiments, the prediction is pixel-wise depth. In some embodiments, the trained machine learning model is trained to predict depth given a reference image.

[0114] In some embodiments, each reference image is of a corresponding reference tooth and is independent of each other, hi some embodiments, each reference image is associated with a predetermined three-dimensional structure of the corresponding reference tooth.

[0115] In some embodiments, the training sets described herein may be in any form of electronic file or document that can be stored on a computer-readable medium, such as, but not limited to, a solid-state drive. The training sets may include any type of media, including, but not limited to, text files, HTML pages, PDF documents, formatting information, metadata, audio recordings, images, and video recordings.

[0116] Examples of feature vectors include, but are not limited to, query-independent (i.e., static features), query-dependent (i.e., dynamic features), and query-level features. Examples of features include, but are not limited to, TF, TF-IDF, BM25, IDF sum and document zone length, document PageRank, HITS rank, and other variations.

[0117] The systems and methods described herein are implemented by machine or computer executable code or software stored in an electronic storage area, such as a memory or electronic storage unit, of the server, and in use, the code is executed by a processor.

[0118] All or part of the trained machine learning model may be transmitted over the Internet or various other communication networks. Such communication may support loading of the algorithm from one computer or processor to another. An example of this is loading from a management server or host computer to an application server computer platform. Other types of media that may store algorithmic elements include optical, electrical, and electromagnetic waves used across physical interfaces (e.g., those used for communication over wired and fiber optic terrestrial networks and various spatial links). The physical elements that transmit such waves (e.g., wired, wireless, or optical links) are also considered media for storing the software.

[0119] A machine-readable medium embodying computer-executable code may take a variety of forms, including tangible storage media, carrier wave media, and physical transmission media. Examples of non-volatile storage media include, but are not limited to, optical and magnetic disks, any storage device in any computer that may be used, for example, to implement an algorithm. Volatile storage media include dynamic memory such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications.

[0120] Forms of computer readable media include floppy disks, flexible disks, hard disks, magnetic tape, other magnetic media, CD-ROMs, DVDs or DVD-ROMs, other optical media, punch cards, paper tape, other physical storage media with patterns of holes, RAM, ROM, PROMs and EPROMs, FLASH-EPROMs, other memory chips or cartridges, carrier waves carrying data or instructions, cables or links carrying such carrier waves, and other media from which a computer can read program code or data. Many of these computer readable media are involved in carrying one or more sequences of instructions to a processor for execution.

[0121] The methods or systems described herein are implemented with the aid of an application or app that can be installed on a target electronic device. The app includes a graphical user interface (GUI) on the display of the device or system described herein. The app is programmed or otherwise configured to perform various functions of the system, such as allowing a subject to manage, for example, creating and editing three-dimensional representations. The app's GUI is displayed on the target electronic device. Examples of electronic devices include, but are not limited to, computers, televisions, smartphones, tablets, and smartwatches. The electronic device may include, for example, a passive screen, a capacitive touchscreen, or a resistive touchscreen. The electronic device may also include a network interface and a browser that allows the subject to access various sites or locations (e.g., websites) on an intranet or the Internet. The app is configured to enable the mobile device to communicate with a server.

[0122] In some embodiments, the present disclosure provides a kit comprising (i) a device described herein and (ii) instructions for using the device in scanning teeth.

[0123] In some embodiments, the kit comprises (i) a device described herein and (ii) instructions for using the device in the methods described herein.

Claims

1. A system (100) for generating a three-dimensional representation of a user's dental features (101, 401, 402, 403), The device (107) is equipped with The device (107) comprises an elongated body, The aforementioned elongated body is a) A tip portion (102) having a surface portion, b) A base end (103) equipped with an electrical wireless connection to a power supply, c) comprising a shaft (106) connecting the tip portion (102) and the base portion (103), The shaft (106) has a central shaft axis that passes through the shaft (106) and extends from the tip portion (102) to the base portion (103), The aforementioned surface portion is 1) At least one optical sensor (201, 304) having a field of view, wherein the optical sensor (201, 304) has a central optical axis perpendicular to the field of view, the central optical axis is perpendicular to the central axis of the shaft, the optical sensor (201, 304) is configured to capture a two-dimensional image (400) of the dental features (101, 401, 402, 403), and comprises a fixed-focus lens (202), 2) comprising at least one illumination source (203, 302, 303) oriented to illuminate the field of view, The system (100) further comprises a computing unit (104) configured to communicate with the device (107), receive the two-dimensional image (400), and generate a three-dimensional representation (404) of the dental features (101, 401, 402, 403) based on the two-dimensional image (400), The scale of the aforementioned dental features (101, 401, 402, 403) is: The brightness of the dental features (101, 401, 402, 403) in the two-dimensional image (400), In the two-dimensional image (400), the shape of the shadow projected by the dental features (101, 401, 402, 403), and In the two-dimensional image (400), the blur generated by the fixed-focus lens (202) A system (100) determined based on the above.

2. The system (100) according to claim 1, wherein the surface portion further comprises an additional optical sensor.

3. The system (100) according to any one of claims 1 to 2, wherein the fixed-focus lens (202) is an autofocus lens that mimics a fixed-focus lens.

4. The system (100) according to any one of claims 1 to 2, wherein the optical sensors (201, 304) are RGB sensors.

5. The system (100) according to claim 4, wherein the RGB sensor has a frame rate of at least 5 Hz and / or up to 60 Hz.

6. The system (100) according to claim 1, wherein the lighting source (203, 302, 303) is a light-emitting diode (LED).

7. The system (100) according to claim 6, wherein the scale of the dental features (101, 401, 402, 403) is also determined based on the pattern and size of the reflection of the LED light.

8. A method for generating a three-dimensional representation (404) of a user's dental features (101, 401, 402, 403) using the system (100) described in Claim 1, a) Obtaining a set of two-dimensional images (400) of the dental features (101, 401, 402, 403) using the optical sensors (201, 304), b) A method comprising generating a three-dimensional representation (404) of the dental features (101, 401, 402, 403) based at least partially on a set of two-dimensional images (400) of the dental features (101, 401, 402, 403).

9. Generating the three-dimensional representation (404) of the dental features (101, 401, 402, 403) comprises comparing a set of two-dimensional images (400) of the dental features (101, 401, 402, 403) with a reference image of a reference dental feature, The method according to claim 8, wherein the reference image of the reference dental feature is associated with a predetermined three-dimensional structure of the reference dental feature.

10. The method according to claim 9, wherein the reference image of the reference dental feature is captured by the optical sensor (201, 304).

11. The method according to claim 9, wherein the reference image of the reference dental feature is generated by synthesis.

12. The method according to any one of claims 9 to 11, further comprising adopting a predetermined three-dimensional structure of the standard dental feature as the three-dimensional representation (404) of the dental feature (101, 401, 402, 403).

13. To generate the three-dimensional representation (404) of the dental features (101, 401, 402, 403), 1) Comparing the set of two-dimensional images (400) of the dental features (101, 401, 402, 403) with a plurality of reference images, 2) Determining which of the plurality of reference images is most similar to the set of two-dimensional images (400) of the dental features (101, 401, 402, 403), The aforementioned reference images are independent of their respective corresponding reference dental features. Each reference image is associated with the predetermined three-dimensional structure of the corresponding reference dental feature. The method according to claim 8.

14. 3) The method according to claim 13, further comprising, as the three-dimensional representation (404) of the dental features (101, 401, 402, 403), adopting a predetermined three-dimensional structure of the reference dental feature associated with the reference image that is most similar to the set of two-dimensional images (400) of the dental features (101, 401, 402, 403).

15. Comparing the set of two-dimensional images (400) of the dental features (101, 401, 402, 403) with the plurality of reference images includes using a trained machine learning model to match a portion of the two-dimensional images (400) of the dental features (101, 401, 402, 403) with a portion of the reference images, The method according to any one of claims 13 to 14, wherein the trained machine learning model is trained using at least a portion of the plurality of reference images.

16. The method according to claim 8, wherein the set of two-dimensional images (400) comprises three images of the dental feature (101, 401, 402, 403), and in particular includes a buccal image of the dental feature (101, 401, 402, 403), an occlusal image of the dental feature (101, 401, 402, 403), and a lingual image of the dental feature (101, 401, 402, 403).

17. The method according to claim 8, wherein in the set of two-dimensional images (400), each two-dimensional image (400) overlaps with at least one other two-dimensional image (400) by at least about 10%.

18. The method according to claim 8, wherein the three-dimensional representation (404) of the dental feature (101, 401, 402, 403) is a three-dimensional point cloud.

Images