An automatic quantitative intelligent analysis system and method for food impaction of close proximity

Abstract

The application discloses a kind of adjacent close type food plug automation quantitative intelligent analysis system and method, it is related to intraoral food plug diagnostic technical field, synchronous acquisition occlusal force data, static three-dimensional oral scanning data, buccal dynamic oral scanning data, jaw-face gap data;According to occlusal force data and static three-dimensional oral scanning data, three-dimensional tooth segmentation is carried out, and adjacent area boundary and three-dimensional occlusal contact positioning data are obtained;According to the boundary of adjacent area, establish plug area coordinate system;According to three-dimensional occlusal contact positioning data and buccal dynamic oral scanning data, buccal best fitting registration is carried out;Combined with plug area coordinate system and registration result, the Euclidean distance of discrete point is calculated, and the instantaneous separation of interproximal gap is obtained;Jaw-face gap data is converted into jaw-face color band mapping diagram;The type of patient food plug is identified and diagnosed and curative effect is evaluated.The application realizes the adjacent close type food plug chair-side quickly, accurately adjacent area key data, for the auxiliary diagnosis of adjacent close type food plug.

Description

Technical Field

[0001] This invention relates to the field of oral food impaction diagnosis technology, and more specifically to an automated quantitative intelligent analysis system and method for closely spaced food impaction. Background Technology

[0002] Food impaction refers to the phenomenon where food gets wedged into the interdental spaces between adjacent teeth during chewing under the influence of occlusal forces. It often causes a range of oral diseases, including swelling and discomfort, interproximal caries, halitosis, gingivitis, and periodontitis. Studies show that the incidence of food impaction is over 90%, with the majority of affected individuals being between 40 and 59 years old. The most common location is between the first and second maxillary molars.

[0003] The core cause of food impaction is the gap between the contact areas of two adjacent teeth. This gap may exist when the teeth are at rest or it may occur during chewing. Based on the timing of gap formation, food impaction is classified into static food impaction and (dynamic) close-proximal food impaction. Static food impaction refers to food impaction caused by partial or complete lack of proximal contact between affected teeth in a non-occlusal state (studies show that a gap width of approximately >60µm in the contact area constitutes static food impaction). Close-proximal food impaction, although there is contact between adjacent teeth in a resting state, experiences a brief separation of the contact area during occlusion, leading to food impaction; this type of impaction accounts for approximately 71.4% of all food impaction cases.

[0004] Clinically, the presence or absence of resistance when floss passes through the interproximal surfaces of teeth in a non-occlusal state is often used as a diagnostic criterion. If the floss encounters little or no resistance, it is diagnosed as stationary food impaction; if there is some resistance, it is diagnosed as close-proximal food impaction. However, due to differences in the diameter specifications of different brands of dental floss, individual variations in operator technique and subjective assessment, and the inability to accurately measure the actual tightness of the interproximal spaces under occlusal conditions, dental floss testing is primarily a qualitative evaluation method. It is difficult to quantify the interproximal spaces of impacted teeth, thus limiting its accuracy in clinical guidance. Furthermore, the evaluation of treatment for food impaction relies on the patient's subjective experience, and clinicians currently lack an objective, quantitative evaluation system.

[0005] Clinicians can choose different treatment methods depending on the type of food impaction. For static food impaction, due to the loss of proximal contact, the clinical approach is to directly or indirectly restore the proximal contact relationship between the teeth in front of and behind the impacted area. However, the pathogenesis of tightly packed food impaction, specifically the component force that opens the interproximal space due to the imbalance of forces on the teeth during occlusion, is widely accepted by scholars. Verification of this requires quantitative measurement of the changes in the interproximal space between the impacted teeth before and after occlusion. From a pathophysiological perspective, tightly packed food impaction suggests that the size of the interproximal contact area between adjacent teeth changes dynamically during chewing.

[0006] However, clinical diagnosis mainly relies on qualitative examination with dental floss and the patient's subjective feelings, which has limitations such as high operator dependence, inability to quantify and assess, and lack of objective indicators. In particular, it lacks the ability to detect dynamic changes in interproximal spaces under functional conditions, which restricts the realization of precise diagnosis and treatment.

[0007] In addition, such as Figure 1 As shown, the maximum friction force generated by removing the metal strip located in the adjacent area from the cheek or tongue, or the maximum friction force generated by the metal strip entering the adjacent area from the lip opening, is the adjacent area strength, which is a quantitative detection. It can be seen that the adjacent area is passively separated in the existing technology, which does not conform to the physiological condition, and the detection equipment is complicated.

[0008] Therefore, how to provide an automated quantitative intelligent analysis system and method for closely packed food impaction is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, the present invention provides an automated quantitative intelligent analysis system and method for close-proximal food impaction. It utilizes intraoral three-dimensional scanning technology to obtain data on the anatomical morphology and occlusal relationship of the patient's dentition. The distribution of occlusal contact is located using a color-coded interdental space map, and the force intensity in different areas of the occlusal surface is determined based on the color intensity, thereby assisting in the location of adjustment points. Furthermore, digital registration technology is used to perform registration analysis on dentition models under static and occlusal contact conditions, quantitatively measuring the changes in the boundary lines of the target tooth's proximal area before and after occlusion (including range and position). This allows for the calculation of the instantaneous changes in the interproximal area under functional conditions, i.e., the instantaneous separation width of the interproximal space under occlusal contact, thus enabling auxiliary diagnosis of close-proximal food impaction. After treatment, the efficacy can be verified based on the distribution changes in the color-coded interdental space map and the quantitative data of the target tooth's interproximal space, providing an objective and repeatable evaluation standard for the clinical diagnosis and treatment of this type of disease. This intelligent analysis system ultimately promotes the development of the diagnosis and treatment process for close-proximal food impaction towards visualization, standardization, precision, automation, and intelligence.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: an automated quantitative intelligent analysis system for closely packed food impaction, comprising: The data acquisition module is used to simultaneously acquire occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data based on the patient's dental occlusion physiological state. The tooth segmentation module is used to perform three-dimensional tooth segmentation based on the occlusal force data and static three-dimensional oral scan data to obtain the boundary of the adjacent area and three-dimensional occlusal contact positioning data. A coordinate system establishment module is used to establish a clogging region coordinate system based on the adjacent region boundary. The fitting and registration module is used to perform optimal buccal fitting and registration based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data. The calculation module is used to calculate the Euclidean distance of discrete points by combining the coordinate system of the plugged area and the registration results, and to obtain the instantaneous separation amount of adjacent gaps; The conversion module is used to convert the maxillofacial space data into a maxillofacial color band mapping map; The analysis and evaluation module is used to differentiate the type of food impaction in patients and evaluate the treatment efficacy by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

[0011] Preferably, three-dimensional tooth segmentation is performed based on the occlusal force data and static three-dimensional intraoral scan data, including: The acquired static 3D oral scan data is imported into the processor in point cloud mode for tooth segmentation processing. In this process, representative contour sampling points are selected from the dental surface using the farthest point sampling method, and the contour sampling points are sampled using a boundary awareness algorithm to obtain the final sampling points. The final sampling points are fed into the segmentation network for analysis to obtain the three-dimensional tooth segmentation results and the boundaries of adjacent regions.

[0012] Preferably, buccal best-fit registration is performed based on the three-dimensional occlusal contact positioning data and the buccal dynamic intraoral scan data, including: The iterative nearest-point algorithm is used for coarse registration of the cheek side fitting; A point-to-plane ICP algorithm was used for precise buccal fitting and registration. Weighted registration was then used to optimize the buccal region, resulting in the final registration result.

[0013] Preferably, the Euclidean distance between discrete points is calculated by combining the coordinate system of the plugged area and the registration results to obtain the instantaneous separation of adjacent gaps, including: Based on the registration results, the adjacent areas of teeth are automatically extracted using KD-Tree nearest point search and normal constraint. Then, calculate the distance between the two adjacent teeth before and after occlusion using the coordinate system of the impaction zone. Using the formula Δd=d 咬合后 -d 咬合前 Quantify the relative separation change of adjacent interpupillary spaces at the moment of occlusion.

[0014] Preferably, the Δd value at each point represents the degree of separation of the local adjacent area, and the maximum value of Δd is extracted to reflect the maximum degree of separation of the adjacent area, which is used as the basis for judging the close-proximity food impaction.

[0015] Preferably, the objective function for registration is: ; in, As the source point; Indicate the corresponding point for the target; Normal to the target point; For weights.

[0016] Preferably, an automated quantitative intelligent analysis method for closely packed food impaction includes: Based on the patient's dental occlusion physiological state, occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data are collected simultaneously. Based on the bite force data and static three-dimensional oral scan data, three-dimensional tooth segmentation is performed to obtain the boundary of the adjacent area and three-dimensional bite contact positioning data. Establish the crater region coordinate system based on the adjacent region boundary; Based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data, buccal best-fit registration is performed. By combining the coordinate system of the plugged area and the registration results, the Euclidean distance of discrete points is calculated to obtain the instantaneous separation amount of adjacent gaps; The maxillofacial space data is converted into a maxillofacial color band mapping map; The patient's food impaction type was differentiated and the treatment efficacy was evaluated by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

[0017] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses an automated quantitative intelligent analysis system and method for adjacent tight food impaction. Combining machine learning and cross-modal data models, it realizes the automated process from oral scan data to adjacent area contact characteristics, and realizes fast and accurate chairside key data of adjacent tight food impaction for auxiliary diagnosis of adjacent tight food impaction. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A schematic diagram of a prior art measuring device provided by the present invention.

[0020] Figure 2 This is a schematic diagram of an automated quantitative intelligent analysis method for closely packed food impaction provided in an embodiment of the present invention.

[0021] Figure 3 The diagram shows 2D and 3D schematics of the maximum intercuspal occlusal force distribution of a subject in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of intraoral three-dimensional scanning data of buccal occlusion when the upper right first molar and the upper right second molar (16, 17) are impacted in the upper and lower jaws and the largest cusp is in the intercuspation position, which is provided in an embodiment of the present invention.

[0023] Figure 5 Color band diagram of the occlusal space of a patient with impacted bites (numbers 46 and 47) provided in an embodiment of the present invention.

[0024] Figure 6 The diagram shows the occlusal contact of patients with impacted 46 and 47 as provided in the embodiments of the present invention.

[0025] Figure 7 The color band diagrams for the 16 and 17 interlocking gaps provided in the embodiments of the present invention.

[0026] Figure 8 This is a schematic diagram of buccal occlusion when the cusps are in the maximum intercuspal position, as provided in an embodiment of the present invention.

[0027] Figure 9 This is a schematic diagram of automated Δd measurement provided in an embodiment of the present invention.

[0028] Figure 10 This is a schematic diagram of the implementation process of an embodiment of the present invention, using intraoral three-dimensional scan data of a subject with close food impaction between the left upper first molar and the left upper second molar (26, 27) as an example.

[0029] Figure 11 This is a schematic diagram showing the changes in the adjacent areas of teeth 26 and 27 before and after occlusion, provided in an embodiment of the present invention.

[0030] Figure 12This is a diagram showing the change in the relative distance between adjacent tooth contact areas before and after occlusion, provided in an embodiment of the present invention.

[0031] Figure 13 This is a schematic diagram of buccal occlusion when the upper and lower jaws of the three units anterior and posterior to the impacted tooth are in the intercuspal position of the largest cusp, as provided in an embodiment of the present invention.

[0032] Figure 14 This is a schematic diagram illustrating the change Δd of the adjacent tooth contact area before and after occlusion, provided in an embodiment of the present invention.

[0033] Figure 15 This is a schematic diagram showing the comparison of patients' subjective evaluations before and after treatment, provided in an embodiment of the present invention.

[0034] Figure 16 This is a schematic diagram of the occlusal force on the occlusal surface of an impacted tooth after adjustment, provided as an embodiment of the present invention.

[0035] Figure 17 This is a schematic diagram illustrating the changes in Δd before and after treatment, provided in an embodiment of the present invention.

[0036] Figure 18 This is a schematic diagram illustrating the changes in inter-space occlusion before and after treatment, as provided in an embodiment of the present invention.

[0037] Figure 19 This is a schematic diagram of the tooth segmentation process provided in an embodiment of the present invention.

[0038] Figure 20 This is a schematic diagram of the digital registration process for teeth provided in an embodiment of the present invention.

[0039] Figure 21 This is a schematic diagram illustrating the calculation process for tooth adjacency distance and separation state provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] In recent years, the continuous integration of digital technology and oral medicine has provided new approaches for the quantitative measurement of interdental spaces. With the development of digital technology applications in dentistry, the accuracy of intraoral scanning impressions has become widely recognized. Directly locating the occlusal contact distribution of teeth based on the interdental space color bands on the digital model allows for the inference of the dynamic force distribution of teeth during mastication, offering a simple, intuitive, and precise method. Research results show that the dynamic maximum occlusal contact area during mastication overlaps by 98.5% with the maximum intercuspal occlusal contact area during static occlusion; therefore, the static occlusal contact recorded by the digital model fully meets clinical needs.

[0042] Based on the spatiotemporal characteristics of the gaps between teeth, food impaction can be classified into static and (mobile) close-proximal types. The choice of clinical treatment strategy largely depends on the specific classification. Clinically, the presence of resistance when floss passes through the proximal surfaces is often used for diagnosis, relying on the patient's subjective experience as a basis for evaluating treatment efficacy, lacking objective, repeatable, and quantitative guidance methods.

[0043] This invention discloses an automated quantitative intelligent analysis system for closely packed food impaction, comprising: The data acquisition module is used to simultaneously acquire occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data based on the patient's dental occlusion physiological state. The tooth segmentation module is used to perform three-dimensional tooth segmentation based on the occlusal force data and static three-dimensional oral scan data, and obtain the boundary of the adjacent area and three-dimensional occlusal contact positioning data, which is achieved through the above three-dimensional scanning, registration, distance calculation and contact determination.

[0044] A coordinate system establishment module is used to establish a clogging region coordinate system based on the adjacent region boundary. The fitting and registration module is used to perform optimal buccal fitting and registration based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data. The calculation module is used to calculate the Euclidean distance of discrete points by combining the coordinate system of the plugged area and the registration results, and to obtain the instantaneous separation amount of adjacent gaps; The conversion module is used to convert the maxillofacial space data into a maxillofacial color band mapping map; The analysis and evaluation module is used to differentiate the type of food impaction in patients and evaluate the treatment efficacy by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

[0045] During chewing, teeth undergo minute rotations or tilts around the root due to the biomechanical properties of the periodontal ligament, resulting in dynamic changes in the interproximal space between adjacent teeth. Under normal physiological conditions, this change typically manifests as a decrease in the interproximal space, while an increase in the space can easily lead to food impaction in tightly packed areas. The width of the interproximal space of natural teeth in a non-occlusal state is approximately 3–21 μm. Given this scale and the fragility of its physiological structure, it is difficult to directly obtain complete morphological and positional information within the mouth, and it is also difficult to observe the dynamic changes in the interproximal space during chewing in real time.

[0046] Intraoral 3D scanning technology is the premise and foundation of digital diagnosis and treatment. Based on the principles of optical triangulation, structured light and confocal microscopy, it acquires high-density point cloud data of the upper and lower jaw dentition, and generates a high-precision 3D surface model (usually stored in STL format) through triangulation and mesh reconstruction.

[0047] Based on the occlusal positional relationship captured during the scanning process (i.e., the maximum intercuspal position trained in step one), rigid registration is performed on the maxillary and mandibular models to accurately reproduce the patient's actual occlusal state. For each vertex of the maxillary occlusal surface, the software calculates the shortest Euclidean distance to the corresponding area surface of the mandibular model and segments the distance value range according to a set threshold. Positive values ​​represent gap size, values ​​close to zero represent contact, and negative values ​​represent penetration. Based on this principle, the intraoral scanning system uses a non-linear or linear color table to visually encode the spatial distances of the maxillary and mandibular models under occlusal conditions. In oral biomechanics, tight contact (small gap) in a certain area of ​​the maxillary and mandibular occlusal surfaces means that this area will preferentially contact during occlusion and is prone to becoming a stress concentration area.

[0048] Based on the anatomical features of teeth, the bottom of the incisal abduction gap or the occlusal abduction gap constitutes the top boundary of the proximal junction, which can be obtained through intraoral scanning technology. This invention uses this region as the research object, acquiring a three-dimensional digital model of the target tooth through intraoral scanning, drawing the top boundary of the proximal junction, and quantitatively analyzing the deviation of this boundary in three-dimensional space before and after occlusion (i.e., distance change). This allows for the identification and characterization of the gap change characteristics of the proximal junction experiencing food impaction during functional movement, including its range, location, and direction. This method provides a reliable technical basis for the objective diagnosis of tightly packed food impaction.

[0049] In this embodiment of the invention, three-dimensional intraoral scan data of the patient is acquired. The occlusal contact distribution of teeth is located based on the color bands on the digital model, and the force distribution on the tooth surface during mastication is inferred to pinpoint the adjustment site. The buccal best-fit registration technique is used to analyze the changes in the interproximal space of teeth before and after occlusion, thereby aiding in the diagnosis of close-proximal food impaction. After treatment, the efficacy can be verified based on the distribution changes of the color bands and the quantitative data of the interproximal space of the target tooth. Specific operating steps are as follows: Figure 2As shown.

[0050] Specifically, the Teeteser bite force measuring instrument was used to train and measure the maximum intercuspal bite force of the subjects, and the bite force data was obtained.

[0051] The Teetester bite force measuring instrument consists of three parts: a flexible sensor measuring plate, a data acquisition host, and bite force analysis software. Based on flexible array pressure sensing technology, this instrument can detect the dynamic distribution of pressure on the occlusal contact surfaces of the dental arch.

[0052] The flexible sensor force measuring plate is 0.1mm thick and is a flexible printed circuit board (PET material) with a force-sensitive nano-functional layer coated on its surface. This sensor conforms to the natural curvature of the dental arch, with a minimum unit sensing area of ​​1mm × 1mm, enabling spatial sampling of the pressure distribution across the entire occlusal contact surface. The data acquisition host achieves human-computer interaction through control buttons and status indicator lights. The occlusal force analysis software manages patient records, records and replays the process, and can extract parameters such as pressure values ​​at each contact point, force-time curve characteristics, and occlusal contact area, providing visualized quantitative evidence for assessing the subject's occlusal status. Figure 3 As shown.

[0053] By using the Teetester bite force measuring instrument to guide bite training, subjects can establish a stable and reliable maximum intercuspal occlusion, ensuring that they can repeat the bite with consistent force and jaw position when the bite contact status is collected in subsequent intraoral scans, thereby maximizing the consistency and repeatability of bite data collection.

[0054] Specifically, the buccal occlusion of the target tooth in the subject's mouth was scanned in the three units of the upper and lower jaws and the largest cusp intersection.

[0055] Previous studies have shown that fewer units in intraoral 3D scanning data result in higher scanning accuracy. To ensure complete scanning data of teeth before and after the impaction area, this embodiment of the invention ultimately decided to scan three units of data, including the impacted tooth. The intraoral teeth are divided into four quadrants. Intraoral 3D scanning data of a patient with impaction of the right maxillary first molar and right maxillary second molar (16, 17) at the buccal occlusion position with the largest cusp intercuspal position is shown below. Figure 4 As shown.

[0056] Specifically, the distribution of tooth occlusal contact is directly located based on the color bands in the gaps.

[0057] Doctors can use a pre-oral scanning system to quantify and grade the occlusal gaps between the upper and lower jaws by defining custom color band ranges (e.g., a gap of 0 mm is mapped to red, indicating close contact between the occlusal surfaces; 0.15-0.30 mm is mapped to a warm color, indicating a moderate gap; and 0.30 mm is mapped to green, indicating a significant gap), thereby inferring the degree of stress in different areas and analyzing poor stress on the occlusal surfaces.

[0058] like Figures 5-8 As shown, Figure 5 Color band diagram of the occlusal space of a patient with impaction between the right mandibular first molar and the right mandibular second molar (46, 47) provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the occlusal contact of impacted cusps 46 and 47 provided in an embodiment of the present invention. The occlusal point of 47 is concentrated at the distal lingual cusp and marginal ridge, with a component force pushing 47 distally; there is occlusal contact between the distal marginal ridge of 46 and the mesial marginal ridge of 47, and there may be filling cusps in the opposing occlusion. Figure 7 The color band diagrams for the 16 and 17 interlocking gaps provided in the embodiments of the present invention. Figure 8 This is a schematic diagram of buccal occlusion when the cusps are in the maximum intercuspal position, as provided in an embodiment of the present invention.

[0059] Specifically, three-dimensional tooth segmentation is performed based on the bite force data and static three-dimensional intraoral scan data, including: The acquired static 3D oral scan data is imported into the processor in point cloud mode for tooth segmentation processing. In this process, representative contour sampling points are selected from the dental surface using the farthest point sampling method, and the contour sampling points are sampled using a boundary awareness algorithm to obtain the final sampling points. The final sampling points are fed into the segmentation network for analysis to obtain the three-dimensional tooth segmentation results and the boundaries of adjacent regions.

[0060] Specifically, by applying cross-modal data quantitative analysis methods, automated identification of contact characteristics in adjacent areas can be achieved from oral scan data: like Figure 19 As shown, tooth segmentation includes: The acquired STL data is imported into the VS Code processor in point cloud mode for tooth segmentation. First, representative sampling points are selected from the tooth list surface using Farthest Point Sampling (FPS) to capture the approximate range and outline of each tooth. Then, Boundary Aware Point Sampling (BAPS) is used to improve the precision of tooth segmentation. Finally, the sampling points are fed into the Tooth Group Network for analysis to achieve single-tooth instance segmentation.

[0061] Specifically, buccal best-fit registration is performed based on the three-dimensional occlusal contact positioning data and the buccal dynamic intraoral scan data, including: The iterative nearest-point algorithm is used for coarse registration of the cheek side fitting; A point-to-plane ICP algorithm was used for precise buccal fitting and registration. Weighted registration was then used to optimize the buccal region, resulting in the final registration result.

[0062] Specifically, the objective function for registration is: ; in, As the source point; Indicate the corresponding point for the target; Normal to the target point; For weights.

[0063] Specifically, such as Figure 20 As shown, the digital registration process includes: The ICP (Iterative Closest Point) algorithm is used for coarse registration of buccal fitting: by selecting key feature points on the tooth surface, the distance between the source point cloud and the target point cloud is calculated to obtain rotation and translation transformation.

[0064] The Point-to-Plane ICP algorithm is used for cheek-side fitting and fine registration: accurate alignment is achieved by minimizing the normal distance error from the source point to the target surface.

[0065] Finally, weighted registration was used to ensure that the buccal region dominated the optimization: a higher weight was assigned to the stable anatomical region (buccal region), and a lower weight was assigned to adjacent regions to avoid alignment errors in adjacent regions affecting subsequent measurements. The registration objective function was: As the source point; Indicate the corresponding point for the target; Normal to the target point; For weights.

[0066] Specifically, by combining the coordinate system of the plugged area and the registration results, the Euclidean distance of discrete points is calculated to obtain the instantaneous separation of adjacent gaps, including: Based on the registration results, the adjacent areas of teeth are automatically extracted using KD-Tree nearest point search and normal constraint. Then, calculate the distance between the two adjacent teeth before and after occlusion using the coordinate system of the impaction zone. Using the formula Δd=d 咬合后 -d 咬合前 d 咬合前Before registration, draw lines in the contact area of ​​each tooth, convert the lines to points, and measure the distance between the points; d 咬合后 To determine the distance between the two teeth after registration, the line is converted into points, and the distance between these points is measured. Then, the relative separation change of adjacent interdental spaces at the moment of occlusion is quantified.

[0067] Specifically, Δd measurement is automated, such as... Figure 9 As shown: The KD-Tree algorithm is used to automatically extract the narrow bandwidth of the adjacent region of the target tooth. Normal constraints are used to ensure that the extracted region is not disturbed by other surfaces, avoiding human error. Then, the algorithm is used to calculate the point-to-point distance between the target tooth A and its adjacent tooth B. Finally, Δd represents the relative separation state of the two teeth after occlusion and before occlusion, calculated using the formula: Δd = d 咬合后 -d 咬合前 The calculation process for tooth proximity distance and open / closed state is as follows: Figure 21 As shown.

[0068] Specifically, the Δd value at each point represents the degree of separation of the local adjacent areas, and the maximum value of Δd is extracted to reflect the maximum degree of separation of the adjacent areas, which is used as the basis for judging the food impaction of close proximity.

[0069] Specifically, the final analysis and evaluation of Δd: For the final measurement of Δd, Δd at each point represents the relative separation change between two adjacent teeth at that location. Key indicators are extracted through statistical calculations to assess the dynamic changes in the adjacent area: the maximum value of Δd is found, representing the maximum local separation degree in the adjacent area, and thus ultimately determining whether it is a case of tightly adjacent food impaction.

[0070] This invention, for the first time, combines intraoral three-dimensional scanning technology with dynamic registration analysis of occlusal contact, enabling quantitative diagnosis of food impaction in closely spaced adjacent teeth. By acquiring three-dimensional models of the dentition in both static and occlusal states, and using an iterative nearest-point algorithm for optimal buccal registration, the dynamic displacement (including range, position, and instantaneous separation) of the target tooth's adjacent area during occlusion is quantitatively calculated. This overcomes the limitations of traditional qualitative methods such as dental flossing and fills the gap in this field lacking objective quantitative indicators.

[0071] This invention constructs a multimodal data analysis method based on "gap-occlusion-force". On one hand, the gap is quantified and graded using a pre-existing intraoral scanning system, and the degree of force in different regions is inferred based on a preset color mapping relationship (e.g., a gap of 0 is mapped to red, indicating close contact between the occlusal surfaces; 30-150μm is mapped to warm colors, indicating a moderate gap; 200-300μm is mapped to green, indicating a significant gap), thereby assisting in locating the occlusion site. On the other hand, the impaction mechanism is revealed from a biomechanical perspective by accurately calculating the changes in adjacent gaps under functional states.

[0072] This invention provides an integrated digital solution for the clinical diagnosis and treatment of food impaction. It not only assists in diagnosis and classification and guides treatment selection, but also enables objective evaluation of treatment efficacy through model registration and gap comparison after treatment, promoting the standardization, precision, and automation of the diagnostic and treatment process. Its operation is compatible with existing digital dental equipment, making it easy to apply in clinical work and possessing significant promotional value and application prospects.

[0073] In a specific embodiment of the present invention, taking the intraoral three-dimensional scan data of a subject with close food impaction between the left upper first molar and the left upper second molar (26, 27) as an example, an automated quantitative intelligent analysis method for close food impaction specifically includes the following steps: Establish a coordinate system: Based on the 26th tooth in the mesial region of the impaction area, the line connecting the midpoints of the mesial and distal marginal ridges, and the center point of the 26th tooth, establish a coordinate system (X-axis represents the mesial-distal direction, Y-axis represents the buccal-lingual / lingobuccal direction, Z-axis represents the gingival direction).

[0074] Extract the boundary line of 26.27 and separate 26.27 (i.e., the three-dimensional data of 26.27 before occlusion).

[0075] Draw a curve along the adjacent boundary of the distal and mesial regions of teeth 26 and 27 in the impacted area, respectively. While ensuring that the data is a true and intuitive scan, draw the curve as close as possible to the top boundary of the adjacent region. Then copy "26" and "27" to obtain "Copy 26" and "Copy 27".

[0076] Best-fit buccal data acquisition: Using buccal occlusal data of 26 and 27 as the common area, best-fit registration was performed: When registering "26" and "27", the buccal occlusal data were fixed and the pre-occlusal data was floated ("copy 26", "copy 27"). After registration, "26" and "27" are the pre-occlusal data, and "copy 26" and "copy 27" become the post-occlusal data.

[0077] Analyze the changes in the adjacent areas of impacted teeth before and after occlusion: Create a certain number of points on the four curves of teeth "26", "27" and "copy 26", "copy 27" according to the distance. Calculate the coordinates and distances of the points on the curves of teeth "26" and "27" before occlusion and on the curves of teeth "copy 26" and "copy 27" after occlusion in the established coordinate system. Then calculate the distance difference before and after occlusion and the distance changes on each coordinate axis. Finally, the change Δd represents the change in the adjacent areas of teeth before and after occlusion.

[0078] The specific operation process is as follows: Figures 10-12 As shown.

[0079] Specifically, the type of food impaction in patients is differentiated and the treatment efficacy is evaluated based on the characteristics of the proximal area during target tooth occlusal contact: Diagnosis: If the static interproximal gap (measurement methods include but are not limited to using occlusal paper of different thicknesses, metal film, feeler gauge, vernier caliper, etc. to enter the contact area of ​​adjacent teeth) is ≤60μm and Δd>30, it is judged as food impaction with tight interproximal gap; Treatment guidelines: Locate the adjustment site based on the color band diagram of the interstitial space (e.g., remove the contact of the edge ridge of the adjacent interstitial space, open the food overflow channel); Evaluation of efficacy: Repeat the above steps after treatment and compare the changes in Δd before and after treatment. Δd < 30 indicates a cure, and Δd 30~50 indicates an effectiveness. Also compare the distribution of the interstitial color band (disappearance of adverse stress sites) and the patient's VAS score (reduction ≥ 2 points).

[0080] This invention utilizes intraoral scanning to acquire dentition and occlusal data. Occlusal contact distribution is located using a color-coded interproximal space map, and the degree of force is determined based on color, aiding in the identification of adjustment points. Combined with a cross-modal data model, contact characteristics of the proximal area are automatically identified. Digital registration technology is employed to quantitatively analyze the three-dimensional changes in the proximal area of ​​the target tooth before and after occlusion, calculating the instantaneous separation amount Δd of the interproximal space under functional conditions, thereby achieving auxiliary diagnosis of food impaction in closely spaced proximal teeth. After treatment, the efficacy can be evaluated using the color-coded interproximal space distribution and quantitative data, promoting the visualization, standardization, precision, and intelligence of the diagnosis and treatment of food impaction in proximal teeth.

[0081] In one specific embodiment of the present invention, an automated quantitative intelligent analysis method for closely packed food impaction includes: Based on the patient's dental occlusion physiological state, occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data are collected simultaneously. Based on the bite force data and static three-dimensional oral scan data, three-dimensional tooth segmentation is performed to obtain the boundary of the adjacent area and three-dimensional bite contact positioning data. Establish the crater region coordinate system based on the adjacent region boundary; Based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data, buccal best-fit registration is performed. By combining the coordinate system of the plugged area and the registration results, the Euclidean distance of discrete points is calculated to obtain the instantaneous separation amount of adjacent gaps; The maxillofacial space data is converted into a maxillofacial color band mapping map; The patient's food impaction type was differentiated and the treatment efficacy was evaluated by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

[0082] The embodiments of the present invention can directly obtain key data of the adjacent surface contact area, and have the following beneficial effects: 1. Significantly improves the speed of clinical diagnosis and treatment, avoiding unreliable data due to human factors. It directly and quantitatively measures the strength, area, and other relevant indicators of the interproximal junctions of all teeth in the mouth, establishing quantitative and visual standards for direct or indirect restoration of these junctions. This reduces food impaction or periodontal tissue damage after treatment, improving diagnostic and treatment efficiency and quality.

[0083] 2. By detecting the dynamic chewing trend of the adjacent area of ​​impacted teeth in patients with close-proximal food impaction, and comparing the trend of related indicators in the adjacent area of ​​non-impacted teeth, the dynamic pathogenesis characteristics of close-proximal food impaction can be clarified. Data can be saved and compared, which is convenient for clinical research.

[0084] 3. Quantitative and personalized adjustment of pressure for close-proximal food impaction: Determine the clinical pressure adjustment amount based on the strength comparison of the adjacent areas of impacted and non-impacted teeth, improve or restore the adjacent strength of impacted teeth, reduce or avoid the occurrence of food impaction, and form a systematic and comprehensive pressure adjustment treatment plan for close-proximal food impaction.

[0085] In one specific embodiment of the present invention, The patient complained of food impaction in the upper and lower right molars for two years, with an increased frequency of occurrence in the past six months. Food would get stuck every time he ate fibrous food, and it was difficult to remove with dental floss or other tools, causing great pain. He requested treatment.

[0086] Treatment process: (1) Medical history taking and clinical examination: According to the patient's condition description, it is a grade III food impaction: meat, fibrous food, once a time; other foods, occasionally; Food impaction was visible between 1617, 1718, and 4647, along with red and swollen gums.

[0087] Traditional method: Using feeler gauges to measure the adjacent gaps of 1617, it is 30μm; the adjacent gap of 1718 is 50μm; and the adjacent gap of 4647 is 40μm. This indicates that the adjacent gaps of 1617, 1718, and 4647 are in normal contact when at rest (adjacent gaps > 60μm are considered to be lost).

[0088] (2) The Teeteser tooth bite force measuring instrument was used to train and measure the maximum intercuspal bite force of the subjects.

[0089] (3) When the upper and lower jaws of the three units anterior and posterior to the impacted tooth are in the intercuspal position of the largest cusp, buccal occlusion is required (e.g.) Figure 13 (As shown).

[0090] (4) Based on the color band of the gap, the distribution of occlusal contact of the teeth was directly located, and the adverse force on the occlusal surface was analyzed: the occlusal contact of the upper and lower teeth 67 on the right side was concentrated near the adjacent marginal ridges of the two teeth. The food overflow channel was observed to be closed during buccal occlusion, suggesting that there may be the formation of filling cusps. There was local occlusal contact between the distal marginal ridge of tooth 47 and the mesial of tooth 18. During occlusion, a distal thrust was generated on tooth 18, resulting in a tooth separation effect.

[0091] (5) Importing 3D data software for automatic identification, analysis, and measurement of contact points in the adjacent area: First, the patient's 3D dental arch model is segmented into individual teeth, and then the target tooth pair for impaction is identified; subsequently, the registration stage is entered, with coarse registration performed sequentially to achieve overall alignment, followed by fine registration of individual teeth, and finally, weighted registration is used to improve the precision of the registration area. In the measurement stage, quantitative analysis is performed on the target tooth pair, including contact area extraction, surface gap calculation, statistical analysis, etc., to obtain the change Δd of the adjacent area of ​​the teeth before and after occlusion, such as... Figure 14 As shown.

[0092] (6) Based on Δd and clinical symptoms, the patient was diagnosed with close-proximal food impaction at 1617, 1718, and 4647. Treatment involved adjusting the occlusal margin, precisely locating the adjustment site to remove unfavorable occlusal contact and simultaneously establish favorable occlusal contact in the center and mesial region of the occlusal surface: removing the contact of the marginal ridge of the interproximal space between the maxilla and mandible 67 to open the food overflow channel, while restoring the main functional area of ​​occlusal contact to the center of the occlusal surface. The occlusal contact of the distal marginal ridge of 47 to 18 was removed to eliminate the tendency for tooth separation towards 18.

[0093] (7) Comparison of patients' subjective evaluations before and after treatment: After treatment, patients felt a significant relief in the degree of impaction in the right posterior teeth, and their VAS scores decreased. Figure 15 As shown.

[0094] (8) Oral scanning and automated measurement of Δd after adjustment, scientifically and objectively verifying the therapeutic effect, such as Figures 16-18 As shown.

[0095] This invention combines intraoral three-dimensional scanning technology with automated analysis of dynamic registration of occlusal contacts, achieving intelligent, standardized, and visualized diagnosis and treatment of closely spaced food impaction. This provides objective and repeatable evaluation criteria for the clinical diagnosis and treatment of this type of disease, improving diagnostic efficiency and quality.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0097] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An automated quantitative intelligent analysis method for closely packed food impaction, characterized in that, include: The data acquisition module is used to simultaneously acquire occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data based on the patient's dental occlusion physiological state. The tooth segmentation module is used to perform three-dimensional tooth segmentation based on the occlusal force data and static three-dimensional oral scan data to obtain the boundary of the adjacent area and three-dimensional occlusal contact positioning data. A coordinate system establishment module is used to establish a clogging region coordinate system based on the adjacent region boundary. The fitting and registration module is used to perform optimal buccal fitting and registration based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data. The calculation module is used to calculate the Euclidean distance of discrete points by combining the coordinate system of the plugged area and the registration results, and to obtain the instantaneous separation amount of adjacent gaps; The conversion module is used to convert the maxillofacial space data into a maxillofacial color band mapping map; The analysis and evaluation module is used to differentiate the type of food impaction in patients and evaluate the treatment efficacy by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

2. The automated quantitative intelligent analysis system for closely packed food impaction according to claim 1, characterized in that, Three-dimensional tooth segmentation is performed based on the bite force data and static three-dimensional intraoral scan data, including: The acquired static 3D oral scan data is imported into the processor in point cloud mode for tooth segmentation processing. In this process, representative contour sampling points are selected from the dental surface using the farthest point sampling method, and the contour sampling points are sampled using a boundary awareness algorithm to obtain the final sampling points. The final sampling points are fed into the segmentation network for analysis to obtain the three-dimensional tooth segmentation results and the boundaries of adjacent regions.

3. The automated quantitative intelligent analysis system for closely packed food impaction according to claim 1, characterized in that, Based on the three-dimensional occlusal contact positioning data and the buccal dynamic intraoral scan data, buccal best-fit registration is performed, including: The iterative nearest-point algorithm is used for coarse registration of the cheek side fitting; A point-to-plane ICP algorithm was used for precise buccal fitting and registration. Weighted registration was then used to optimize the buccal region, resulting in the final registration result.

4. The automated quantitative intelligent analysis system for closely packed food impaction according to claim 1, characterized in that, By combining the coordinate system of the plugged area and the registration results, the Euclidean distance of discrete points is calculated to obtain the instantaneous separation of adjacent gaps, including: Based on the registration results, the adjacent areas of teeth are automatically extracted using KD-Tree nearest point search and normal constraint. Then, calculate the distance between the two adjacent teeth before and after occlusion using the coordinate system of the impaction zone. Using the formula Δd=d 咬合后 -d 咬合前 Quantify the relative separation change of adjacent interpupillary spaces at the moment of occlusion.

5. The automated quantitative intelligent analysis system for closely packed food impaction according to claim 4, characterized in that, The degree of separation of local adjacent areas is represented by Δd at each point. The maximum value of Δd is extracted to reflect the maximum degree of separation of adjacent areas, which is used as the basis for judging food impaction with close proximity.

6. The automated quantitative intelligent analysis system for closely packed food impaction according to claim 3, characterized in that, Weighted registration was used to optimize the buccal region, resulting in the final registration result, including: ； in, As the source point; Indicate the corresponding point for the target; Normal to the target point; For weights.

7. An automated quantitative intelligent analysis method for adjacent and tightly packed food impaction, employing the automated quantitative intelligent analysis system for adjacent and tightly packed food impaction as described in any one of claims 1-6, characterized in that, include: Based on the patient's dental occlusion physiological state, occlusal force data, static three-dimensional oral scan data, buccal dynamic oral scan data, and maxillofacial interfacial data are collected simultaneously. Based on the bite force data and static three-dimensional oral scan data, three-dimensional tooth segmentation is performed to obtain the boundary of the adjacent area and three-dimensional bite contact positioning data. Establish the crater region coordinate system based on the adjacent region boundary; Based on the three-dimensional occlusal contact positioning data and the buccal dynamic oral scan data, buccal best-fit registration is performed. By combining the coordinate system of the plugged area and the registration results, the Euclidean distance of discrete points is calculated to obtain the instantaneous separation amount of adjacent gaps; The maxillofacial space data is converted into a maxillofacial color band mapping map; The patient's food impaction type was differentiated and the treatment efficacy was evaluated by combining the instantaneous separation amount of the adjacent interspace and the maxillofacial color band mapping map.

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