Gum data analysis system based on medical big data

Abstract

The invention discloses a gum data analysis system based on medical big data, relates to the technical field of gum analysis, and solves the problem that whether gum arrangement is normal or not is not determined based on the arrangement state of all teeth. Monitoring the distance between the middle points of adjacent teeth of the upper and lower gums; the system is significant to orthodontic re-consultation patients, and doctors can quickly master tooth movement deviation in the orthodontic process and finely adjust parameters of the orthodontic appliance according to arrangement signals generated by the system, so that the orthodontic effect is ensured to be achieved on time; for oral cavity restoration patients, a new restoration body is prevented from influencing peripheral tooth arrangement, and the mechanical balance of an oral cavity chewing system is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of gum analysis, and in particular to a gum data analysis system based on medical big data. Background Art

[0002] Traditional periapical films can clearly show the condition of a single tooth and its root in the gums, such as the length and shape of the root, and the presence or absence of periapical lesions; panoramic films can show the entire dentition and bilateral gums, and are used to check the eruption of wisdom teeth, whether there are fractures in the jaws, and the degree of alveolar bone absorption of multiple teeth. For example, in patients with periodontitis, panoramic films can be used to observe the degree of alveolar bone absorption along the dentition in the horizontal or vertical direction, helping doctors determine the stage of the disease.

[0003] The application with publication number CN111083922A discloses a dental image analysis method for orthodontic diagnosis and a device using the same. The method comprises the following steps: obtaining a dental image of a patient; and detecting at least a portion of a plurality of measurement points (landmarks) for orthodontic diagnosis from the dental image using a measurement point detection module, wherein the measurement points are anatomical reference points indicating the relative position of at least one of facial bones, teeth and facial contours required for orthodontic diagnosis, and the measurement point detection module comprises a mechanical training module based on an artificial neural network.

[0004] For the confirmed dental images, the relevant features of the teeth in the dental images are analyzed, and the corresponding teeth are verified and checked with the corresponding tooth templates. Based on the verification results, it is assessed whether the teeth are arranged normally. However, in the actual assessment process, it is necessary to determine whether the arrangement of the gums is normal based on the arrangement status of all teeth, and to determine the parameter representation between the center point distances between the teeth to determine the specific arrangement results. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a gum data analysis system based on medical big data, which solves the problem of determining whether the gum arrangement is normal based on the arrangement status of all teeth.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dental data analysis system based on medical big data, comprising:

[0007] The CT image acquisition end acquires and confirms the dental CT images involved in the dental detection, and transmits the confirmed dental CT images to the tooth area determination end;

[0008] The tooth region determination end selects the tooth region from the dental CT image based on the CT values ​​associated with different points in the dental CT image, then confirms the individual tooth from the performance characteristics of the selected tooth region, and then confirms the pulp region from the edge contour of the individual tooth. The specific method is as follows:

[0009] Based on the confirmed dental CT image, the CT values ​​associated with different points in the dental CT image are confirmed and calibrated as H i , where i represents different points in the dental CT image;

[0010] The CT meets: H i ∈[1500Hu, 3000Hu] points are calibrated as tooth points, where Hu is a numerical unit. Based on the calibrated groups of tooth points, the image areas covered by the groups of tooth points are confirmed. Based on the overall edge contours of such image areas, the areas within the overall edge contours in the dental CT images are calibrated as tooth areas.

[0011] Locking H from the tooth area i ∈[30Hu, 100Hu], the locked points are marked as pulp points, and the areas covered by several groups of pulp points are marked as pulp areas;

[0012] The pulp areas associated with different tooth areas were calibrated in turn using the same processing method;

[0013] The tooth calibration processing end, based on the pulp area confirmed in the corresponding tooth area, confirms the center points of the tooth area and the internal pulp area, confirms the distance characteristics between the two sets of center points, and calibrates the single tooth associated with such tooth area as a normal tooth or a missing tooth based on the numerical expression of the distance characteristics. The specific method is as follows:

[0014] Based on the determined tooth region, the edge contour of the region is locked, and an edge contour line belonging to the tooth region is generated, the edge contour line is placed in a set of two-dimensional coordinate systems, and the two-dimensional coordinates of different contour points in the edge contour line are confirmed from the two-dimensional coordinate systems, and then a plurality of groups of two-dimensional coordinates associated with different contour points are averaged to determine the mean coordinates, and then point calibration is performed in the tooth region based on the location of the mean coordinates to confirm the center point A belonging to the tooth region;

[0015] Then, from the pulp area confirmed in the tooth area, the edge contour of the pulp area is preliminarily confirmed, and the edge contour line of the pulp area is generated, and then the edge contour line is placed in the two-dimensional coordinate system in the same way, and the center point B of the pulp area is simultaneously confirmed in the two-dimensional coordinate system;

[0016] Based on the specific positions of the center point A and the center point B, the straight-line distance L between the two sets of center points A and B is determined, and it is identified whether the straight-line distance L satisfies: L≤Y1, where Y1 is a preset value. If so, the teeth associated with this tooth area are marked as normal teeth. If not, the teeth associated with this tooth area are marked as missing teeth.

[0017] The feature midpoint confirmation end re-analyzes the single tooth calibrated as a missing tooth, and locks the symmetry midline based on the overall edge contour of the single tooth and the center point of the internal pulp area. Then, based on the determined symmetry midline, the feature center point of the missing tooth is locked based on the symmetry midline. The specific method is as follows:

[0018] The center point of the pulp area of ​​the missing tooth is marked as the main point, and the feature point closest to the main point is locked in the outer area of ​​the missing tooth, and the feature point is used as the secondary point of the main point;

[0019] Connect the main point and the secondary point, and extend the line. The extended line is used as the symmetrical midline of the missing tooth.

[0020] Based on the determined symmetry midline, different tooth regions located on both sides of the symmetry midline are identified from the tooth image belonging to the missing tooth, and based on the external contours of the tooth regions on both sides, the tooth region on the side with a larger area expression value is determined, and the tooth region is used as the region to be mirrored, and based on the confirmed symmetry midline, the region to be mirrored is mirrored to confirm the mirrored region, and the region after the region to be mirrored and the mirrored region are merged is used as the main feature region, and based on the overall edge contour of the main feature region, the feature center point of the main feature region is locked, and the locked feature center point is used as the feature center point of the missing tooth;

[0021] The missing teeth after the feature center point calibration is completed are transmitted to the tooth feature analysis and processing end;

[0022] The tooth feature analysis and processing end performs arrangement analysis on the missing teeth after the feature center point calibration is completed and the normal teeth after the center point calibration is completed. The upper or lower gums are preferentially identified from the dental CT images, and the center point spacing of the corresponding teeth in the upper or lower gums is numerically analyzed to determine the analysis results and display them. The specific method is as follows:

[0023] The upper or lower gum is locked from the dental CT image. The upper or lower gum has been calibrated in advance. The upper gum is the area where the teeth are arranged above, and the lower gum is the area where the teeth are arranged below.

[0024] Confirm the characteristics of the internal teeth located in the upper gums, including normal teeth or missing teeth, and mark the characteristic center points of the missing teeth and the center points of the normal teeth as the midpoints of the internal teeth: identify the straight-line distances between the inner midpoints of the adjacent internal teeth in the corresponding gums, and then mark the confirmed several straight-line distances as L q , where q represents different straight-line distances, and the confirmed L q Perform mean processing to confirm the characteristic parameter L1 of the upper gum;

[0025] Treat the inner teeth of the lower gum in the same way as the upper gum, and confirm the characteristic parameter L2 of the lower gum;

[0026] Identify whether L1 and L2 satisfy: |L1-L2|>Y2, where Y2 is a preset value. If so, generate and display an abnormal arrangement signal; if not, generate a normal arrangement signal.

[0027] The present invention provides a dental data analysis system based on medical big data. Compared with the prior art, it has the following beneficial effects:

[0028] The tooth calibration processing end uses a unique algorithm to calibrate the tooth status based on the distance characteristics between the center point of the tooth area and the pulp area; compared with traditional manual judgment based on experience, the system can quickly process a large amount of dental CT image data and quickly check for missing teeth. Once the missing tooth causes the center point to deviate, the system will immediately and accurately identify it, provide key guidance for the subsequent oral restoration plan, accelerate the diagnosis and treatment process, and allow patients to receive appropriate restoration treatment more quickly;

[0029] The tooth feature analysis and processing end focuses on the tooth arrangement in the gums and monitors the midpoint distance between adjacent teeth in the upper and lower gums. It can detect abnormal tooth arrangement in a timely manner, which is of great significance for patients returning for orthodontic visits. Doctors can quickly grasp the tooth movement deviation in the orthodontic process based on the arrangement signals generated by the system, and fine-tune the parameters of the orthodontic appliance to ensure that the orthodontic effect is achieved on time. For patients with oral rehabilitation, it can prevent the new restoration from affecting the surrounding tooth arrangement and maintain the mechanical balance of the oral chewing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] First embodiment

[0033] See also Figure 1 , the present application provides a dental data analysis system based on medical big data, including a CT image acquisition end, a tooth region determination end, a tooth calibration processing end, a feature midpoint confirmation end and a tooth feature analysis processing end, wherein the CT image acquisition end is electrically connected to an input node of the tooth region determination end, the tooth region determination end is electrically connected to an input node of the tooth calibration processing end, and the tooth calibration processing end is electrically connected to a feature midpoint confirmation end or a tooth feature analysis processing end input node, respectively, and the feature midpoint confirmation end is electrically connected to an input node of the tooth feature analysis processing end;

[0034] The CT image acquisition end acquires and confirms the dental CT images involved in the dental detection, and transmits the confirmed dental CT images to the tooth area determination end, wherein the dental CT images are detected by the corresponding dental CT detection equipment, and the specific detection process and specific detection method are instructed by the relevant operator to the person to be detected to perform image detection processing, and the dental CT images are determined based on the corresponding detection processing process;

[0035] The tooth region determination end selects the tooth region from the dental CT image based on the CT values ​​associated with different points in the dental CT image, and then confirms the single tooth from the performance characteristics of the selected tooth region, and then confirms the pulp region from the edge contour of the single tooth, wherein the specific method of confirmation is:

[0036] Based on the confirmed dental CT image, the CT values ​​associated with different points in the dental CT image are confirmed and calibrated as H i , where i represents different points in the dental CT image;

[0037] The CT meets: H i∈[1500Hu, 3000Hu] is calibrated as a tooth point, where Hu is a numerical unit. Based on the calibrated groups of tooth points, the image areas covered by the groups of tooth points are confirmed. Based on the overall edge contour of such image areas, the area inside the overall edge contour is calibrated as the tooth area from the dental CT image. Specifically, because each tooth is separated, there are several determined tooth areas. Each different tooth area corresponds to a group of teeth, and there is a corresponding edge contour in the tooth area. The specific position of the corresponding tooth can be locked based on the corresponding edge contour. The relevant points of the middle points inside such tooth areas do not belong to the [1500Hu, 3000Hu] CT value range, because the central area belongs to the pulp area, and the outer area of ​​the pulp area belongs to the tooth area. The corresponding points will generate an outer area surrounding the pulp area. Therefore, based on the overall edge contour of the corresponding outer area, the tooth area belonging to this image can be locked;

[0038] Locking H from the tooth area i ∈[30Hu, 100Hu], the locked points are marked as pulp points, and the areas covered by several groups of pulp points are marked as pulp areas;

[0039] The pulp regions associated with different tooth regions are calibrated in turn using the same processing method. Specifically, in the determined tooth region, the pulp is located at the center of the tooth region.

[0040] Among them, the tooth calibration processing end, based on the pulp area confirmed in the corresponding tooth area, confirms the center points of the tooth area and the internal pulp area, confirms the distance characteristics between the two groups of center points, and based on the numerical expression of the distance characteristics, calibrates the single tooth associated with such tooth area as a normal tooth or a missing tooth. Specifically, if the distance between the center points associated with the corresponding teeth is relatively close, then there is no problem with the characteristics between the center points, but when the corresponding teeth are missing, it will cause the corresponding center point deviation to be large, so the corresponding tooth is a missing tooth;

[0041] Among them, the specific method of calibrating a single tooth is:

[0042] Based on the determined tooth region, the edge contour of the region is locked, and an edge contour line belonging to the tooth region is generated, the edge contour line is placed in a set of two-dimensional coordinate systems, and the two-dimensional coordinates of different contour points in the edge contour line are confirmed from the two-dimensional coordinate systems, and then a plurality of groups of two-dimensional coordinates associated with different contour points are averaged to determine the mean coordinates, and then point calibration is performed in the tooth region based on the location of the mean coordinates to confirm the center point A belonging to the tooth region;

[0043] Then, from the pulp area confirmed in the tooth area, the edge contour of the pulp area is preliminarily confirmed, and the edge contour line of the pulp area is generated, and then the edge contour line is placed in the two-dimensional coordinate system in the same way, and the center point B of the pulp area is simultaneously confirmed in the two-dimensional coordinate system;

[0044] Based on the specific positions of the center point A and the center point B, the straight-line distance L between the two sets of center points A and B is confirmed, and it is identified whether the straight-line distance L satisfies: L≤Y1, where Y1 is a preset value, and its specific value is determined by the operator based on experience. If it satisfies, the teeth associated with this tooth area are marked as normal teeth, otherwise, the teeth associated with this tooth area are marked as missing teeth;

[0045] Specifically, in the actual confirmation process, when the center points of the tooth area and the pulp area are greatly different, it means that the corresponding tooth is missing. It is a missing tooth, and its pulp area is located at the center of the tooth area. Generally, it will not be reduced due to damage or loss of the tooth. When the corresponding tooth is missing, it will cause the center point of the tooth to shift, resulting in a large difference between the center point of the tooth area and the center point of the pulp area. Under normal circumstances, the pulp area is located at the center of the tooth area, and the center point of the tooth area and the center point of the pulp area are basically close, so the straight-line distance between the two is also close.

[0046] Among them, the feature midpoint confirmation end re-analyzes the single tooth calibrated as a missing tooth, locks the symmetry midline based on the overall edge contour of the single tooth and the center point of the internal pulp area, and then locks the feature center point of the missing tooth based on the determined symmetry midline. The specific locking method is:

[0047] The center point of the pulp area of ​​the missing tooth is calibrated as the main point, and the feature point closest to the main point is locked in the outer area of ​​the missing tooth (that is, the enamel area, the CT value of the enamel area is between 2500-3000Hu), and this feature point is used as the secondary point of the main point;

[0048] Connect the main point and the secondary point, and extend the line. The extended line is used as the symmetrical midline of the missing tooth.

[0049] Based on the determined symmetry midline, different tooth regions on both sides of the symmetry midline are identified from the tooth image belonging to the missing tooth, and based on the external contours of the tooth regions on both sides, the tooth region on one side with a larger area expression value is determined, and this tooth region is used as the region to be mirrored, and based on the confirmed symmetry midline, the region to be mirrored is mirrored to confirm the mirrored region, and the region after the region to be mirrored and the mirrored region are merged is used as the main feature region, and based on the overall edge contour of the main feature region, the feature center point of the main feature region is locked, and the locked feature center point is used as the feature center point of the missing tooth. The feature center point confirmation method of the main feature region is the same as the confirmation method of the center point of the pulp region, and based on the corresponding overall edge contour and the associated two-dimensional coordinate system, the two-dimensional coordinates of the contour points on the contour are confirmed, and after locking the average coordinates of several two-dimensional coordinates, the center point is specifically confirmed;

[0050] The missing teeth after the feature center point calibration is transmitted to the tooth feature analysis and processing end.

[0051] Second embodiment

[0052] In the specific implementation process of this embodiment, the arrangement of internal teeth in the dental CT image is calibrated and analyzed to assess whether the upper and lower corresponding teeth are arranged up to standard;

[0053] Among them, the tooth feature analysis processing end performs arrangement analysis on the missing teeth after the feature center point calibration is completed and the normal teeth (that is, center point A) after the center point calibration is completed, and the upper or lower gums are preferentially identified from the dental CT images, and the center point spacing of the corresponding teeth in the upper or lower gums is numerically analyzed to determine the analysis results and display them. The specific method of performing the numerical analysis is as follows:

[0054] The upper or lower gums are locked from the dental CT images. When the images are taken, they have been calibrated in advance. The upper gums are the area where the teeth are arranged above, and the lower gums are the area where the teeth are arranged below.

[0055] Confirm the characteristics of the internal teeth located in the upper gums, including normal teeth or missing teeth, and mark the characteristic center points of the missing teeth and the center points of the normal teeth as the midpoints of the internal teeth: identify the straight-line distances between the inner midpoints of the adjacent internal teeth in the corresponding gums, and then mark the confirmed several straight-line distances as L q , where q represents different straight-line distances, and the confirmed L q Perform mean processing to confirm the characteristic parameter L1 of the upper gum;

[0056] Treat the inner teeth of the lower gum in the same way as the upper gum, and confirm the characteristic parameter L2 of the lower gum;

[0057] Identify whether L1 and L2 satisfy: |L1-L2|>Y2, where Y2 is a preset value, and its specific value is determined by the operator based on experience. If it is satisfied, an abnormal arrangement signal is generated and displayed; if it is not satisfied, a normal arrangement signal is generated;

[0058] Specifically, in the actual treatment process, the relative arrangement between adjacent teeth in the upper and lower gums should be in a relatively standard state. If it is not in a standard state, it means that the corresponding upper and lower arrangements of the teeth are abnormal. For such abnormal situations, timely confirmation and signal display are required to facilitate medical staff to take corresponding targeted measures.

[0059] Third embodiment

[0060] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0061] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The dental data analysis system based on medical big data is characterized by: include: The CT image acquisition end acquires and confirms the dental CT images involved in the dental detection, and transmits the confirmed dental CT images to the tooth area determination end; The tooth region determination end selects the tooth region from the dental CT image based on the CT values ​​associated with different points in the dental CT image, then confirms the individual tooth from the performance characteristics of the selected tooth region, and then confirms the pulp region from the edge contour of the individual tooth; The tooth calibration processing end, based on the pulp area confirmed in the corresponding tooth area, confirms the center points of the tooth area and the internal pulp area, confirms the distance characteristics between the two sets of center points, and calibrates the single tooth associated with such tooth area as a normal tooth or a missing tooth based on the numerical expression of the distance characteristics; The characteristic midpoint confirmation end re-analyzes the single tooth calibrated as a missing tooth, locks the symmetry midline based on the overall edge contour of the single tooth and the center point of the internal pulp area, and then locks the characteristic center point of the missing tooth based on the determined symmetry midline; The tooth feature analysis and processing end performs arrangement analysis on the missing teeth after the feature center point calibration and the normal teeth after the center point calibration. The upper or lower gums are preferentially identified from the dental CT images, and the center point distances of the corresponding teeth in the upper or lower gums are numerically analyzed to determine and display the analysis results.

2. The dental data analysis system based on medical big data according to claim 1 is characterized in that: The tooth region determining end screens the tooth region in the following specific manner: Based on the confirmed dental CT image, the CT values ​​associated with different points in the dental CT image are confirmed and calibrated as H i , where i represents different points in the dental CT image; The CT meets: H i Points ∈[1500Hu, 3000Hu] are calibrated as tooth points, where Hu is a numerical unit. Based on the calibrated groups of tooth points, the image areas covered by the groups of tooth points are confirmed. Based on the overall edge contours of such image areas, the areas inside the overall edge contours in the dental CT images are calibrated as tooth areas.

3. The dental data analysis system based on medical big data according to claim 2 is characterized in that: The tooth region determination end determines the dental pulp region in the following specific manner: Locking H from the tooth area i ∈[30Hu, 100Hu], the locked points are marked as pulp points, and the areas covered by several groups of pulp points are marked as pulp areas; The pulp areas associated with different tooth areas were calibrated in turn using the same processing method.

4. The dental data analysis system based on medical big data according to claim 2, characterized in that: The specific method of the tooth calibration processing end for calibrating a single tooth is: Based on the determined tooth region, the edge contour of the region is locked, and an edge contour line belonging to the tooth region is generated, the edge contour line is placed in a set of two-dimensional coordinate systems, and the two-dimensional coordinates of different contour points in the edge contour line are confirmed from the two-dimensional coordinate systems, and then a plurality of groups of two-dimensional coordinates associated with different contour points are averaged to determine the mean coordinates, and then point calibration is performed in the tooth region based on the location of the mean coordinates to confirm the center point A belonging to the tooth region; Then, from the pulp area confirmed in the tooth area, the edge contour of the pulp area is preliminarily confirmed, and the edge contour line of the pulp area is generated, and then the edge contour line is placed in the two-dimensional coordinate system in the same way, and the center point B of the pulp area is simultaneously confirmed in the two-dimensional coordinate system; Based on the specific positions of center point A and center point B, the straight-line distance L between the two sets of center points A and B is confirmed, and it is identified whether this straight-line distance L satisfies: L≤Y1, where Y1 is a preset value. If it satisfies, the teeth associated with this tooth area are calibrated as normal teeth.

5. The dental data analysis system based on medical big data according to claim 4 is characterized in that: If the straight-line distance L does not satisfy: L≤Y1, the teeth associated with this tooth region are marked as missing teeth.

6. The dental data analysis system based on medical big data according to claim 1, characterized in that: The specific method of locking the characteristic center point of the missing tooth at the characteristic midpoint confirmation end is: The center point of the pulp area of ​​the missing tooth is marked as the main point, and the feature point closest to the main point is locked in the outer area of ​​the missing tooth, and the feature point is used as the secondary point of the main point; Connect the main point and the secondary point, and extend the line. The extended line is used as the symmetrical midline of the missing tooth. Based on the determined symmetry midline, different tooth regions located on both sides of the symmetry midline are identified from the tooth image belonging to the missing tooth, and based on the external contours of the tooth regions on both sides, the tooth region on the side with a larger area expression value is determined, and the tooth region is used as the region to be mirrored, and based on the confirmed symmetry midline, the region to be mirrored is mirrored to confirm the mirrored region, and the region after the region to be mirrored and the mirrored region are merged is used as the main feature region, and based on the overall edge contour of the main feature region, the feature center point of the main feature region is locked, and the locked feature center point is used as the feature center point of the missing tooth; The missing teeth after the feature center point calibration is transmitted to the tooth feature analysis and processing end.

7. The dental data analysis system based on medical big data according to claim 6, characterized in that: The specific method of the tooth feature analysis processing end for numerically analyzing the center point distance of the corresponding teeth located in the upper or lower gums is: The upper or lower gum is locked from the dental CT image. The upper or lower gum has been calibrated in advance. The upper gum is the area where the teeth are arranged above, and the lower gum is the area where the teeth are arranged below. Confirm the characteristics of the internal teeth located in the upper gums, including normal teeth or missing teeth, and mark the characteristic center points of the missing teeth and the center points of the normal teeth as the midpoints of the internal teeth: identify the straight-line distances between the inner midpoints of the adjacent internal teeth in the corresponding gums, and then mark the confirmed several straight-line distances as L q , where q represents different straight-line distances, and the confirmed L q Perform mean processing to confirm the characteristic parameter L1 of the upper gum; Treat the inner teeth of the lower gum in the same way as the upper gum, and confirm the characteristic parameter L2 of the lower gum; Identify whether L1 and L2 satisfy: |L1-L2|>Y2, where Y2 is a preset value. If so, generate and display an arrangement abnormality signal.

8. The dental data analysis system based on medical big data according to claim 7, characterized in that: If L1 and L2 do not satisfy: |L1-L2|>Y2, a normal arrangement signal is generated.

Images