Oral auxiliary examination control system

Abstract

The application relates to the technical field of oral auxiliary examination control, in particular to an oral auxiliary examination control system; the system comprises a memory, an auxiliary examination control module and an output processing module; three-dimensional oral images obtained through an oral endoscope or a CBCT device are used to analyze the health states of oral superficial layers and structural layers in real time, and preliminary auxiliary examination reports of the oral superficial layers and the structural layers are automatically generated; the reports are comprehensively analyzed according to the report time length and abnormal values to obtain report optimization values, so that the emergency degree and the complexity degree of each auxiliary examination report can be accurately evaluated; meanwhile, the diagnosis records of oral doctors and the current task amount are used for deep analysis to obtain corresponding processing values, so that the diagnosis level and the work efficiency of the doctors can be comprehensively evaluated, the dynamic adjustment of the processing values ensures the continuous optimization of the doctors, the doctors are accurately matched to suitable doctors, and therefore the overall efficiency and the accuracy of oral examinations are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oral auxiliary examination control, in particular to an oral auxiliary examination control system. BACKGROUND

[0002] Oral auxiliary examination refers to the examination of the oral health status of a patient by means of various auxiliary equipment and technology in the process of stomatology, so as to enable doctors to evaluate the oral status of the patient and formulate a treatment plan. It can be seen that the development of oral auxiliary examination technology improves the early detection rate and diagnosis and treatment level of oral diseases, and is crucial to maintaining the oral health of patients. The current oral auxiliary examination usually adopts manual examination and judgment with the aid of auxiliary equipment, which greatly reduces the work efficiency. Moreover, the manual examination method may miss the oral examination due to different subjective consciousness of people. SUMMARY

[0003] The present application relates to the technical field of oral auxiliary examination control, in particular to an oral auxiliary examination control system.

[0004] The present application relates to the technical field of oral auxiliary examination control, in particular to an oral auxiliary examination control system.

[0005] The storage is connected in communication with the oral examination equipment to acquire the three-dimensional oral images of the user in real time and store them. The three-dimensional oral images are classified into the oral superficial layer and the oral structure layer according to their sources.

[0006] The auxiliary examination control module analyzes the three-dimensional oral images of the user in real time to preliminarily judge the state of the oral superficial layer and the state of the oral structure layer, and outputs an auxiliary examination report accordingly. The auxiliary examination report includes a superficial layer auxiliary examination report and a structure layer auxiliary examination report. The output process of the auxiliary examination report is as follows:

[0007] Step one: if the category is the oral superficial layer, step two will be performed; if the category is the oral structure layer, step three will be performed.

[0008] Step two: based on the three-dimensional oral image of the category of the oral superficial layer, the state of the oral superficial layer is preliminarily judged, and a superficial layer auxiliary examination report is output. Specifically, the superficial layer auxiliary examination report is composed of the three-dimensional oral image, the category, the superficial abnormal part, the corresponding superficial abnormal value, the local image and the position coordinates.

[0009] Step three: judging the state of the oral structure layer based on the three-dimensional oral image of the category of oral structure layer, and outputting a structure layer auxiliary examination report; the structure layer auxiliary examination report is composed of the three-dimensional oral image, the category, the structure abnormal area and the corresponding structure abnormal value, and the local image;

[0010] The output processing module matches the auxiliary examination report of the user based on the auxiliary examination report of the user to send to the corresponding oral doctor end.

[0011] Preferably, the state of the oral superficial layer is preliminarily judged based on the three-dimensional oral image of the user, and the specific process is: 2-1: extracting the three-dimensional oral model of the category of oral superficial layer, reconstructing it to obtain a three-dimensional surface model with a smooth surface, dividing the oral cavity into several parts according to the three-dimensional surface model, and generating a unique position coordinate according to the position of each part in the three-dimensional surface model, and then forming a plurality of surface points in each part after grid processing of the three-dimensional surface model;

[0012] 2-2: calculating the curvature value of each surface point, and comprehensively quantitatively analyzing the curvature change degree and the curvature abnormal degree in each part to obtain the curvature performance value of each part;

[0013] 2-3: identifying the color of each surface point in each part and the category thereof, and the specific category is gingiva or tooth, and comprehensively quantitatively analyzing the color abnormal degree and the color change degree of each part to obtain the color performance value of each part;

[0014] 2-4: formulaically calculating and analyzing the curvature performance value K Q and the color performance value K Y of each part to obtain the superficial abnormal value QY, and the specific calculation formula is:

[0015] QY=λ5×K Q +λ6×K Y

[0016] Wherein λ5 and λ6 are respectively a set proportion constant; thus the superficial abnormal value of each part in the three-dimensional oral image can be obtained, if the superficial abnormal value is greater than or equal to the set superficial abnormal threshold value, then the part is recorded as a superficial abnormal part, and the local image thereof and the corresponding position coordinate are extracted from the three-dimensional oral image; the oral superficial layer auxiliary examination report is composed of the three-dimensional oral image, the category, the superficial abnormal part and the corresponding superficial abnormal value, the local image and the position coordinate, and is outputted.

[0017] Preferably, the specific process of comprehensively quantitatively analyzing the curvature change degree and the curvature abnormal degree in each part is:

[0018] The curvature gradient value is obtained by calculating the difference between the curvature values of two adjacent surface points, and a plurality of curvature gradient values are obtained. A standard curvature gradient value is set, and the curvature gradient value is compared with the standard curvature gradient value. If the curvature gradient value is greater than the standard curvature gradient value, the curvature gradient value is recorded as an abnormal curvature gradient value. The number of abnormal curvature gradient values in the part is counted, and the mean value of the abnormal curvature gradient values is calculated. The number of abnormal curvature gradient values and the mean value of abnormal curvature gradient values are normalized and the numerical value is obtained. The numerical value is weighted to obtain the curvature gradient change value Q1.

[0019] A standard curvature interval is set, and the curvature values of the surface points are compared with the standard curvature interval to determine the curvature abnormal points and the corresponding abnormal curvature values. The specific determination method is as follows: if the curvature value of the surface point is greater than the upper limit of the standard curvature interval or less than the lower limit of the standard curvature interval, the surface point is recorded as a curvature abnormal point, and the difference between the curvature value and the upper limit of the standard curvature interval and the lower limit of the standard curvature interval is calculated, and the minimum difference is selected as the abnormal curvature value of the curvature abnormal point. The curvature abnormal point is marked with a color different from the background color. The sum of the abnormal curvature values corresponding to each curvature abnormal point is calculated to obtain the total abnormal curvature value Q2.

[0020] The curvature gradient change value Q1 and the total abnormal curvature value Q2 are formulaically calculated and analyzed to obtain the curvature performance value K Q , and the specific calculation formula is

[0021] K Q =log2(λ1×e Q1 +λ2×e Q2 +1)

[0022] Wherein λ1 and λ2 are proportional constants, and their specific values are set by the person skilled in the art according to actual needs.

[0023] Preferably, the specific process of comprehensive quantitative analysis of the color abnormality degree and the color change degree of each part is as follows:

[0024] The color of each surface point is represented as an RGB triplet using the RGB color model, and the specific RGB triplet is represented as (Ri, Gi, Bi), where i=1, 2, 3…I, I belongs to a positive integer, I represents the total number of surface points, and i represents any one surface point. Different categories of surface points correspond to a standard color triplet, which is recorded as (HR, HG, HB). The color difference value Di of each surface point is calculated in the RGB space by using the Euclidean distance, and the specific Euclidean distance formula is as follows:

[0025] A standard color difference value is set, if the color difference value is greater than the standard color difference value, the surface point is recorded as a color difference abnormal point, and the color difference value is taken as the color difference abnormal value of the color difference abnormal point; the color difference abnormal values of the color difference abnormal points are summed to obtain a color difference abnormal total value S1;

[0026] The color three elements of the adjacent two surface points are calculated by using the Euclidean distance in the RGB space to obtain the color gradient value of the adjacent two surface points with respect to the color three elements, so that a plurality of color gradient values are obtained; a standard color gradient value is set, if the color gradient value is greater than the standard color gradient value, the color gradient value is recorded as an abnormal color gradient value, the number of abnormal color gradient values is counted, and the mean value of the abnormal color gradient values is calculated; the number of abnormal color gradient values and the mean value of abnormal color gradient values are normalized and the numerical value is taken, and the numerical value is weighted to obtain an abnormal color change value S2;

[0027] The color difference abnormal total value S1 and the abnormal color change value S2 are calculated and analyzed to obtain a color performance value K Y , and the specific calculation formula is:

[0028] K Y = log2(λ3×e Y1 +λ4×e Y2 +1)

[0029] Wherein λ3 and λ4 are proportional constants, which are set by the person skilled in the art according to the actual needs.

[0030] Preferably, the three-dimensional oral image of the user is used to preliminarily judge the state of the oral structure layer, and the specific process is: 3-1: extracting the three-dimensional oral model of the category of oral structure layer, and dividing it into a plurality of regions according to the anatomical region division principle, wherein each tooth and its corresponding transmission area part, tooth root part and alveolar bone part are divided into a region, so that a plurality of regions are obtained;

[0031] 3-2: identifying each region and extracting the density value of the root tip transmission area of each tooth, the periodontal membrane thickness of the alveolar bone, and the alveolar bone distance therefrom, and recording them as A, C and V respectively; a set of standard tooth three elements is set, including standard density interval, standard periodontal membrane thickness interval and standard alveolar bone distance, and recording them as [R1, R2], [R3, R4] and R5 respectively; the density value, periodontal membrane thickness, alveolar bone distance, standard density interval, standard periodontal membrane thickness interval and standard alveolar bone distance of each tooth are calculated and analyzed to obtain the root tip performance value G A , the periodontal performance value G C and the alveolar bone performance value G V, the specific calculation formula is:

[0032]

[0033] Wherein η1, η2, η3 are respectively set proportional coefficient, its value is set by the person skilled in the art according to actual demand self;

[0034] 3-3: the root tip performance value G A , periodontal performance value G C And alveolar bone performance value G V Carry out normalization processing and take its value, the value is calculated and analyzed to obtain the structure abnormal value ACV of tooth, the specific calculation formula is:

[0035] ACV=δ1×G A +δ2×G C +δ3×G V

[0036] Wherein δ1, δ2, δ3 are respectively set proportional coefficient;

[0037] 3-4: thus the structure abnormal value of each tooth can be obtained, if the structure abnormal value is greater than or equal to the structure abnormal threshold value, then the tooth region is recorded as a structure abnormal region, and its local image is extracted from the three-dimensional oral image; the structure layer auxiliary examination report is composed of three-dimensional oral image, belonging category, structure abnormal region and its corresponding structure abnormal value, local image, and is output.

[0038] Preferably, the specific process of report matching based on the user's auxiliary examination report is:

[0039] Step one: set several oral physician terminals, and classify them according to the field of expertise, into superficial layer category and structure layer category; thus the superficial layer category and structure layer category correspond to several oral physician terminals respectively; each oral physician terminal corresponds to a processing value, and the oral physician terminals of the same category are sorted in order according to their corresponding processing values from large to small, and the oral physician terminal with the largest processing value is selected as the target terminal;

[0040] Step two: extract the report time from each auxiliary examination report belonging to the same category, and calculate the time difference value between the report time and the current system time to obtain the report duration, and record it as P1; the number of superficial abnormal parts or structure abnormal values in each auxiliary examination report is counted, and the sum of the corresponding superficial abnormal values or structure abnormal values is calculated to obtain the superficial abnormal total value or structure abnormal total value as the report abnormal value, and record it as P2; the report duration P1 and the report abnormal value P2 are normalized and the values are taken, the values are calculated and analyzed to obtain the report optimization value Pg, the specific calculation formula is:

[0041] Pg = g1 x P1 + g2 x P2

[0042] wherein g1, g2 are the set proportional constant respectively;

[0043] Step three: arrange the auxiliary examination reports of the same category in order according to their corresponding report preferred values from large to small, select the auxiliary examination report with the largest report preferred value as the target report, and assign the target report to the target doctor terminal of the same category, thereby the number of reports in the processing list of the oral doctor terminal increases by one; each time the oral doctor terminal completes a report diagnosis, the number of reports in the corresponding processing list decreases by one, and the diagnosis parameters of this report diagnosis are recorded, including the diagnosis start time and the diagnosis end time;

[0044] Step four: each time a report diagnosis is completed, the latest processing value of the oral doctor terminal is obtained by diagnosis cumulative analysis, and is updated to step one; specifically:

[0045] Step five: repeat steps one to four until the diagnosis and processing of all auxiliary examination reports are completed.

[0046] Preferably, the specific process of diagnosis cumulative analysis is:

[0047] The number of report diagnoses is counted, and if the number of report diagnoses is zero, the processing value is assigned as M;

[0048] If the number of report diagnoses is greater than zero, diagnosis cumulative analysis is performed, specifically:

[0049] The historical diagnosis times of each oral doctor terminal in the same category and the diagnosis start time, diagnosis end time of each diagnosis are extracted, and the diagnosis duration is calculated by time difference to obtain the diagnosis duration of each diagnosis, and the diagnosis duration of each diagnosis is calculated by mean value to obtain the diagnosis mean length Z1;

[0050] The feedback record of the oral doctor terminal is obtained, wherein the feedback record includes the number of feedbacks and the feedback evaluation, each feedback is divided into satisfied feedback and unsatisfied feedback according to the feedback evaluation, the number of satisfied feedback and unsatisfied feedback is counted respectively, and is recorded as Z2 and Z3 respectively;

[0051] The number of reports in the processing list of the oral doctor terminal at the current time is obtained, and is recorded as Z4;

[0052] The diagnosis mean length Z1, the number of satisfied feedback Z2, the number of unsatisfied feedback Z3 and the number of reports Z4 are normalized and the numerical values are taken, and the numerical values are calculated and analyzed by formula to obtain the processing value Zβ, and the specific calculation formula is:

[0053]

[0054] Wherein β1, β2, β3, β4 are respectively set proportional constant.

[0055] The beneficial effects of the present application are:

[0056] 1. The three-dimensional oral image obtained by the oral endoscope or CBCT device analyzes the health status of the oral superficial layer and structure layer in real time, automatically assesses possible oral abnormalities, reduces the workload of doctors, and improves the inspection efficiency; The preliminary auxiliary examination report of the oral superficial layer and structure layer can effectively assist the oral doctor in the auxiliary examination; Compared with traditional manual diagnosis, the workload of doctors is reduced, and artificial errors are avoided, ensuring the high accuracy of the diagnosis result; 2. Through image labeling, local image extraction and other methods, the doctor can better understand the image information, provide clearer views, and enhance decision support, and the doctor can quickly locate the abnormal area according to the report, providing auxiliary function for the oral doctor;

[0057] 3. The report optimization value is obtained by comprehensively analyzing the report duration and report abnormal value, which can accurately evaluate the emergency degree and complexity of each auxiliary examination report, ensure that the emergency and complex reports can be processed first, and avoid delay or omission; At the same time, the diagnosis record of the oral doctor end and the current task amount are analyzed to obtain the corresponding processing value, which can comprehensively evaluate the diagnosis level and work efficiency of the doctor, and the dynamic adjustment of the processing value ensures the continuous optimization of the doctor, not only improves the diagnosis efficiency, but also ensures the best use of resources, realizes precise matching to the appropriate doctor end, and improves the overall efficiency and accuracy of the oral examination. BRIEF DESCRIPTION OF DRAWINGS

[0058] The present application will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 It is a schematic diagram of the system module of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0061] Please refer to Figure 1 The present application is an oral auxiliary examination control system, which comprises a memory, an auxiliary examination control module and an output processing module.

[0062] The memory is connected in real time with the oral examination device (usually referred to as an endoscope or CBCT, etc.) to collect the three-dimensional oral image of the user and store it; specifically, the image is collected using an oral endoscope to generate several two-dimensional oral images, which are algorithmically spliced to obtain the three-dimensional oral image of the user; or, the CBCT (cone beam CT) is used to scan the user's mouth to obtain the three-dimensional oral image of the user; it should be noted that if the user needs to check the local oral problem (such as gingivitis, dental caries, dental calculus, etc.), the oral examination device usually used is an endoscope, which is mainly used to check the superficial layer of the user's oral cavity; if the user needs to check the complex oral disease (for example, periodontal disease, tooth root disease, tooth displacement, jaw bone disease, etc.), the oral examination device usually used is CBCT (cone beam CT), which is mainly used to check the structure layer of the user's oral cavity; thus, each three-dimensional oral image can be classified into an oral superficial layer and an oral structure layer according to its source.

[0063] The auxiliary examination control module analyzes the three-dimensional oral image of the user in real time to preliminarily determine the state of the oral superficial layer and the state of the oral structure layer, and outputs an auxiliary examination report accordingly; specifically:

[0064] Step one: if the category is the oral superficial layer, step two will be performed; if the category is the oral structure layer, step three will be performed;

[0065] Step two: preliminarily determine the state of the oral superficial layer, specifically:

[0066] 2-1: extract the three-dimensional oral model of the oral superficial layer, and reconstruct it to obtain a three-dimensional surface model; divide the oral cavity into several parts according to the three-dimensional surface model (usually, the gingival part and the tooth part are separated; for example, one part is a small part of the gingiva, and one part is a small part of the tooth), and generate a unique position coordinate according to the position of each part in the three-dimensional surface model to represent its position in the three-dimensional surface model; perform meshing processing on the three-dimensional surface model to ensure that the spatial position and normal information of each surface point in the part are available; 2-2: calculate the curvature value of each surface point; it should be noted that caries, calculus, gingivitis and other problems usually manifest as local curvature of large concave or missing or convex; the curvature values of adjacent two surface points are calculated to obtain the curvature gradient value, and a plurality of curvature gradient values are obtained; a standard curvature gradient value is set (the standard curvature gradient value refers to the curvature change of the normal oral cavity, and its specific value is set by the person skilled in the art according to his own work experience), the curvature gradient value is compared with the standard curvature gradient value, if the curvature gradient value is greater than the standard curvature gradient value, it indicates that the curvature change is abnormal, and the possibility of oral superficial abnormality is greater; the curvature gradient value is recorded as an abnormal curvature gradient value, the number of abnormal curvature gradient values in the part is counted, and the mean value of each abnormal curvature gradient value is calculated; the number of abnormal curvature gradient values and the mean value of abnormal curvature gradient values are normalized and the value is taken, and the value is weighted to obtain the curvature gradient change value Q1; it can be easily obtained from the curvature gradient change value that the more the number of abnormal curvature gradient values in the part, the greater the mean value of abnormal curvature gradient values, indicating that the curvature change in the part is more abnormal, indicating that the possibility of oral superficial abnormality is greater, and the curvature gradient change value is greater;

[0067] A standard curvature interval is set (the standard curvature interval refers to the curvature of the normal oral cavity, and its specific value is set by the person skilled in the art according to his own work experience), and the curvature values of each surface point are compared with the standard curvature interval to determine the curvature abnormal point and its corresponding abnormal curvature value; the specific determination method is: if the curvature value of the surface point is greater than the upper limit of the standard curvature interval or less than the lower limit of the standard curvature interval, the surface point is recorded as a curvature abnormal point, and the difference between its curvature value and the upper limit of the standard curvature interval and the lower limit of the standard curvature interval is calculated, and the minimum difference is selected as the abnormal curvature value of the curvature abnormal point, and the curvature abnormal point is marked with a color different from the background color; the sum of the abnormal curvature values of each curvature abnormal point is calculated to obtain the total abnormal curvature value, which is recorded as Q2;

[0068] The curvature gradient change value Q1 and the total abnormal curvature value Q2 are calculated and analyzed to obtain the curvature performance value KQ The specific calculation formula is

[0069] K Q = log2(λ1×e Q1 +λ2×e Q2 +1)

[0070] wherein λ1 and λ2 are respectively set proportional constants, and the specific values thereof are set by the person skilled in the art according to actual needs; it can be known from the formula that the greater the curvature gradient change value is, the greater the abnormal curvature total value is, indicating that the greater the possibility of the existence of oral superficial abnormalities at the site is, and the greater the curvature performance value is; it should be noted that the normal tooth surface curvature is smooth and continuous, and the healthy gum curve is smooth and adheres to the tooth; therefore, the abnormal curvature change means that the greater the possibility of the existence of oral superficial abnormalities is; 2-3: identify the color of each surface point in each site and the category thereof (the specific category is gum or tooth), and express the color of each surface point as an RGB three-tuple using the RGB color model, specifically (Ri, Gi, Bi), wherein i = 1, 2, 3…I, I belongs to a positive integer, I represents the total number of surface points, and i represents any one surface point; set a standard color three-tuple corresponding to the surface points of different categories, and record it as (HR, HG, HB); for example, the color corresponding to the standard color three-tuple corresponding to the gum is light pink, and the color corresponding to the standard color three-tuple corresponding to the tooth is white; it should be noted that the color difference value Di of each surface point is calculated in the RGB space by using the Euclidean distance, and the specific Euclidean distance formula is:

[0071] Set a standard color difference value (the standard color difference value refers to the allowable range of the color of normal gum or normal tooth, and the value thereof is set by the person skilled in the art according to his own work experience), compare and analyze the color difference values of each surface point with the standard color difference value, if the color difference value is greater than the standard color difference value, the surface point is recorded as a color difference abnormal point, and the color difference value is taken as the color difference abnormal value of the color difference abnormal point; and the color difference abnormal values of each color difference abnormal point are summed to obtain a color difference abnormal total value, which is recorded as S1.

[0072] The color gradient value of the color tri-element of the two adjacent surface points is calculated by using the Euclidean distance in the RGB space, and thus a plurality of color gradient values representing the color change degree value of the two adjacent surface points are obtained; a standard color gradient value (the standard color gradient value refers to the color change performance of a normal oral cavity, and the value is set by a person skilled in the art according to his own work experience) is set, and each color gradient value is compared with the standard color gradient value; if the color gradient value is greater than the standard color gradient value, it indicates that the color change is abnormal, and the color gradient value is recorded as an abnormal color gradient value; the number of abnormal color gradient values is counted, and the mean value of each abnormal color gradient value is calculated to obtain an abnormal color gradient mean value; the number of abnormal color gradient values and the abnormal color gradient mean value are normalized and the values are taken, and the values are weighted to obtain an abnormal color change value S2;

[0073] The color performance value K is obtained by formulaic calculation and analysis of the total color difference abnormal value S1 and the abnormal color change value S2 Y , and the specific calculation formula is:

[0074] K Y =log2(λ3×e Y1 +λ4×e Y2 +1)

[0075] Wherein λ3 and λ4 are proportional constants set by a person skilled in the art according to actual needs; as can be seen from the formula, the greater the color difference between the user's three-dimensional oral image and the standard color, and the greater the color change of the same type of position, the greater the possibility of oral superficial layer abnormality, and the greater the color performance value; 2-4: the curvature performance value K Q and the color performance value K Y are formulaically calculated and analyzed to obtain the superficial abnormal value QY, and the specific calculation formula is:

[0076] QY=λ5×K Q +λ6×K Y

[0077] Wherein λ5 and λ6 are proportional constants; thus the superficial abnormal value of each part in the three-dimensional oral image is obtained, and the superficial abnormal value is compared with the set superficial abnormal threshold value; if the superficial abnormal value is greater than or equal to the set superficial abnormal threshold value, the part is recorded as a superficial abnormal part, and the local image and its corresponding position coordinates are extracted from the three-dimensional oral image; the superficial layer auxiliary examination report is composed of the three-dimensional oral image, the category, the superficial abnormal part and its corresponding superficial abnormal value, the local image and the position coordinates, and is output;

[0078] Step three: preliminary judgment of oral structure layer state, specifically:

[0079] 3-1: extract the three-dimensional oral model of the category of oral structure layer, and divide it into several regions according to the anatomical region division principle, wherein each tooth and its corresponding transmission zone part, tooth root part and alveolar bone part are divided into a region, thus obtaining several regions;

[0080] 3-2: identify each region and extract the density value of each tooth root tip transmission zone from it (the normal root tip transmission zone density value is usually 400-1000HU, less than 400HU, there is a risk of root tip lesion or bone absorption problem), periodontal membrane thickness of alveolar bone (normal periodontal membrane thickness is 0.2-0.4mm, if the root tip periodontal membrane thickness exceeds 0.4mm, it indicates the risk of root tip inflammation or periodontitis; if less than 0.2mm, it indicates that the tooth root and alveolar bone density are close, the tooth root membrane disappears or thins, and there is a risk of periodontal membrane atrophy, adhesion or sclerotic lesion), alveolar bone distance (alveolar bone distance refers to the vertical distance from the top of the alveolar bone to the bifurcation point of the tooth root, and the normal alveolar bone distance is usually 1-2mm, greater than 2mm indicates the risk of alveolar bone absorption, i.e. periodontal disease), and mark them as A, C and V respectively; set a standard tooth three-element, the standard tooth three-element includes a standard density interval, a standard periodontal membrane thickness interval and a standard alveolar bone distance, and mark them as [R1, R2], [R3, R4] and R5 respectively; it should be noted that R1 is usually valued at 400HU, R2 is valued at 1000HU, R3 is valued at 0.2mm, R4 is valued at 0.4mm, and R5 is valued at 2mm; the density value, periodontal membrane thickness, alveolar bone distance, standard density interval, standard periodontal membrane thickness interval and standard alveolar bone distance of each tooth are calculated and analyzed to obtain the root tip performance value G A , periodontal performance value G C and alveolar bone performance value G V of each tooth, and the specific calculation formula is:

[0081]

[0082] wherein η1, η2, η3 are the set proportion coefficients, the values of which are set by the personnel in the art according to the actual needs; min{A-[R1, R2]} means that A is calculated by subtracting R1 and R2 respectively, and the smallest difference is selected; min{C-[R3, R4]} min{A-[R1, R2]} means that C is calculated by subtracting R3 and R4 respectively, and the smallest difference is selected;

[0083] 3-3: the root tip performance value G A , periodontal performance value G C and alveolar bone performance value G VThe normalized values are taken and formulaic calculation and analysis are performed on the values to obtain the structural abnormality value ACV of the teeth, and the specific calculation formula is:

[0084] ACV = δ1 x G A + δ2 x G C + δ3 x G V

[0085] wherein δ1, δ2, and δ3 are respectively set proportional coefficients, and according to the formula, when the root tip performance value G A is greater, the periodontal performance value G C is greater, the alveolar bone performance value G V is greater, it indicates that the risk of abnormality of the oral structure layer is greater, and then the structural abnormality value is greater;

[0086] 3-4: The structural abnormality value of each tooth is obtained, and a comparison analysis is performed on the structural abnormality value and the set structural abnormality threshold value, if the structural abnormality value is greater than or equal to the structural abnormality threshold value, then the tooth region is recorded as a structural abnormality region, and a local image thereof is extracted from the three-dimensional oral image; a structural layer auxiliary examination report is formed by the three-dimensional oral image, the category, the structural abnormality region, the corresponding structural abnormality value, and the local image, and the structural layer auxiliary examination report is outputted;

[0087] The superficial layer auxiliary examination report and the structural layer auxiliary examination report are sent to the output processing module;

[0088] The three-dimensional oral image obtained by the oral endoscope or the CBCT device analyzes the health status of the superficial layer and the structural layer of the oral cavity in real time, automatically evaluates possible oral abnormalities, reduces the work burden of the doctor, and improves the examination efficiency; the preliminary auxiliary examination report of the superficial layer and the structural layer of the oral cavity is automatically generated, which can effectively assist the oral doctor in the auxiliary examination; compared with the traditional manual diagnosis, the workload of the doctor is reduced, and the artificial error is avoided, so that the high accuracy of the diagnosis result is ensured; at the same time, through image labeling and local image extraction, the doctor can better understand the image information, provide a clearer view, enhance the decision support, and the doctor can quickly locate the abnormal region according to the report to provide auxiliary function for the oral doctor. The output processing module matches the auxiliary examination report (including the superficial layer auxiliary examination report and the structural layer auxiliary examination report) of the user to send to the corresponding doctor end to achieve the goal of improving the efficiency and accuracy of the user's oral examination; specifically:

[0089] Step one: set several oral physician ends, and classify them according to the field of expertise, divide into superficial layer category and structure layer category, the specific classification is classified by the person skilled in the art according to the field of expertise of each oral physician; the superficial layer category and the structure layer category correspond to several oral physician ends respectively, each oral physician end corresponds to a processing value, obtains the latest processing value, and sorts the oral physician ends in the same category according to the corresponding processing value from large to small, selects the oral physician end with the largest processing value as the target end;

[0090] Step two: extract the report time (report time refers to the user's report generation time) from each auxiliary examination report belonging to the same category, and calculate the time difference with the current system time to obtain the report duration, and record it as P1; count the number of superficial abnormal parts or structure abnormal values in each auxiliary examination report, and sum the corresponding superficial abnormal values or structure abnormal values to obtain the superficial abnormal total value or structure abnormal total value as the report abnormal value, and record it as P2; normalize the report duration P1 and the report abnormal value P2 and take their values, and analyze the values by formula to obtain the report optimization value Pg, the specific calculation formula is:

[0091] Pg=g1×P1+g2×P2

[0092] Wherein g1, g2 are the set proportion constant, according to the formula, when the report duration is larger, it means that the report needs to be processed first, then the corresponding report optimization value is larger; when the report abnormal value is larger, it means that the report needs to be processed first, then the corresponding report optimization value is larger; thus the report optimization value of each auxiliary examination report can be obtained;

[0093] Step three: sort the auxiliary examination reports in the same category according to their corresponding report optimization value from large to small, select the auxiliary examination report with the largest report optimization value as the target report; assign the target report to the target physician end in the same category, so that the number of reports in the processing list of the oral physician end increases by one; each time the oral physician end completes a report diagnosis, the number of reports in the corresponding processing list decreases by one, and the diagnosis parameters of this report diagnosis are recorded, including the diagnosis start time and the diagnosis end time;

[0094] Step four: each time a report diagnosis is completed, the latest processing value of the oral physician end is obtained by diagnosis cumulative analysis, which is:

[0095] Count the number of report diagnoses, if the number of report diagnoses is zero, the processing value is assigned to M (M is a fixed value, the specific value is set by the person skilled in the art, usually the person skilled in the art will set it to two-thirds of the current oral physician end in the same category about the processing value ranking table);

[0096] If the number of diagnosis reports is greater than zero, perform diagnosis cumulative analysis, specifically:

[0097] Extract the historical diagnosis number of each oral physician terminal in the same category, as well as the diagnosis start time and diagnosis end time of each diagnosis, and calculate the diagnosis duration by the time difference to obtain the diagnosis duration of each diagnosis. Calculate the average of the diagnosis duration of each diagnosis to obtain the diagnosis average length, denoted as Z1;

[0098] Obtain the feedback record of the oral physician terminal, wherein the feedback record includes the number of feedbacks and the feedback evaluation. It should be noted that when the user performs oral examination and subsequent oral diagnosis, the user will provide feedback to the oral physician after completing the diagnosis. Usually, the number of feedbacks is less than the actual number of diagnoses of the oral physician, because not all users will provide feedback after completing the diagnosis. According to the feedback evaluation, each feedback is divided into satisfied feedback and unsatisfied feedback, and the number of satisfied feedback and unsatisfied feedback is counted respectively, and denoted as Z2 and Z3 respectively;

[0099] Obtain the number of reports in the processing list of the oral physician terminal at the current time, and denote it as Z4;

[0100] Normalize the diagnosis average length Z1, the number of satisfied feedback Z2, the number of unsatisfied feedback Z3, and the number of reports Z4, and take their values. Formulaic calculation and analysis are performed on the values to obtain the processing value Zβ. The specific calculation formula is:

[0101]

[0102] Wherein β1, β2, β3, β4 are respectively set proportional constants. According to the formula, the larger the diagnosis average length, the lower the efficiency of the oral physician, and the smaller the processing value. The larger the number of satisfied feedbacks and the smaller the number of unsatisfied feedbacks, the larger the processing value. The larger the number of reports in the processing list, the smaller the processing value.

[0103] Update the processing value of each oral physician terminal in the same category to step one;

[0104] Step five: repeat steps one to four until all the diagnosis processing of the auxiliary examination report is completed.

[0105] The report preferred value obtained by comprehensive analysis of the report length and the report abnormal value can accurately evaluate the emergency degree and complexity of each auxiliary examination report, ensures that the report with high emergency degree and complexity can be processed preferentially, and avoids delay or omission; meanwhile, the diagnosis record and the current task quantity of the oral doctor end are analyzed to obtain the corresponding processing value, which can comprehensively evaluate the diagnosis level and work efficiency of the doctor, and the dynamic adjustment of the processing value ensures the continuous optimization of the doctor, improves the diagnosis efficiency, ensures the best use of resources, realizes accurate matching to the appropriate doctor end, and improves the overall efficiency and accuracy of the oral examination.

[0106] The above is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the structure of the present application or exceed the scope defined by the present claims, which shall belong to the protection scope of the present application.

Claims

1. An oral auxiliary examination control system, characterized by, The application relates to an auxiliary examination system and method for oral cavity, which comprises a memory, an auxiliary examination control module and an output processing module. The memory is connected with an oral cavity examination device to collect three-dimensional oral cavity images of a user in real time and store the images. The three-dimensional oral cavity images are classified into oral cavity superficial layers and oral cavity structure layers according to their sources. The auxiliary examination control module analyzes the three-dimensional oral cavity images of the user in real time to preliminarily judge the states of the oral cavity superficial layers and the oral cavity structure layers, and outputs auxiliary examination reports. The auxiliary examination reports include superficial layer auxiliary examination reports and structure layer auxiliary examination reports. Step 1: if the category is the oral cavity superficial layer, step 2 is executed; if the category is the oral cavity structure layer, step 3 is executed. Step 2: the three-dimensional oral cavity images of the oral cavity superficial layer are used to preliminarily judge the state of the oral cavity superficial layer, and the superficial layer auxiliary examination report is outputted. The superficial layer auxiliary examination report is composed of the three-dimensional oral cavity image, the category, the superficial abnormal part, the corresponding superficial abnormal value, the local image and the position coordinates. Step 3: the three-dimensional oral cavity images of the oral cavity structure layer are used to preliminarily judge the state of the oral cavity structure layer, and the structure layer auxiliary examination report is outputted. The structure layer auxiliary examination report is composed of the three-dimensional oral cavity image, the category, the structure abnormal region, the corresponding structure abnormal value and the local image. The state of the oral cavity structure layer is preliminarily judged based on the three-dimensional oral cavity images of the user, and the specific process is as follows: 3-1: the three-dimensional oral cavity model of the oral cavity structure layer is extracted, and is divided into a plurality of regions according to the anatomical region division principle, wherein each tooth and the corresponding transmission zone part, tooth root part and alveolar bone part are divided into a region, so that a plurality of regions are obtained. 3-2: each region is identified, and the density value of each tooth root transmission zone, periodontal membrane thickness of alveolar bone and alveolar bone distance are extracted from the region. A set of standard tooth three elements are set, and the standard tooth three elements include a standard density interval, a standard periodontal membrane thickness interval and a standard alveolar bone distance.

2. The oral ancillary examination control system of claim 1, wherein, The density value, periodontal membrane thickness, alveolar bone distance, standard density interval, standard periodontal membrane thickness interval and standard alveolar bone distance of each tooth are calculated and analyzed to obtain the root tip performance value, periodontal performance value and alveolar bone performance value of each tooth. The root tip performance value, periodontal performance value and alveolar bone performance value are normalized and the numerical values are obtained, and the numerical values are calculated and analyzed to obtain the structure abnormal value of the tooth. The structure abnormal value of each tooth is obtained, and if the structure abnormal value is greater than or equal to the structure abnormal threshold value, the tooth region is recorded as a structure abnormal region, and the local image is extracted from the three-dimensional oral cavity image. The structure layer auxiliary examination report is composed of the three-dimensional oral cavity image, the category, the structure abnormal region, the corresponding structure abnormal value and the local image, and is outputted. The output processing module matches the auxiliary examination report of the user to send the report to a corresponding oral cavity doctor end. The state of the oral cavity superficial layer is preliminarily judged based on the three-dimensional oral cavity images of the user, and the specific process is as follows: 2-1: Extract the three-dimensional oral model of the superficial layer of the oral cavity, and reconstruct it to obtain a three-dimensional surface model with smooth surfaces; divide the oral cavity into several parts according to the three-dimensional surface model, and generate a unique position coordinate according to the position of each part in the three-dimensional surface model; and perform meshing processing on the three-dimensional surface model, so that a plurality of surface points are formed in each part; 2-2: Calculate the curvature value of each surface point, and comprehensively quantitatively analyze the curvature change degree and the curvature abnormality degree in each part to obtain the curvature performance value of each part; 2-3: Identify the color of each surface point in each part and the category to which it belongs, and the specific category is gingiva or tooth, and comprehensively quantitatively analyze the color abnormality degree and the color change degree of each part to obtain the color performance value of each part; 2-4: Perform formulaic calculation and analysis on the curvature performance value and the color performance value of each part to obtain the superficial abnormality value; thus, the superficial abnormality value of each part in the three-dimensional oral image can be obtained, and if the superficial abnormality value is greater than or equal to the set superficial abnormality threshold value, the part is recorded as a superficial abnormal part, and the local image of the part and the corresponding position coordinate are extracted from the three-dimensional oral image; the superficial layer auxiliary examination report is composed of the three-dimensional oral image, the category, the superficial abnormal part, the corresponding superficial abnormality value, the local image and the position coordinate, and is output.

3. An oral ancillary examination control system according to claim 2, wherein, The specific process of comprehensively quantitatively analyzing the curvature change degree and the curvature abnormality degree in each part is as follows: The curvature values of the adjacent two surface points are calculated to obtain the curvature gradient value, and thus a plurality of curvature gradient values are obtained; a standard curvature gradient value is set, the curvature gradient value is compared with the standard curvature gradient value, and if the curvature gradient value is greater than the standard curvature gradient value, the curvature gradient value is recorded as an abnormal curvature gradient value; the number of abnormal curvature gradient values in the part is counted, and the mean value of each abnormal curvature gradient value is calculated to obtain the mean value of the abnormal curvature gradient; The number of abnormal curvature gradient values and the mean value of the abnormal curvature gradient are normalized and the numerical value is taken, and the numerical value is weighted to obtain the curvature gradient change value; A standard curvature interval is set, and the curvature values of each surface point are compared with the standard curvature interval to determine the curvature abnormal point and the corresponding abnormal curvature value, and the specific determination method is as follows: if the curvature value of the surface point is greater than the upper limit of the standard curvature interval or less than the lower limit of the standard curvature interval, the surface point is recorded as a curvature abnormal point, and the difference between the curvature value and the upper limit of the standard curvature interval and the lower limit of the standard curvature interval is calculated, and the minimum difference is selected as the abnormal curvature value of the curvature abnormal point, and the curvature abnormal point is marked with a color different from the background color; The sum of the abnormal curvature values corresponding to each curvature abnormal point is calculated to obtain the total abnormal curvature value; The curvature gradient change value and the total abnormal curvature value are formulaically calculated and analyzed to obtain the curvature performance value.

4. The oral ancillary examination control system of claim 3, wherein, The specific process of comprehensively quantitatively analyzing the color abnormality degree and the color change degree of each part is as follows: The color of each surface point is represented as an RGB triplet using the RGB color model, and the specific RGB triplet is represented as (Ri, Gi, Bi), where i = 1, 2, 3 … I, I belongs to a positive integer, I represents the total number of surface points, and i represents any one of the surface points; A standard color triplet is set for each surface point of different categories, and is recorded as (HR, HG, HB). The color difference value Di of each surface point is calculated in the RGB space by using the Euclidean distance, and the specific Euclidean distance formula is: ； A standard color difference value is set. If the color difference value is greater than the standard color difference value, the surface point is recorded as a color difference abnormal point, and the color difference value is taken as the color difference abnormal value of the color difference abnormal point. The color difference abnormal values of each color difference abnormal point are summed to obtain the total color difference abnormal value; The color triplet of adjacent two surface points is calculated in the RGB space by using the Euclidean distance to obtain the color gradient value of adjacent two surface points with respect to the color triplet. Thus, a plurality of color gradient values are obtained. A standard color gradient value is set. If the color gradient value is greater than the standard color gradient value, the color gradient value is recorded as an abnormal color gradient value. The number of abnormal color gradient values is counted, and the mean value of each abnormal color gradient value is calculated to obtain the mean value of abnormal color gradient. The number of abnormal color gradient values and the mean value of abnormal color gradient are normalized and the numerical value is taken, and the numerical value is weighted to obtain the abnormal color change value. The color performance value is obtained by formulaic calculation and analysis of the total color difference abnormal value and the abnormal color change value.

5. The oral ancillary examination control system of claim 1, wherein, The specific process of report matching based on user's auxiliary examination report is as follows: Step one: set a plurality of oral doctor terminals, and classify them according to the field of expertise, into superficial layer category and structure layer category. Thus, the superficial layer category and the structure layer category correspond to a plurality of oral doctor terminals respectively. Each oral doctor terminal corresponds to a processing value, and the oral doctor terminals of the same category are sorted in order according to their corresponding processing values from large to small. The oral doctor terminal with the largest processing value is selected as the target terminal; Step two: extract the report time from each auxiliary examination report belonging to the same category, and calculate the time difference value between the report time and the current system time to obtain the report duration; The number of superficial abnormal parts or structure abnormal values in each auxiliary examination report is counted, and the corresponding superficial abnormal value or structure abnormal value is summed to obtain the superficial abnormal total value or structure abnormal total value as the report abnormal value. The report duration and the report abnormal value are normalized and the numerical value is taken, and the numerical value is formulaically calculated to obtain the report optimization value. Thus, the report optimization value of each auxiliary examination report is obtained. Step three: arrange the auxiliary examination reports of the same category in order according to their corresponding report preferred values from large to small, select the auxiliary examination report with the largest report preferred value as the target report, and assign the target report to the target doctor terminal of the same category, thereby increasing the number of reports in the processing list of the oral doctor terminal by one; each time the oral doctor terminal completes a report diagnosis, the number of reports in the corresponding processing list decreases by one, and the diagnosis parameters of this report diagnosis are recorded, including the diagnosis start time and the diagnosis end time; Step four: each time a report diagnosis is completed, a diagnosis cumulative analysis is performed to obtain the latest processing value of the oral doctor terminal, and the processing value is updated to step one; specifically: Step five: repeat steps one to four until all auxiliary examination reports are diagnosed and processed.

6. An oral ancillary examination control system according to claim 5, wherein, The specific process of diagnosis cumulative analysis is as follows: Count the number of report diagnoses, if the number of report diagnoses is zero, then assign the processing value as M; If the number of report diagnoses is greater than zero, perform diagnosis cumulative analysis, specifically: Extract the historical diagnosis times of each oral doctor terminal in the same category, as well as the diagnosis start time and diagnosis end time of each diagnosis, and perform time difference calculation to obtain the diagnosis time of each diagnosis, then perform mean value calculation to obtain the diagnosis average length; Obtain the feedback record of the oral doctor terminal, which includes the number of feedbacks and feedback evaluations, and divide each feedback into satisfied feedback and unsatisfied feedback according to the feedback evaluations, and count the number of satisfied feedback and unsatisfied feedback respectively; Obtain the number of reports in the processing list of the oral doctor terminal at the current time; Normalize the diagnosis average length, the number of satisfied feedback, the number of unsatisfied feedback and the number of reports, and take their numerical values, and perform formula calculation and analysis on the numerical values to obtain the processing value.

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