Intelligent analysis method and system for oral images

Abstract

The present invention discloses an intelligent analysis method and system for oral images, which relate to the field of image processing technology. Through intelligent processing and analysis of oral images, a tooth deviation index YPZ is calculated to accurately reflect the overall deviation degree of tooth arrangement and occlusal relationship. By comparing with a preset clinical statistical deviation threshold LZ, an oral recommendation evaluation plan can be generated, and the deviation index and deviation threshold are iteratively optimized based on patient feedback information. In the traditional orthodontic process, doctors rely on experience and manual judgment, which may result in subjective errors, and the adjustment process lacks a real-time feedback mechanism, resulting in a lag in the treatment effect. Through the feedback and adjustment mechanism of the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, a backpropagation algorithm is used to realize dynamic evaluation of oral health status and real-time optimization of treatment plans, greatly improving the accuracy and efficiency of the treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an intelligent analysis method and system for oral images. Background Art

[0002] With the rapid development of modern medical technology, image analysis, as an interdisciplinary field, has become a core technology in many medical applications. Its applications range from medical imaging to surgical assistance, encompassing the entire process from disease diagnosis to treatment planning. With the continuous advancement of oral medicine, intelligent analysis of oral images has gradually become a key tool in dentistry, especially in orthodontics, restoration, and treatment planning, where oral image analysis can provide more precise information.

[0003] Currently, when evaluating tooth alignment and occlusion, most dentists rely on traditional manual manipulation and experience to make judgments, typically visually inspecting or using traditional X-rays and CT images to observe tooth position and occlusion. While these methods can provide some reference information, they have significant drawbacks. First, relying on manual visual inspection or experience for evaluation is susceptible to the doctor's subjective judgment, which may lead to inconsistent diagnostic results. Second, traditional imaging technology has certain limitations in image resolution, contrast, and clarity, making it difficult to provide accurate information, especially for subtle changes in teeth and complex occlusions. Moreover, doctors often need to spend a lot of time on manual measurement and comparison, which is time-consuming and prone to errors. Therefore, the lack of automated, highly accurate, and efficient evaluation tools has seriously restricted further development. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent analysis method and system for oral images, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent analysis method for oral images, comprising the following steps:

[0006] S1, performing denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral image to obtain a processed oral image;

[0007] S2. Use edge detection algorithm and image segmentation method to extract the outline and position of the teeth from the processed oral image, obtain the coordinate information Pt and outline data Ct of each tooth, and form the tooth position feature set Ft;

[0008] S3. Compare the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt;

[0009] S4. Statistically analyzing the tooth deviation vector Dt to obtain a tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship;

[0010] S5. Obtain an image oral suggestion evaluation plan by comparing the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ, collect feedback information based on the content of the image oral suggestion evaluation plan, and then use the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S11, performing denoising preprocessing on the collected oral image, including using Gaussian filtering to remove noise in the oral image, and obtaining a filtered oral image;

[0013] S12. Perform contrast enhancement preprocessing on the filtered oral image using histogram equalization, and then sharpen the oral image using a Laplace operator to highlight edge information in the oral image, thereby obtaining a sharpened oral image.

[0014] Preferably, said S2 includes S21 and S22;

[0015] S21. Use an edge detection algorithm to identify the edges of the teeth in the oral image after sharpening, including using the Canny edge detection algorithm to identify the edges of the teeth in the oral image, specifically including smoothing, gradient calculation, non-maximum suppression and dual threshold detection processing steps, and finally output a binary image to mark the edges in the image, separate the teeth and the background in the oral image, and obtain the tooth edge image Ee.

[0016] Preferably, S22, using an image segmentation method to extract the contours and positions of the teeth from the edge image Ee, including extracting the specific area of each tooth, and then calculating the precise coordinate position of each tooth, using a threshold-based region growing algorithm or other segmentation techniques for segmentation, segmenting the edge image Ee by using a threshold segmentation method, and only when the pixel value of the edge image is greater than the threshold, the pixel is considered to be part of the tooth, and a judgment is made as to whether each pixel belongs to the tooth area, and the specific area of the tooth is marked using the white area in the edge image Ee to obtain a segmented tooth image S, and then using a contour tracking and boundary detection algorithm to extract the edge coordinates of each tooth from the tooth image S to generate contour data Ct, and then performing geometric calculations on the contour data Ct to obtain the center coordinates of each tooth, generating coordinate information Pt, and forming a tooth position feature set Ft;

[0017] The coordinate information Pt specifically represents the geometric center coordinates of teeth 1 to n, and the coordinate information Pt={(x1, y1), (x2, y2), ..., (xn, yn)}, where (xn, yn) represents the geometric center coordinates (x, y) of the nth tooth;

[0018] The contour data Ct specifically includes the two-dimensional coordinates of the contours of teeth 1 to n, and the contour data Ct={{(x11, y11), (x12, y12), ...}, {(x21, y21), (x22, y22), ...}, ..., {(xn1, yn1), (xn2, yn2), ..., (xnk, ynk)}}; (x11, y11) represents the first contour coordinate position (x, y) of the first tooth, (x12, y12) represents the second contour coordinate position (x, y) of the first tooth, and (xnk, ynk) represents the kth contour coordinate position (x, y) of the nth tooth;

[0019] The position feature set Ft specifically includes contour data Ct and coordinate information Pt of 1 to n teeth. The position feature set Ft is specifically Ft={(Ct, n, Pt, n)|i=1, 2, ..., n}.

[0020] Preferably, said S3 includes S31;

[0021] S31. Substitute the coordinate information Pt of each tooth in the tooth position feature set Ft into the preset calibration model and use the Euclidean distance method to calculate, obtain the position deviation ratio △Pt of each tooth, and integrate them simultaneously to form a tooth deviation vector Dt, wherein the tooth deviation vector Dt specifically includes the position deviation ratio △Pt of teeth 1 to n, and the tooth deviation vector Dt={△Pt1, △Pt2, ..., △Ptn}. The preset calibration model is based on the ideal position of the teeth under normal occlusion and arrangement.

[0022] Preferably, said S4 includes S41 and S42;

[0023] S41. Statistically calculating the position deviation ratio ΔPt of each tooth in the tooth deviation vector Dt to obtain an average value of the tooth deviation, which is labeled as the tooth deviation average index ΔPj. The position deviation ratio ΔPt of each tooth is then analyzed to determine the degree of dispersion, which is labeled as the tooth deviation standard deviation ΔPc.

[0024] The tooth deviation average index ΔPj is obtained by the following calculation formula:

[0025] ;

[0026] ;

[0027] ;

[0028] Where n represents teeth 1 to n, specifically the total number of teeth, i represents the i-th tooth, xi and yi represent the horizontal and vertical positions of the geometric center coordinates of the i-th tooth, respectively, MDx and MDy represent the horizontal mean deviation and vertical mean deviation, respectively, XZ and YZ represent the horizontal mean and vertical mean in the calibration model, respectively;

[0029] The tooth deviation standard deviation ΔPc is obtained by the following calculation formula:

[0030] ;

[0031] ;

[0032] ;

[0033] Where MCx and MCy represent the standard deviation of the horizontal axis and the standard deviation of the vertical axis, respectively.

[0034] Preferably, S42, performing weighted calculation based on the obtained tooth deviation average index ΔPj and tooth deviation standard deviation ΔPc to obtain a tooth deviation index YPZ, and quantifying the tooth arrangement and occlusion relationship, including when the YPZ value is large, it indicates that the patient's tooth deviation is serious, and there may be obvious irregular arrangement or occlusion problems; when the YPZ value is small, it indicates that the patient's teeth are relatively neat and the occlusion relationship is relatively normal, and reflects the overall deviation degree of the tooth arrangement and occlusion relationship;

[0035] The tooth deviation index YPZ is obtained by the following calculation formula:

[0036] ;

[0037] Where z1 and z2 represent the preset weight values of the mean index of tooth deviation △Pj and the standard deviation of tooth deviation △Pc, respectively, and z1+z2=1. The specific value is set by the user.

[0038] Preferably, said S5 includes S51 and S52;

[0039] S51. Obtain an image oral cavity suggestion evaluation scheme by comparing the tooth deviation index YPZ with a preset clinical statistical deviation threshold LZ. The image oral cavity suggestion evaluation scheme is obtained by the following comparison method:

[0040] When the tooth deviation index YPZ ≤ the clinical statistical deviation threshold LZ, obtain an oral evaluation plan with images showing normal tooth arrangement and occlusion;

[0041] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to adjust the observation frequency, and marking the oral image source patient evaluation mark as BJ=2;

[0042] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*150%, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to undergo orthodontic treatment, and marking the oral image source patient evaluation mark as BJ=5;

[0043] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*200%, an oral evaluation plan with image-based tooth arrangement and occlusion warning is obtained, prompting the patient to perform tooth arrangement correction and tooth occlusion adjustment, and marking the oral image source patient evaluation mark as oral evaluation mark BJ=9.

[0044] Preferably, S52, feedback information is collected according to the content of the image oral recommendation evaluation plan, including feedback information collected from patient follow-up records and feedback information collected from the mini-program questionnaire, wherein the time period for collecting feedback information is scaled and adjusted through the oral evaluation mark BJ, and the feedback information is subjected to steps S1 to S4 to obtain the feedback index FZ, which is then combined with the tooth deviation index YPZ to calculate the error index E, and the gradient descent method of the back-propagation mechanism is used to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, including scaling the error index E with the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0045] An intelligent analysis system for oral images, comprising an image processing module, a feature extraction module, a deviation calculation module, an evaluation module, and a feedback iteration module;

[0046] The image processing module performs denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral cavity image to obtain a processed oral cavity image;

[0047] The feature extraction module uses edge detection algorithm and image segmentation method to extract the contour and position of the teeth from the processed oral image, obtains the coordinate information Pt and contour data Ct of each tooth, and forms the tooth position feature set Ft;

[0048] The deviation calculation module compares the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt;

[0049] The evaluation module performs statistical analysis based on the tooth deviation vector Dt to obtain the tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship;

[0050] The feedback iteration module compares the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ to obtain the image oral suggestion evaluation plan, collects feedback information based on the content of the image oral suggestion evaluation plan, and then uses the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0051] The present invention provides an intelligent analysis method and system for oral images, which has the following beneficial effects:

[0052] (1) Through intelligent processing and analysis of oral images, the tooth deviation index YPZ is calculated to accurately reflect the overall deviation degree of tooth arrangement and occlusal relationship. By comparing with the preset clinical statistical deviation threshold LZ, an oral recommendation evaluation plan can be generated, and the deviation index and deviation threshold are iteratively optimized based on patient feedback information. In the traditional orthodontic process, doctors rely on experience and manual judgment, which may lead to subjective errors, and the adjustment process lacks a real-time feedback mechanism, resulting in a lag in treatment effect. Through the feedback and adjustment mechanism of the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, the back propagation algorithm is used to achieve dynamic assessment of oral health status and real-time optimization of treatment plans, greatly improving the accuracy and efficiency of treatment effects.

[0053] (2) By using the Euclidean distance method, the coordinate information Pt of each tooth in the tooth position feature set Ft is substituted into the preset calibration model for comparison, and the position deviation ratio △Pt of each tooth is accurately calculated, and these deviation data are integrated into the tooth deviation vector Dt. This process can accurately reflect the deviation of each tooth relative to the ideal arrangement position, providing a solid data foundation for subsequent deviation statistical analysis. Then, by statistically analyzing the position deviation ratio △Pt of each tooth in the tooth deviation vector Dt, the tooth deviation average index △Pj and tooth deviation standard deviation △Pc are generated, reflecting the overall deviation degree of tooth arrangement and occlusion relationship. Through weighted calculation, the tooth deviation index YPZ is further obtained. This index quantifies the tooth arrangement and occlusion relationship, facilitating clinical evaluation and diagnosis.

[0054] (3) By comparing the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ, the patient's tooth arrangement and occlusion status can be accurately identified, flexible and personalized oral health recommendations can be provided, and the patient's assessment mark BJ can be adjusted in a timely manner, making the assessment results more accurate and dynamic. By collecting feedback information such as patient follow-up records and mini-program questionnaires, the understanding of the patient's oral health status is further deepened, and the feedback data is processed using the feedback index FZ. The error index E is calculated by comparing with the tooth deviation index YPZ, and the tooth deviation index YPZ and the clinical statistical deviation threshold LZ are continuously adjusted. The gradient descent method of the back-propagation mechanism is used for iterative optimization. This process ensures the continuous precision of the oral health assessment plan, can reflect the changes in the patient's oral health in real time, and ensures that each treatment or prevention recommendation can be optimized and adjusted as the patient's status changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the steps of an intelligent analysis method for oral images according to the present invention;

[0056] Figure 2 This is a schematic diagram of a block diagram of an intelligent analysis system for oral images according to the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] The present invention provides an intelligent analysis method for oral images. Figure 1 , including the following steps:

[0060] S1, performing denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral image to obtain a processed oral image;

[0061] S2. Use edge detection algorithm and image segmentation method to extract the outline and position of the teeth from the processed oral image, obtain the coordinate information Pt and outline data Ct of each tooth, and form the tooth position feature set Ft;

[0062] S3. Compare the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt;

[0063] S4. Statistically analyzing the tooth deviation vector Dt to obtain a tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship;

[0064] S5. Obtain an image oral suggestion evaluation plan by comparing the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ, collect feedback information based on the content of the image oral suggestion evaluation plan, and then use the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0065] This embodiment, through intelligent processing and analysis of oral images, provides an efficient, accurate, and personalized orthodontic treatment plan assessment tool. This tool, utilizing advanced image processing and machine learning techniques, can automatically assess tooth alignment and occlusion without requiring hardware modifications. Specifically, by constructing a tooth position feature set Ft and comparing it with a calibration model, the system can rapidly obtain the deviation value for each tooth and further calculate a tooth deviation index YPZ, which accurately reflects the overall degree of deviation in tooth alignment and occlusion. By comparing this with a preset clinical statistical deviation threshold LZ, the method generates a recommended oral evaluation plan and iteratively optimizes the deviation index and threshold based on patient feedback, ensuring the accuracy and personalization of the plan. Traditional orthodontic treatment relies on experience and manual judgment, which can lead to subjective errors. Furthermore, the adjustment process lacks a real-time feedback mechanism, resulting in delayed treatment outcomes. The present invention, by utilizing a feedback and adjustment mechanism for the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, utilizes a backpropagation algorithm to achieve dynamic assessment of oral health status and real-time optimization of treatment plans, significantly improving the accuracy and efficiency of treatment outcomes. In addition, the system's combined application of image processing and edge detection algorithms not only reduces the need for manual intervention, but also significantly reduces the occurrence of human errors and improves the accuracy of treatment plans.

[0066] Example 2

[0067] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0068] S11, performing denoising preprocessing on the collected oral image, including using Gaussian filtering to remove noise in the oral image, and obtaining a filtered oral image;

[0069] S12. Perform contrast enhancement preprocessing on the filtered oral image using histogram equalization, and then sharpen the oral image using a Laplace operator to highlight edge information in the oral image, thereby obtaining a sharpened oral image.

[0070] Said S2 includes S21 and S22;

[0071] S21. Use an edge detection algorithm to identify the edges of the teeth in the oral image after sharpening, including using the Canny edge detection algorithm to identify the edges of the teeth in the oral image, specifically including smoothing, gradient calculation, non-maximum suppression and dual threshold detection processing steps, and finally output a binary image to mark the edges in the image, separate the teeth and the background in the oral image, and obtain the tooth edge image Ee.

[0072] S22. Use an image segmentation method to extract the contours and positions of the teeth from the edge image Ee, including extracting the specific area of each tooth, and then calculating the precise coordinate position of each tooth. Segmentation is performed using a threshold-based region growing algorithm or other segmentation techniques. The edge image Ee is segmented using a threshold segmentation method. When the pixel value of the edge image is greater than the threshold, the pixel is considered to be part of the tooth. Each pixel is judged to determine whether it belongs to the tooth area. The white area in the edge image Ee is used to mark the specific area of the tooth to obtain a segmented tooth image S. Then, a contour tracking and boundary detection algorithm is used to extract the edge coordinates of each tooth from the tooth image S to generate contour data Ct. Then, geometric calculations are performed on the contour data Ct to obtain the center coordinates of each tooth, generate coordinate information Pt, and form a tooth position feature set Ft.

[0073] The coordinate information Pt specifically represents the geometric center coordinates of teeth 1 to n, and the coordinate information Pt={(x1, y1), (x2, y2), ..., (xn, yn)}, where (xn, yn) represents the geometric center coordinates (x, y) of the nth tooth;

[0074] The contour data Ct specifically includes the two-dimensional coordinates of the contours of teeth 1 to n, and the contour data Ct={{(x11, y11), (x12, y12), ...}, {(x21, y21), (x22, y22), ...}, ..., {(xn1, yn1), (xn2, yn2), ..., (xnk, ynk)}}; (x11, y11) represents the first contour coordinate position (x, y) of the first tooth, (x12, y12) represents the second contour coordinate position (x, y) of the first tooth, and (xnk, ynk) represents the kth contour coordinate position (x, y) of the nth tooth;

[0075] The position feature set Ft specifically includes contour data Ct and coordinate information Pt of 1 to n teeth. The position feature set Ft is specifically Ft={(Ct, n, Pt, n)|i=1, 2, ..., n}.

[0076] In this embodiment, the complete process from image acquisition to precise tooth identification combines multiple advanced technologies such as denoising, contrast enhancement, sharpening, edge detection, and image segmentation to accurately extract the tooth position feature set Ft from the oral image. First, through denoising preprocessing and contrast enhancement, the quality of the oral image is guaranteed, noise is removed, and details are enhanced, making the subsequent edge detection and image segmentation more accurate. Next, the edge detection algorithm and image segmentation method use the Canny edge detection algorithm to accurately identify the edges of the teeth, and further extract the contours and precise positions of the teeth through threshold segmentation technology, generating the tooth position feature set Ft, which includes the precise geometric center coordinates Pt and two-dimensional contour coordinates Ct of each tooth. These processing steps work together to ensure the high-precision acquisition of tooth position information, providing a solid data foundation for subsequent deviation analysis and personalized treatment plans. Through this process, the present invention achieves high-precision tooth positioning and segmentation in oral image analysis, avoiding the problems of misidentification or incomplete processing caused by poor image quality or inaccurate algorithms in traditional methods. The effective generation of the tooth position feature set Ft ensures that subsequent analysis of tooth deviations can be performed based on accurate geometric information, significantly improving the system's effectiveness in orthodontic applications. This method overcomes the instability and errors associated with traditional manual labeling, improving the efficiency and accuracy of tooth position and morphology analysis while avoiding the limitations of manual manipulation. It is highly adaptable and maintains high stability across image processing for different patients, ensuring accurate identification and deviation calculation for each tooth.

[0077] Example 3

[0078] This embodiment is explained in Example 2, please refer to Figure 1 , specifically: said S3 includes S31;

[0079] S31. Substitute the coordinate information Pt of each tooth in the tooth position feature set Ft into the preset calibration model and use the Euclidean distance method to calculate, obtain the position deviation ratio △Pt of each tooth, and integrate them simultaneously to form a tooth deviation vector Dt, wherein the tooth deviation vector Dt specifically includes the position deviation ratio △Pt of teeth 1 to n, and the tooth deviation vector Dt={△Pt1, △Pt2, ..., △Ptn}. The preset calibration model is based on the ideal position of the teeth under normal occlusion and arrangement.

[0080] The calibration model includes a preset ideal tooth arrangement and occlusal position feature set Ft. Specifically, an ideal position is obtained by combining multiple normal people's tooth data sets and obtaining an average ideal tooth arrangement data set through statistical methods.

[0081] Said S4 includes S41 and S42;

[0082] S41. Statistically calculating the position deviation ratio ΔPt of each tooth in the tooth deviation vector Dt to obtain an average value of the tooth deviation, which is labeled as the tooth deviation average index ΔPj. The position deviation ratio ΔPt of each tooth is then analyzed to determine the degree of dispersion, which is labeled as the tooth deviation standard deviation ΔPc.

[0083] The tooth deviation average index ΔPj is obtained by the following calculation formula:

[0084] ;

[0085] ;

[0086] ;

[0087] Where n represents teeth 1 to n, specifically the total number of teeth, i represents the i-th tooth, xi and yi represent the horizontal and vertical positions of the geometric center coordinates of the i-th tooth, respectively, MDx and MDy represent the horizontal mean deviation and vertical mean deviation, respectively, XZ and YZ represent the horizontal mean and vertical mean in the calibration model, respectively;

[0088] The tooth deviation standard deviation ΔPc is obtained by the following calculation formula:

[0089] ;

[0090] ;

[0091] ;

[0092] Where MCx and MCy represent the standard deviation of the horizontal axis and the standard deviation of the vertical axis, respectively.

[0093] S42. Perform weighted calculation based on the obtained mean tooth deviation index ΔPj and standard deviation of tooth deviation ΔPc to obtain a tooth deviation index YPZ to quantify the relationship between tooth arrangement and occlusion. A larger YPZ value indicates that the patient's tooth deviation is severe, and there may be obvious irregular arrangement or occlusion problems. A smaller YPZ value indicates that the patient's teeth are relatively neat and the occlusion is relatively normal, and reflects the overall degree of deviation between the tooth arrangement and occlusion.

[0094] The tooth deviation index YPZ is obtained by the following calculation formula:

[0095] ;

[0096] Where z1 and z2 represent the preset weight values of the mean index of tooth deviation △Pj and the standard deviation of tooth deviation △Pc, respectively, and z1+z2=1. The specific value is set by the user.

[0097] In this embodiment, the coordinate information Pt of each tooth in the tooth position feature set Ft is substituted into a preset calibration model for comparison using the Euclidean distance method, accurately calculating the position deviation ratio ΔPt of each tooth, and integrating these deviation data into the tooth deviation vector Dt. This process can accurately reflect the deviation of each tooth from the ideal arrangement position, providing a solid data foundation for subsequent deviation statistical analysis. Next, by statistically analyzing the position deviation ratio ΔPt of each tooth in the tooth deviation vector Dt, the tooth deviation average index ΔPj and tooth deviation standard deviation ΔPc are generated, reflecting the overall degree of deviation between the tooth arrangement and occlusion relationship. Through weighted calculation, the tooth deviation index YPZ is further obtained. This index quantifies the tooth arrangement and occlusion relationship, facilitating clinical evaluation and diagnosis. This not only improves the accuracy of oral image analysis, but also provides an objective and quantifiable evaluation method through the quantitative calculation of the tooth deviation index YPZ, enabling the rapid and accurate diagnosis of tooth arrangement and occlusion problems. Unlike traditional manual judgment methods, this method avoids the interference of human error by combining the mean tooth deviation index ΔPj and the standard deviation of tooth deviation ΔPc, ensuring accurate reflection of the degree of tooth deviation. By accurately quantifying YPZ, the degree of abnormality in tooth arrangement and occlusion can be clearly indicated, providing a scientific basis for subsequent treatment plans, greatly improving the accuracy of clinical decision-making and treatment effectiveness. In addition, the use of a weighted calculation method makes the assessment of the tooth deviation index YPZ more flexible, and the weight parameters can be adjusted according to specific clinical needs, further improving the applicability and accuracy of the method.

[0098] Example 4

[0099] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: S5 includes S51 and S52;

[0100] S51. Obtain an image oral cavity suggestion evaluation scheme by comparing the tooth deviation index YPZ with a preset clinical statistical deviation threshold LZ. The image oral cavity suggestion evaluation scheme is obtained by the following comparison method:

[0101] When the tooth deviation index YPZ ≤ the clinical statistical deviation threshold LZ, obtain an oral evaluation plan with images showing normal tooth arrangement and occlusion;

[0102] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to adjust the observation frequency, and marking the oral image source patient evaluation mark as BJ=2;

[0103] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*150%, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to undergo orthodontic treatment, and marking the oral image source patient evaluation mark as BJ=5;

[0104] When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*200%, an oral evaluation plan with image-based tooth arrangement and occlusion warning is obtained, prompting the patient to perform tooth arrangement correction and tooth occlusion adjustment, and marking the oral image source patient evaluation mark as oral evaluation mark BJ=9.

[0105] S52. Collect feedback information based on the content of the oral image suggestion assessment plan, including feedback information collected from patient follow-up records and feedback information collected through the mini-program questionnaire. The time period for collecting feedback information is scaled using the oral assessment marker BJ, and steps S1 to S4 are performed on the feedback information to obtain a feedback index FZ. The error index E is then calculated with the tooth deviation index YPZ. The tooth deviation index YPZ and the clinical statistical deviation threshold LZ are iteratively adjusted using the gradient descent method of the back-propagation mechanism, including scaling the tooth deviation index YPZ and the clinical statistical deviation threshold LZ by the error index E.

[0106] The error index E is specifically calculated by comparing the feedback index FZ with the previously calculated tooth deviation index YPZ. The error index E represents the difference between the feedback information and the current deviation, and is a measure of the deviation adjustment process. At this time, the system uses the backpropagation mechanism and gradient descent method to further adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0107] In this embodiment, by comparing the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ, the patient's tooth arrangement and occlusion status can be accurately identified. By setting multiple critical values, when the tooth deviation index YPZ exceeds different critical thresholds, the evaluation plan can be gradually adjusted, from prompting slight abnormalities to recommending tooth correction or warning of serious problems. This multi-level evaluation mechanism can provide flexible and personalized oral health advice when different degrees of deviation occur, and timely adjust the patient's evaluation mark BJ, making the evaluation results more accurate and dynamic. More importantly, by collecting feedback information such as patient follow-up records and mini-program questionnaires, the understanding of the patient's oral health status is further deepened, and the feedback data is processed using the feedback index FZ. By bringing this feedback information into steps S1 to S4 for reprocessing, and comparing it with the tooth deviation index YPZ to calculate the error index E, the tooth deviation index YPZ and the clinical statistical deviation threshold LZ are continuously adjusted, and the gradient descent method of the backpropagation mechanism is used for iterative optimization. This process ensures the continuous refinement of oral health assessment plans, which can reflect changes in the patient's oral health in real time, and ensure that each treatment or prevention recommendation can be optimized and adjusted as the patient's condition changes.

[0108] Example 5

[0109] An intelligent analysis system for oral images, please refer to Figure 2 ,Specifically: including image processing module, feature extraction module, deviation calculation module, evaluation module and feedback iteration module;

[0110] The image processing module performs denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral cavity image to obtain a processed oral cavity image;

[0111] The feature extraction module uses edge detection algorithm and image segmentation method to extract the contour and position of the teeth from the processed oral image, obtains the coordinate information Pt and contour data Ct of each tooth, and forms the tooth position feature set Ft;

[0112] The deviation calculation module compares the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt;

[0113] The evaluation module performs statistical analysis based on the tooth deviation vector Dt to obtain the tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship;

[0114] The feedback iteration module compares the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ to obtain the image oral suggestion evaluation plan, collects feedback information based on the content of the image oral suggestion evaluation plan, and then uses the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

[0115] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis method for oral images, characterized by: The following steps are involved: S1, performing denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral image to obtain a processed oral image; S2. Use edge detection algorithm and image segmentation method to extract the outline and position of the teeth from the processed oral image, obtain the coordinate information Pt and outline data Ct of each tooth, and form the tooth position feature set Ft; S3. Compare the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt; S4. Statistically analyzing the tooth deviation vector Dt to obtain a tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship; S5. Obtain an image oral suggestion evaluation plan by comparing the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ, collect feedback information based on the content of the image oral suggestion evaluation plan, and then use the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

2. The intelligent analysis method for oral images according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, performing denoising preprocessing on the collected oral image, including using Gaussian filtering to remove noise in the oral image, and obtaining a filtered oral image; S12. Perform contrast enhancement preprocessing on the filtered oral image using histogram equalization, and then sharpen the oral image using a Laplace operator to highlight edge information in the oral image, thereby obtaining a sharpened oral image.

3. The intelligent analysis method for oral images according to claim 2, characterized in that: Said S2 includes S21 and S22; S21. Use an edge detection algorithm to identify the edges of teeth in the oral image after sharpening, including using a Canny edge detection algorithm to identify the edges of teeth in the oral image, separate the teeth from the background in the oral image, and obtain a tooth edge image Ee.

4. The intelligent analysis method for oral images according to claim 3, characterized in that: S22. Use image segmentation methods to extract the contours and positions of the teeth from the edge image Ee, including using a threshold segmentation method to segment the edge image Ee, using the white area in the edge image Ee to mark the specific area of the teeth, obtaining the segmented tooth image S, and then using contour tracking and boundary detection algorithms to extract the edge coordinates of each tooth from the tooth image S to generate contour data Ct. Then, perform geometric calculations on the contour data Ct to obtain the center coordinates of each tooth, generate coordinate information Pt, and form the tooth position feature set Ft.

5. The intelligent analysis method for oral images according to claim 4, characterized in that: Said S3 includes S31; S31. Substitute the coordinate information Pt of each tooth in the tooth position feature set Ft into the preset calibration model and use the Euclidean distance method to calculate to obtain the position deviation ratio △Pt of each tooth, and simultaneously integrate them to form the tooth deviation vector Dt.

6. The intelligent analysis method for oral images according to claim 5, characterized in that: Said S4 includes S41 and S42; S41. By performing statistical calculation on the position deviation ratio △Pt of each tooth in the tooth deviation vector Dt, the average value of the tooth deviation is obtained, which is marked as the tooth deviation average index △Pj. Then, the degree of dispersion of the position deviation ratio △Pt of each tooth is analyzed, which is marked as the tooth deviation standard deviation △Pc.

7. The intelligent analysis method for oral images according to claim 6, characterized in that: S42. Perform weighted calculation based on the obtained tooth deviation average index △Pj and tooth deviation standard deviation △Pc to obtain the tooth deviation index YPZ, quantify the relationship between tooth arrangement and occlusion, and reflect the overall deviation degree of the tooth arrangement and occlusion relationship.

8. The intelligent analysis method for oral images according to claim 1, characterized in that: Said S5 includes S51 and S52; S51. Obtain an image oral cavity suggestion evaluation scheme by comparing the tooth deviation index YPZ with a preset clinical statistical deviation threshold LZ. The image oral cavity suggestion evaluation scheme is obtained by the following comparison method: When the tooth deviation index YPZ ≤ the clinical statistical deviation threshold LZ, obtain an oral evaluation plan with images showing normal tooth arrangement and occlusion; When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to adjust the observation frequency, and marking the oral image source patient evaluation mark as BJ=2; When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*150%, an oral evaluation plan with dental arrangement and occlusion abnormalities is obtained, prompting the patient to undergo orthodontic treatment, and marking the oral image source patient evaluation mark as BJ=5; When the tooth deviation index YPZ is greater than the clinical statistical deviation threshold LZ*200%, an oral evaluation plan with image-based tooth arrangement and occlusion warning is obtained, prompting the patient to perform tooth arrangement correction and tooth occlusion adjustment, and marking the oral image source patient evaluation mark as oral evaluation mark BJ=9.

9. The intelligent analysis method for oral images according to claim 8, characterized in that: S52. Collect feedback information based on the content of the image oral recommendation evaluation plan, including feedback information collected from patient follow-up records and feedback information collected from mini-program questionnaires. The time period for collecting feedback information is scaled and adjusted using the oral evaluation mark BJ, and the feedback information is subjected to steps S1 to S4 to obtain the feedback index FZ, which is then combined with the tooth deviation index YPZ to calculate the error index E. The gradient descent method of the back-propagation mechanism is used to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, including scaling the error index E to the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

10. An intelligent analysis system for oral images, applied to the intelligent analysis method for oral images according to any one of claims 1 to 9, characterized in that: It includes image processing module, feature extraction module, deviation calculation module, evaluation module and feedback iteration module; The image processing module performs denoising preprocessing, contrast enhancement preprocessing and sharpening preprocessing on the collected oral cavity image to obtain a processed oral cavity image; The feature extraction module uses edge detection algorithm and image segmentation method to extract the contour and position of the teeth from the processed oral image, obtains the coordinate information Pt and contour data Ct of each tooth, and forms the tooth position feature set Ft; The deviation calculation module compares the tooth position feature set Ft with the preset calibration model to obtain the deviation value of each tooth and form a tooth deviation vector Dt; The evaluation module performs statistical analysis based on the tooth deviation vector Dt to obtain the tooth deviation index YPZ, which reflects the overall deviation degree of the tooth arrangement and occlusal relationship; The feedback iteration module compares the tooth deviation index YPZ with the preset clinical statistical deviation threshold LZ to obtain the image oral suggestion evaluation plan, collects feedback information based on the content of the image oral suggestion evaluation plan, and then uses the back propagation mechanism to iteratively adjust the tooth deviation index YPZ and the clinical statistical deviation threshold LZ.

Images