Oral health monitoring system based on laser irradiation and data processing method thereof

Abstract

The invention relates to the technical field of spectral data processing, in particular to an oral health monitoring system based on laser irradiation and a data processing method thereof, comprising: acquiring first spectral data of teeth and second spectral data of saliva in an oral region; obtaining a spectral residual contrast according to the characteristic distribution of each dimension of the spectral data of each band of each oral cavity region and the data deviation condition in a healthy state; according to the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data, combining the characteristic relevance of the first spectral data and the second spectral data to obtain a collaborative change index; according to the fluctuation condition of the collaborative change index, combining the spectral residual contrast of the first spectral data to obtain an abnormal attention degree; and monitoring the tooth health condition of each oral cavity region based on the abnormal attention. According to the invention, the recognition precision of the oral health state is improved, and an auxiliary diagnosis result with a good diagnosis effect is provided for medical personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral data processing, and in particular to an oral health monitoring system based on laser irradiation and a data processing method thereof. Background Art

[0002] Oral health not only affects an individual's chewing, pronunciation, and aesthetic appearance, but is also closely related to overall health. Traditional oral examination methods rely primarily on clinical observation and imaging methods such as X-rays, which are invasive, subject to significant subjective influence by the operator, and carry radiation risks. Lasers and related spectral technologies have rapidly developed in medical testing in recent years, offering advantages such as high sensitivity, good real-time performance, non-invasiveness, and portability. Using lasers and spectral data analysis technology, rapid, objective, and non-invasive detection of pathological information in oral hard tissues (such as teeth) and soft tissue environments (such as saliva) can be achieved, enabling early diagnosis, risk assessment, and personalized health management.

[0003] In existing technologies, oral health monitoring based on laser irradiation primarily uses laser-induced fluorescence and reflectance spectroscopy to collect spectral data from teeth and saliva in real time and analyze lesion characteristics, thereby achieving early non-invasive diagnosis and health assessment. However, within the oral cavity, different components (such as water, proteins, enzymes, immune molecules, metabolites, and trace elements) contribute significantly to the spectral response. Furthermore, under different health states, the concentrations and proportions of these biochemical components typically vary very slightly, resulting in subtle changes in the corresponding spectral characteristics. This makes current feature extraction algorithms inaccurate in identifying oral health status, and consequently, their effectiveness in assisting oral health monitoring is poor. Summary of the Invention

[0004] In order to address the technical problem that existing methods fail to consider the coordinated changes in oral components, are insufficient in the accuracy of oral health status identification, and are therefore less effective in assisting oral health status monitoring, the present invention aims to provide an oral health monitoring system based on laser irradiation and a data processing method thereof. The technical solutions adopted are as follows: In a first aspect, the present invention provides a data processing method for an oral health monitoring system based on laser irradiation, comprising: Acquire spectral data of each oral region at different wavelengths, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva in the same oral region; According to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation in the healthy state, the spectral residual contrast of the spectral data of each oral region in each band is obtained; Based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension, a synergistic change index of the first spectral data and the second spectral data in each band in each oral region is obtained; According to the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band, the abnormal attention level of the first spectral data in each band in each oral region is obtained; The dental health status of each oral region is monitored based on the abnormal concerns.

[0005] Preferably, obtaining the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation in a healthy state specifically includes: According to the difference in the distribution of each dimension feature between the spectral data of each oral region in each band and the spectral data of the same oral region in the same band in a healthy state, the spectral deviation value corresponding to the spectral data of each oral region in each band is obtained; Based on the balanced distribution of spectral deviation values ​​corresponding to the same spectral data in each band in all oral regions, a balanced characteristic value in each band is determined; Based on the difference ratio between the spectral deviation value corresponding to the spectral data of each oral area in each band and the balanced characteristic value of the corresponding spectral data in the same band, the spectral residual contrast of the spectral data of each oral area in each band is determined.

[0006] Preferably, the spectral deviation value corresponding to the spectral data of each oral region in each band is obtained based on the difference in the characteristic distribution of each dimension between the spectral data of each oral region in each band and the spectral data of the same oral region in the same band in a healthy state, specifically including: For any spectral data of any oral region in any wavelength band, obtain the peak data, peak wavelength, peak width and peak area of ​​the spectral data, and construct a characteristic distribution sequence of the spectral data; The difference distance between the characteristic distribution sequence of each spectral data of each oral area in each band and the characteristic distribution sequence of the same spectral data of the same oral area in the same band in a healthy state is taken as the spectral deviation value corresponding to the spectral data of each oral area in each band.

[0007] Preferably, the synergistic change index of the first spectral data and the second spectral data in each oral region in each band is obtained based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension, specifically including: According to the difference in spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, the drift consistency degree between different bands of each oral region is obtained; According to the similarity between the characteristic distribution sequence of the first spectral data of each wavelength band and the characteristic distribution sequence of the second spectral data of each wavelength band in the same oral region, the spectral correlation degree between different wavelength bands of each oral region is obtained; The product of the drift consistency degree and the spectral correlation degree is determined as a coordinated change index of the first spectral data and the second spectral data in each band in each oral region.

[0008] Preferably, obtaining the drift consistency degree between different bands of each oral region according to the difference in spectral residual contrast between the first spectral data and the second spectral data at different bands in each oral region specifically includes: For any oral region, the first spectrum data in any wavelength band is recorded as the first characteristic spectrum, and the second spectrum data in any wavelength band is recorded as the second characteristic spectrum; The drift consistency between the first characteristic spectrum and the second characteristic spectrum is determined based on a negative correlation coefficient of a difference between a spectral residual contrast corresponding to the first characteristic spectrum and a spectral residual contrast corresponding to the second characteristic spectrum.

[0009] Preferably, obtaining the spectral correlation degree between different bands of each oral region based on the similarity between the characteristic distribution sequence of the first spectral data of each band and the characteristic distribution sequence of the second spectral data of each band in the same oral region specifically includes: The Pearson correlation coefficient between the characteristic distribution sequence corresponding to the first characteristic spectrum and the characteristic distribution sequence corresponding to the second characteristic spectrum is used as the spectral correlation degree between the first characteristic spectrum and the second characteristic spectrum.

[0010] Preferably, the abnormal attention level of the first spectral data of each oral region in each band is obtained based on the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band, and specifically includes: Obtaining the abnormal credibility between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region according to the degree of deviation between the synergistic change index between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region and the overall distribution of all oral regions; Obtaining a response deviation degree of the first spectral data of each oral region in each band according to the abnormal credibility and a coordinated change index between the first spectral data of each band and the second spectral data of all bands in each oral region; The product of the response deviation degree and the spectral residual contrast of the first spectral data of the corresponding oral region in the same band is used as the abnormal attention level of the first spectral data of each oral region in each band.

[0011] Preferably, obtaining the abnormal credibility between the first spectral data of each band and the second spectral data of each band in each oral region based on the degree of deviation between the coordinated change index between the first spectral data of each band and the second spectral data of each band in each oral region and the overall distribution of all oral regions specifically includes: Any oral region is used as the selected oral region, and any two bands are used as the first band and the second band respectively; Calculating the mean of the synergistic variation index between the first spectral data of the first waveband and the second spectral data of the second waveband in all oral regions to obtain a synergistic characteristic value; The difference between the collaborative change index between the first spectral data of the first band and the second spectral data of the second band in the selected oral region and the collaborative characteristic value is used as the abnormality credibility between the first spectral data of the first band and the second spectral data of the second band in the selected oral region.

[0012] Preferably, obtaining the response deviation degree of the first spectral data of each oral region in each band according to the abnormal credibility and the coordinated change index between the first spectral data of each band in each oral region and the second spectral data of all bands specifically includes: For the selected oral region, normalizing the synergistic variation index between the first spectral data of the first band and the second spectral data of each band to obtain the feature weight of the second spectral data of each band; The characteristic weights are used to perform weighted averaging on the abnormal credibility between the first spectral data of the first band and the second spectral data of each band to obtain the response deviation degree of the first spectral data of the selected oral region in the first band.

[0013] In a second aspect, the present invention provides an oral health monitoring system based on laser irradiation, which is used to implement the steps of a data processing method of an oral health monitoring system based on laser irradiation. The oral health monitoring system based on laser irradiation includes: A data acquisition module, configured to acquire spectral data of each oral region at different wavelengths, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva within the same oral region; The residual comparison module is used to obtain the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation under the healthy state; A collaborative analysis module is configured to obtain a collaborative change index of the first spectral data and the second spectral data in each oral region at each wavelength based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data at different wavelengths in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension; An anomaly analysis module is used to obtain the abnormal attention level of the first spectral data of each oral region in each band based on the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band; A health monitoring module is used to monitor the dental health status of each oral area based on the abnormal attention level.

[0014] The embodiments of the present invention have at least the following beneficial effects: The present invention first collects local data in the oral area, providing a data basis for the subsequent analysis of the synergistic relationship between teeth and saliva in different local areas and different bands. Then, on the first hand, the deviation between the spectral data actually collected and the spectral data in a healthy state is analyzed, and the abnormalities and deviations of the spectral data actually collected are preliminarily quantified to obtain the spectral residual contrast. On the second hand, the differences and correlations of the deviations of the spectral data of teeth and saliva in each band are analyzed, and the synergistic change relationship of the spectral signal changes between teeth and saliva in each band in the local oral area is evaluated. Through matching analysis, a single data source can be mutually verified to reduce errors caused by single data noise or individual differences. Finally, the analysis results of the synergistic change relationship and the comparison results of the deviation are combined to evaluate the abnormalities corresponding to the teeth, obtain abnormal attention, improve the recognition accuracy of the oral health status, and provide medical personnel with auxiliary diagnosis results with better diagnostic effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flowchart of the steps of a data processing method of an oral health monitoring system based on laser irradiation provided by the present invention; Figure 2 is a schematic diagram of a local comparison between the first spectral data and the healthy baseline provided by the present invention; Figure 3 1 is a flowchart of the steps of the method for obtaining spectral residual contrast provided by the present invention; Figure 4 is a flowchart of the steps of the method for obtaining the coordinated change index provided by the present invention; Figure 5 This is a flowchart of the steps of the method for obtaining abnormal attention provided by the present invention; Figure 6 This is a module schematic diagram of an oral health monitoring system based on laser irradiation provided by the present invention. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, characteristics and effects of an oral health monitoring system based on laser irradiation and its data processing method proposed by the present invention.

[0018] Before introducing the specific solutions provided in the embodiments of the present application, some of the terms in the present application are explained to facilitate understanding by those skilled in the art, and are not intended to limit the use in the present application.

[0019] The data collection process for oral laser irradiation is as follows: 1. Instrument preparation: The laser excitation source and spectrometer have a pulsed output mode and an adjustable power of 1-10mW. The spectrometer has a resolution of ≤1nm and a detection range of 400–900nm. The probe is a two-in-one fiber optic probe with a switchable focusing lens for alignment with the tooth surface or salivary membrane. The positioning device uses an adjustable XYZ three-axis translation stage or an intraoral positioning fixture to ensure consistent position during repeated measurements. The controller triggers the laser and synchronizes the spectrometer acquisition.

[0020] 2. Tooth spectral data collection: Patient preparation: rinse with water, wipe the tooth surface dry (or blow dry) to remove saliva residue, use a mouth mirror or retractor to fix the soft tissue and expose the scanning area.

[0021] Probe alignment: Keep the fiber optic probe perpendicular to the tooth surface, with a distance of about 2 mm to 3 mm, and cover the entire tooth surface in a grid or line scanning manner (such as moving once every 1 mm).

[0022] Laser irradiation and signal acquisition: Continuous irradiation with 655 nm laser, 10 ms pulse width, 100 ms interval, and spectrometer integration time set to 50 ms to 100 ms. The data were collected three times and averaged to reduce random errors. The original spectrum (wavelength, intensity) was saved, and the scanning position was recorded by taking photos.

[0023] Data annotation: Associate each set of spectra with the tooth number and scanning coordinates.

[0024] Saliva sample preparation: A small amount of saliva (approximately 10–20 μL) is obtained from the natural secretion of the mouth, and the sample is collected immediately after lightly touching the gums at different target areas. The sample can be directly formed into a thin film on a glass slide or placed in the detection groove of a microfluidic chip.

[0025] Probe positioning and laser irradiation: Use the same laser source or switch to a 405 nm laser source. Lightly touch the probe to the surface of the saliva film, maintaining contact with the sample without squeezing it. Lower the laser power to 1 mW to 5 mW to avoid sample flickering and thermal effects.

[0026] In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0027] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0028] The following describes in detail a laser irradiation-based oral health monitoring system and a data processing method thereof provided by the present invention in conjunction with the accompanying drawings.

[0029] See also Figure 1 , which shows a flowchart of a data processing method for an oral health monitoring system based on laser irradiation provided by one embodiment of the present invention, the method comprising the following steps: Step S100 , obtaining spectral data of each oral region in different wavelength bands, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva in the same oral region.

[0030] First, based on the human oral structure, the oral cavity is divided into several functional areas. As a specific example, each tooth can correspond to an oral area, providing a data basis for the subsequent feature analysis process of data anomalies of different teeth.

[0031] Within the same oral region, the distribution of spectral data for teeth and saliva can be obtained separately. As a specific example, this embodiment obtains first spectral data for teeth and second spectral data for saliva within the 400nm to 900nm wavelength range. This wavelength range is evenly divided into multiple different bands, each with the same wavelength range. For example, a wavelength range of 100nm can be used as an example, where each wavelength range corresponds to a band. In this embodiment, a total of five different bands are included. It should be understood that this embodiment collects spectral data for teeth and saliva using laser-induced fluorescence and reflectance spectroscopy, a technique well known to those skilled in the art and not further described here.

[0032] At this point, two types of spectral data can be obtained in each oral region under each wavelength band, one is the spectral data of the teeth, recorded as the first spectral data in this embodiment, and the other is the spectral data of the saliva, recorded as the second spectral data.

[0033] Step S200 , obtaining the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation in a healthy state.

[0034] When there are oral health problems, the spectral responses of various components in the oral cavity are abnormal, which can assist in the diagnosis of oral health. In the process of extracting abnormal components in the oral cavity based on spectral data, the most significant deviation from the healthy baseline often corresponds to early pathological changes or potential difference information. For example, a local comparison diagram of the first spectral data of a tooth and the corresponding healthy baseline spectral data is shown in the figure below. Figure 2 As shown in Figure 2, by comparing the actual spectrum obtained with the healthy baseline, the degree of deviation of each component is obtained. Figure 2 The horizontal axis is the wavelength, the vertical axis is the signal intensity of the spectral data, the dotted line is the healthy baseline spectral curve, and the solid line is the spectral data with oral problems, which can reflect the differences in spectral data between corresponding bands.

[0035] As a specific example, Figure 3 As shown, the method for obtaining the spectral residual contrast can be implemented by steps S201 to S203.

[0036] Step S201 , obtaining a spectral deviation value corresponding to the spectral data of each oral region in each band based on the differences in the characteristic distribution of each dimension between the spectral data of each oral region in each band and the spectral data of the same oral region in the same band in a healthy state.

[0037] First, it is necessary to obtain the healthy oral data of the human body as a basis for data comparison and analysis. In practice, different normal databases can be constructed for the healthy oral cavity of different age groups. It should be understood that for the dental department or dental hospital, the internal system stores the relevant information of the patient. The spectral data of patients of the same age group in the healthy state of the teeth are obtained and counted through the information system. For each same oral area, the mean of the tooth spectral data of all healthy samples of the same age group is calculated to form the first healthy data of the teeth in the corresponding oral area. Similarly, the mean of the saliva spectral data of all healthy samples of the same age group is calculated to form the second healthy data of the saliva in the corresponding oral area. It should be noted that the age group can enable the relevant medical personnel to be divided according to the specific implementation scenario.

[0038] At this point, each oral region corresponds to a set of healthy spectral data in each wavelength band, including healthy spectral data corresponding to teeth (i.e., first healthy data) and healthy spectral data corresponding to saliva (i.e., second healthy data). For ease of description, in this embodiment, feature analysis is performed based on the data of a single patient. The patient's age group has been determined, and the corresponding first and second healthy data are retrieved from the database for analysis.

[0039] The first step is to obtain the peak data, peak wavelength, peak width and peak area of ​​any spectral data of any oral area in any band, and construct a characteristic distribution sequence of the spectral data.

[0040] The peak data refers to the value of the maximum signal intensity on the selected spectrum data, and the peak width refers to the half-peak width of the peak on the selected spectrum, that is, the width of the peak at half the position of the peak data. The specific acquisition method can obtain the signal intensity equal to the peak data. The length of the interval between the two wavelengths corresponding to the signal intensity is taken as the peak width. The peak area can be calculated by integration, which is a well-known technique and will not be described in detail here.

[0041] It should be understood that each type of spectral data in each band in each oral area is feature extracted using the same method. At the same time, the spectral data in a healthy state also needs to be feature extracted using the same method to obtain the characteristic distribution sequence of each type of spectral data in each band in each oral area.

[0042] In the second step, the difference between the characteristic distribution sequence of each spectral data of each oral area in each band and the characteristic distribution sequence of the same spectral data of the same oral area in the same band in a healthy state is used as the spectral deviation value corresponding to the spectral data of each oral area in each band.

[0043] In this embodiment, the analysis process is introduced by taking the oral characteristic data of the teeth and saliva corresponding to any oral region as an example. Specifically, the first spectral data of the i-th band and the second spectral data of the i-th band contained in any oral region are used as an example for explanation.

[0044] For the first spectral data corresponding to the tooth, the DTW distance of the characteristic distribution sequence between the first spectral data of the i-th band and the first healthy data of the same oral area in the i-th band is calculated as the spectral deviation value corresponding to the first spectral data of the i-th band.

[0045] For the second spectral data corresponding to saliva, the DTW distance of the characteristic distribution sequence between the second spectral data of the i-th band and the second healthy data of the same oral region in the i-th band is calculated as the spectral deviation value corresponding to the second spectral data of the i-th band.

[0046] It should be noted that the calculation method for the spectral deviation value of each type of spectral data is the same. The spectral deviation value represents the characteristic difference and degree of characteristic deviation between each type of spectral data and the corresponding spectral data in a healthy state.

[0047] Step S202 : determining a balanced characteristic value in each waveband based on the balanced distribution of the spectrum deviation values ​​corresponding to the same spectrum data in all oral regions in each waveband.

[0048] For the first spectral data of teeth in the oral area, the mean of the spectral deviation values ​​corresponding to the first spectral data of all oral areas in the i-th band is used as the equilibrium eigenvalue corresponding to the first spectral data in the i-th band, which represents the overall distribution of the deviation of the first spectral data of teeth from the healthy state in all oral areas of the entire mouth in the same band.

[0049] It should be understood that the second spectral data of saliva is also calculated and analyzed using the same method, and the mean of the spectral deviation values ​​corresponding to the second spectral data of all oral areas under the i-th band is used as the equilibrium characteristic value corresponding to the second spectral data under the i-th band, which represents the overall distribution of the deviation of the second spectral data of saliva from the healthy state in all oral areas of the entire mouth under the same band.

[0050] Step S203 , determining the spectral residual contrast of the spectral data of each oral region in each band based on the difference ratio between the spectral deviation value corresponding to the spectral data of each oral region in each band and the balanced eigenvalue of the corresponding spectral data in the same band.

[0051] By comparing the laser spectra of multiple areas in the mouth of the same individual, the spectrum of each tooth of the same individual and each band of the saliva spectrum obtained in each area are compared with the saliva spectra of other teeth and other areas, the local pathological signal is amplified and the residual contrast of each band is extracted.

[0052] For the first spectral data of the teeth in the oral area, the absolute value of the difference between the spectral deviation value corresponding to the first spectral data in the i-th band and the balanced eigenvalue corresponding to the first spectral data in the i-th band is calculated, and the ratio of the absolute value of the difference to the balanced eigenvalue is used as the difference ratio, which is recorded as the spectral residual contrast of the first spectral data in the i-th band. It can be expressed as follows: ; in, represents the spectral residual contrast of the first spectral data of the oral region in the i-th band, represents the spectral deviation value of the first spectral data of the oral region in the i-th band, Represents the balanced eigenvalue of the first spectral data of the oral region in the i-th band.

[0053] It should be understood that the calculation method of the spectral residual contrast corresponding to the second spectral data of saliva in each band in the oral area is the same as the calculation method of the spectral residual contrast corresponding to the first spectral data of the teeth in each band in the oral area. It is only necessary to replace the data involved in the calculation with the relevant data in the same dimension of the corresponding type of spectral data.

[0054] The spectral residual contrast corresponding to each spectral data in each band in the oral area represents the residual distribution of the difference between each spectral data and the overall characteristic distribution of the oral cavity, which can preliminarily reflect the degree of expression of local pathological signals.

[0055] Step S300, based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral area, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension, obtain the collaborative change index of the first spectral data and the second spectral data in each band in each oral area.

[0056] In oral spectra, signals from major components like water and protein often occupy the majority of the dynamic range. Pathologically-induced trace metabolites or mineral changes can be drowned out by the large signal background. Therefore, collaborative analysis of tooth and saliva spectra is required to determine the presence of pathological abnormalities, providing greater confidence than observing either spectral signal alone. Therefore, it is necessary to combine data from both teeth and saliva within the same oral region to analyze the synergistic relationship between the spectra in each band.

[0057] Based on this, Figure 4 As shown, the method for analyzing the coordinated change index of teeth and saliva in each oral region in each wavelength band can be implemented by steps S301 to S303.

[0058] Step S301 : obtaining the drift consistency degree between different wavebands of each oral region according to the difference in spectral residual contrast between the first spectral data and the second spectral data in different wavebands of each oral region.

[0059] When oral problems are present, the spectral responses of multiple bands deviate, and the residual contrast of the oral spectral data bands should change accordingly. If there is a mutual influence between the bands, the changes in their spectral residual contrast will also be correlated. The spectral residual contrast between bands is used to obtain the consistency of spectral drift between the bands of tooth and saliva spectral data.

[0060] Specifically, for any oral area, the first spectral data under any band is recorded as the first characteristic spectrum, and the second spectral data under any band is recorded as the second characteristic spectrum; as a specific example, the first spectral data of the teeth in the x-th oral area in the n-th band is recorded as the first characteristic spectrum, and the second spectral data of the saliva in the x-th oral area in the m-th band is recorded as the second characteristic spectrum. It should be understood that n and m are both serial numbers of the bands, and the values ​​can be the same or different, and are used to represent the distribution of data differences between different types of spectral data corresponding to the same or different bands in the same oral area.

[0061] Furthermore, based on the negative correlation coefficient of the difference between the spectral residual contrast corresponding to the first characteristic spectrum and the spectral residual contrast corresponding to the second characteristic spectrum, the degree of drift consistency between the first characteristic spectrum and the second characteristic spectrum is determined. As a specific example, the degree of drift consistency between the first spectral data of the teeth in the nth band and the second spectral data of the saliva in the mth band in the xth oral region can be expressed as: ; in, It represents the drift consistency between the first spectral data of the teeth in the nth band and the second spectral data of the saliva in the mth band in the xth oral region, that is, the drift consistency between the first characteristic spectrum and the second characteristic spectrum in the xth oral region. It represents the spectral residual contrast of the first spectral data of the x-th oral region in the n-th band, that is, the spectral residual contrast of the first characteristic spectrum of the x-th oral region. It represents the spectral residual contrast of the second spectral data of the xth oral region in the mth band, that is, the spectral residual contrast of the second characteristic spectrum of the xth oral region. Represents an exponential function with the natural constant e as the base, using The differences are negatively correlated in the form of .

[0062] The difference in spectral residual contrast between the first characteristic spectrum and the second characteristic spectrum is expressed as The smaller the difference from 1, the closer the spectral residual contrast between the two is. In other words, the smaller the difference, the more consistent the change in signal deviation, and the larger the corresponding drift consistency value. Drift consistency reflects the consistency of signal deviation changes in spectral data of teeth and saliva in various bands within the same oral region.

[0063] Step S302 , based on the similarity between the characteristic distribution sequence of the first spectral data of each band and the characteristic distribution sequence of the second spectral data of each band in the same oral region, obtain the spectral correlation degree between different bands of each oral region.

[0064] When oral health issues occur, they manifest as changes in the spectral characteristics of related bands. By linking related bands between different types of spectral data, the credibility of abnormal bands can be improved. Based on the spectral characteristics of each band in the oral spectral dataset, the correlation between the spectral bands of tooth and saliva data is obtained.

[0065] Specifically, the Pearson correlation coefficient between the characteristic distribution sequence corresponding to the first characteristic spectrum and the characteristic distribution sequence corresponding to the second characteristic spectrum is used as the degree of spectral correlation between the first characteristic spectrum and the second characteristic spectrum. The degree of spectral correlation reflects the degree of similarity between the characteristic distributions of the spectral data of teeth and saliva in various wavelength bands within the same oral region.

[0066] Step S303 : determining the product of the drift consistency degree and the spectral correlation degree as a collaborative change index of the first spectral data and the second spectral data in each band in each oral region.

[0067] The coordinated change relationship between the spectral data of teeth and saliva in the same oral area in each band is comprehensively evaluated through the characteristic correlation between the bands and the consistency of the signal deviation changes of teeth and saliva in the same oral area.

[0068] The greater the spectral correlation between the first characteristic spectrum and the second characteristic spectrum in the oral region, the more similar the data feature distributions between the first characteristic spectrum and the second characteristic spectrum are, which in turn means that the spectral data distributions of teeth and saliva in the corresponding bands are more similar. At the same time, the greater the drift consistency between the first characteristic spectrum and the second characteristic spectrum in the oral region, the higher the consistency of the signal deviation change between the first characteristic spectrum and the second characteristic spectrum, which in turn means that the deviation change of the spectral data of teeth and saliva in the corresponding bands is more similar. At this time, the synergistic change relationship between the first characteristic spectrum and the second characteristic spectrum in the oral region is stronger.

[0069] Step S400, based on the fluctuation of the collaborative change index of the first spectral data and the second spectral data in each oral area in each band, combined with the spectral residual contrast of the first spectral data in each band, obtain the abnormal attention level of the first spectral data in each oral area in each band.

[0070] The physiological functions and component distribution of teeth and saliva can reflect a certain data synergy in their spectral response. By simultaneously collecting spectral data from teeth and the surrounding saliva and analyzing their matching, the reliability of abnormality identification can be significantly improved when both exhibit mismatching anomalies or specific bands deviate from the healthy baseline. This can amplify weak but real pathological signals, making the extracted features more valuable as auxiliary references, thereby significantly improving the sensitivity and specificity of oral health monitoring.

[0071] Based on this, Figure 5 As shown, the method for obtaining the abnormal attention level of the first spectrum data of each oral region in each wavelength band can be implemented by steps S401 to S403.

[0072] Step S401, based on the degree of deviation between the collaborative change index between the first spectral data of each band in each oral area and the second spectral data of each band and the overall distribution of all oral areas, obtain the abnormal credibility between the first spectral data of each band in each oral area and the second spectral data of each band.

[0073] Comparing the covariation relationships between bands in the spectral data of different local areas of the mouth, the greater the difference in the covariation relationship in the problematic oral region, the higher the credibility of the spectral signal anomaly in the corresponding band, and in other words, the higher the degree of characteristic anomaly. Based on the differences in the covariation relationships between different regions, the credibility of the spectral signal anomaly of the band is obtained.

[0074] In the first step, any oral area is taken as the selected oral area, and any two bands are respectively taken as the first band and the second band; as a specific example, this embodiment takes the yth oral area as the selected oral area, the ith band as the first band, and the jth band as the second band. Based on the reason that the nth band and the mth band are the same, i and j are both serial numbers of the bands, which can be the same or different, and are subsequently used to represent the spectral data of the corresponding bands of teeth and saliva respectively.

[0075] In the second step, the mean value of the synergistic variation index between the first spectral data of the first band and the second spectral data of the second band in all oral regions is calculated to obtain a synergistic feature value.

[0076] In the third step, the difference between the collaborative change index between the first spectral data of the first band and the second spectral data of the second band in the selected oral region and the collaborative characteristic value is used as the abnormality credibility between the first spectral data of the first band and the second spectral data of the second band in the selected oral region.

[0077] As a specific example, the calculation formula of abnormal credibility can be expressed as: ,in, represents the abnormal credibility between the first spectrum data under the i-th band and the second spectrum data under the j-th band in the y-th oral region, represents the synergistic change index between the first spectral data under the i-th band and the second spectral data under the j-th band in the y-th oral region, It represents the mean value of the collaborative change index between the first spectral data under the i-th band and the second spectral data under the j-th band in all oral regions, that is, the collaborative feature value.

[0078] It reflects the difference in the coordinated change relationship between the first spectral data in the i-th band and the second spectral data in the j-th band in the y-th oral region and all oral regions as a whole. The larger the value, the more serious the deviation of the coordinated change relationship in the y-th region from the overall distribution, the greater the degree of abnormality, and the larger the corresponding abnormality credibility value.

[0079] Step S402 , obtaining the response deviation degree of the first spectral data of each oral region in each band according to the abnormal credibility and the coordinated change index between the first spectral data of each band in each oral region and the second spectral data of all bands.

[0080] When there are oral problems, the degree of abnormality in the dental spectral signals in the oral area is of greater reference value. Due to various interferences, the spectral characteristics may be relatively weak. The synergistic changes in the teeth and saliva in each band can be used to amplify the spectral abnormalities of the band and conduct a weighted comprehensive assessment of the dental abnormalities in each oral area.

[0081] In the first step, for the selected oral region, the synergistic variation index between the first spectral data of the first band and the second spectral data of each band is normalized to obtain the characteristic weight of the second spectral data of each band. The normalization method is well known in the art and will not be further described here.

[0082] In the second step, the feature weights are used to perform weighted averaging on the abnormal credibility between the first spectral data of the first band and the second spectral data of each band to obtain the response deviation degree of the first spectral data of the selected oral region in the first band.

[0083] The abnormal credibility of the spectral signal is weighted averaged based on the synergistic variation relationship between the spectral data corresponding to teeth and saliva. The closer the synergistic variation relationship between the two bands of spectral data, the more valuable the band deviation is for auxiliary reference. As a specific example, the degree of response deviation can be expressed as: ; in, Indicates the response deviation degree of the first spectrum data in the ith band within the yth oral region, represents the abnormal credibility between the first spectrum data under the i-th band and the second spectrum data under the j-th band in the y-th oral region, represents the synergistic change index between the first spectral data under the i-th band and the second spectral data under the j-th band in the y-th oral region, Indicates the number of bands, and Norm is the normalization function.

[0084] When the value of the synergistic change index is larger, it means that the synergistic relationship of the spectral signal change between the first spectral data in the first band and the second spectral data in the second band is stronger, and thus the reference value of the data anomaly corresponding to the first spectral data in the first band and the second spectral data in the second band is higher.

[0085] Step S403 : The product of the response deviation degree and the spectral residual contrast of the first spectral data of the corresponding oral region in the same band is used as the abnormality attention level of the first spectral data of each oral region in each band.

[0086] The response deviation of the first spectral data in the i-th band within the y-th oral region reflects the degree of abnormality in the spectral data of the teeth in the corresponding oral region in this band. The spectral residual contrast of the first spectral data in the i-th band within the y-th oral region reflects the degree of abnormality in the spectral data of the teeth in the corresponding oral region in this band compared to their healthy state. Combining these two aspects of the characteristic performance can more accurately characterize the degree of abnormality in the spectral signal of the teeth in the i-th band within the y-th oral region. The greater the degree of abnormality, the greater the corresponding abnormality attention, that is, the more important it is to focus on observing the teeth in this oral region to assist in oral diagnosis.

[0087] Step S500: monitoring the dental health status of each oral region based on the abnormal attention level.

[0088] The greater the value of the abnormal attention level of the first spectral data corresponding to the teeth in each band in each oral area, it means that there are certain abnormalities in the spectral signals of the teeth in the corresponding band in the oral area, and the attention level of the teeth in the oral area needs to be increased.

[0089] In some embodiments, a threshold value can be used to remind medical personnel to pay special attention to the teeth in the oral region, so as to achieve the purpose of auxiliary diagnosis of oral teeth using spectral data. As a specific example, the abnormal attention is normalized, and when the normalized abnormal attention is greater than or equal to the preset abnormal threshold, the medical personnel are reminded to pay special attention to the teeth in the oral region. When the normalized abnormal attention is less than the preset abnormal threshold, it means that the possibility of abnormality in the teeth in the oral region is small at this time, and it does not require too much attention, that is, no reminder operation is required. The abnormal threshold can be set to 0.7, and the implementer can set it according to the specific implementation scenario. It should also be noted that the normalization method is a well-known technology and will not be introduced in detail here.

[0090] In some embodiments, a neural network can also be used to output abnormal or normal results for teeth, providing medical personnel with data reference to assist in the formal diagnosis process. Specifically, as the abnormal attention value of the first spectral data corresponding to the teeth in each wavelength band within each oral region increases, the attention weight value in the corresponding dimension increases, and the neural network is used to directly output the healthy or abnormal state of the teeth in the corresponding oral region.

[0091] As a specific example, a hybrid architecture of convolutional neural networks and Transformer encoders is adopted: first, a convolutional layer is used to extract microscopic features from the oral spectral sequence, and then a multi-head self-attention Transformer module is used to adaptively assign attention weights to each band position, amplifying the band response most relevant to the lesion. Finally, after global pooling, a classification head is connected to realize the judgment of oral health or abnormality.

[0092] The first spectral data of teeth in all bands are input into the convolution layer after denoising, baseline correction and normalization. The convolution kernel slides between adjacent wavelength points, enabling the model to sensitively capture microscopic features, screen the local chemical composition or structure in each small band, and enhance the initial response to tiny pathological signals.

[0093] The convolution output is reshaped along the wavelength axis, positionally encoded, and then fed into a multi-layer Transformer encoder. Its multi-head self-attention mechanism automatically assigns weights to each wavelength position. During the self-attention calculation, the abnormal attention level of the first spectral data in each wavelength band, calculated above, is added as a bias term in the attention score. This automatically biases the attention weight toward wavelengths with greater diagnostic value during forward propagation. Specifically, self-attention assigns high weights only when certain wavelengths exhibit abnormalities in the collaborative analysis of tooth and saliva spectra, thereby eliminating incidental background and individual differences.

[0094] The Transformer's final output is an attention-weighted feature map of each band, which is aggregated into a fixed-dimensional semantic vector through global average pooling, eliminating positional differences. The nonlinear decision boundary between health and abnormality is learned through a multi-layer perceptron (MLP), and healthy or abnormal results are output to monitor oral health risks.

[0095] In summary, when analyzing oral health using laser spectral data, the concentrations and ratios of oral components vary slightly, resulting in subtle changes in spectral signatures, which in turn affects oral health monitoring. Therefore, the present invention, through collaborative analysis of tooth and saliva spectral data, extracts key features reflecting subtle biochemical and structural changes, enabling early, accurate, and noninvasive monitoring of oral health. This improves detection sensitivity and accuracy, reduces the risk of misdiagnosis, and provides a reliable basis for personalized oral health management.

[0096] By comparing with the standard spectrum under healthy conditions, it is possible to detect tiny changes in the proportion of components, provide early warning signals for early pathological conditions, and preliminarily screen out spectral areas that differ greatly from normal data; detailed analysis of different areas in the mouth (such as local areas of teeth and saliva) can accurately locate the distribution areas of abnormal components, and by calculating the spectral contrast between abnormal components and normal background, the degree of abnormality can be further quantified, thereby improving the clarity and reliability of the diagnostic signal; under oral health conditions, the biochemical components in dental hard tissues and saliva should show a certain degree of synergistic consistency. Through matching analysis, single data sources can be mutually verified to reduce errors caused by single data noise or individual differences, thereby effectively distinguishing between normal and abnormal states, reducing misdiagnosis rates, and improving the accuracy and robustness of oral health status monitoring.

[0097] like Figure 6 As shown, the present invention also provides an oral health monitoring system based on laser irradiation, which is used to implement the steps of a data processing method of an oral health monitoring system based on laser irradiation. The oral health monitoring system based on laser irradiation includes: A data acquisition module, configured to acquire spectral data of each oral region at different wavelengths, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva within the same oral region; The residual comparison module is used to obtain the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation under the healthy state; A collaborative analysis module is configured to obtain a collaborative change index of the first spectral data and the second spectral data in each oral region at each wavelength based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data at different wavelengths in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension; An anomaly analysis module is used to obtain the abnormal attention level of the first spectral data of each oral region in each band based on the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band; A health monitoring module is used to monitor the dental health status of each oral area based on the abnormal attention level.

[0098] Since the specific implementation process of a data processing method for an oral health monitoring system based on laser irradiation has been described in detail, it will not be repeated here.

[0099] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A data processing method for an oral health monitoring system based on laser irradiation, characterized in that: The method comprises the following steps: Acquire spectral data of each oral region at different wavelengths, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva in the same oral region; According to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation in the healthy state, the spectral residual contrast of the spectral data of each oral region in each band is obtained; Based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension, a synergistic change index of the first spectral data and the second spectral data in each band in each oral region is obtained; According to the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band, the abnormal attention level of the first spectral data in each band in each oral region is obtained; The dental health status of each oral region is monitored based on the abnormal concerns.

2. The data processing method of the oral health monitoring system based on laser irradiation according to claim 1, characterized in that: The method of obtaining the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation in a healthy state specifically includes: According to the difference in the distribution of each dimension feature between the spectral data of each oral region in each band and the spectral data of the same oral region in the same band in a healthy state, the spectral deviation value corresponding to the spectral data of each oral region in each band is obtained; Based on the balanced distribution of spectral deviation values ​​corresponding to the same spectral data in each band in all oral regions, a balanced characteristic value in each band is determined; Based on the difference ratio between the spectral deviation value corresponding to the spectral data of each oral area in each band and the balanced characteristic value of the corresponding spectral data in the same band, the spectral residual contrast of the spectral data of each oral area in each band is determined.

3. The data processing method of the oral health monitoring system based on laser irradiation according to claim 2, characterized in that: The method of obtaining the spectral deviation value corresponding to the spectral data of each oral region in each band based on the difference in the characteristic distribution of each dimension between the spectral data of each oral region in each band and the spectral data of the same oral region in the same band in a healthy state specifically includes: For any spectral data of any oral region in any wavelength band, obtain the peak data, peak wavelength, peak width and peak area of ​​the spectral data, and construct a characteristic distribution sequence of the spectral data; The difference distance between the characteristic distribution sequence of each spectral data of each oral area in each band and the characteristic distribution sequence of the same spectral data of the same oral area in the same band in a healthy state is taken as the spectral deviation value corresponding to the spectral data of each oral area in each band.

4. The data processing method of the oral health monitoring system based on laser irradiation according to claim 3, characterized in that: The method obtains a synergistic change index of the first spectral data and the second spectral data in each band in each oral region based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension, specifically including: According to the difference in spectral residual contrast between the first spectral data and the second spectral data in different bands in each oral region, the drift consistency degree between different bands of each oral region is obtained; According to the similarity between the characteristic distribution sequence of the first spectral data of each wavelength band and the characteristic distribution sequence of the second spectral data of each wavelength band in the same oral region, the spectral correlation degree between different wavelength bands of each oral region is obtained; The product of the drift consistency degree and the spectral correlation degree is determined as a collaborative change index of the first spectral data and the second spectral data in each band in each oral region.

5. The data processing method of the oral health monitoring system based on laser irradiation according to claim 4, characterized in that: The method of obtaining the drift consistency between different wavelength bands of each oral region according to the difference in the spectral residual contrast between the first spectrum data and the second spectrum data in different wavelength bands of each oral region specifically includes: For any oral region, the first spectrum data in any wavelength band is recorded as the first characteristic spectrum, and the second spectrum data in any wavelength band is recorded as the second characteristic spectrum; The drift consistency between the first characteristic spectrum and the second characteristic spectrum is determined based on a negative correlation coefficient of a difference between a spectral residual contrast corresponding to the first characteristic spectrum and a spectral residual contrast corresponding to the second characteristic spectrum.

6. The data processing method of the oral health monitoring system based on laser irradiation according to claim 5, characterized in that: The obtaining of the spectral correlation degree between different bands of each oral region based on the similarity between the characteristic distribution sequence of the first spectral data of each band and the characteristic distribution sequence of the second spectral data of each band within the same oral region specifically includes: The Pearson correlation coefficient between the characteristic distribution sequence corresponding to the first characteristic spectrum and the characteristic distribution sequence corresponding to the second characteristic spectrum is used as the spectral correlation degree between the first characteristic spectrum and the second characteristic spectrum.

7. The data processing method of the oral health monitoring system based on laser irradiation according to claim 1, characterized in that: The abnormal attention level of the first spectral data of each oral region in each band is obtained based on the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band, specifically including: Obtaining the abnormal credibility between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region according to the degree of deviation between the synergistic change index between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region and the overall distribution of all oral regions; Obtaining a response deviation degree of the first spectral data of each oral region in each band according to the abnormal credibility and a coordinated change index between the first spectral data of each band and the second spectral data of all bands in each oral region; The product of the response deviation degree and the spectral residual contrast of the first spectral data of the corresponding oral region in the same band is used as the abnormal attention level of the first spectral data of each oral region in each band.

8. The data processing method of the oral health monitoring system based on laser irradiation according to claim 7, characterized in that: The abnormality credibility between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region is obtained based on the degree of deviation between the coordinated change index between the first spectral data of each wavelength band and the second spectral data of each wavelength band in each oral region and the overall distribution of all oral regions, specifically including: Any oral region is used as the selected oral region, and any two bands are used as the first band and the second band respectively; Calculating the mean of the synergistic variation index between the first spectral data of the first waveband and the second spectral data of the second waveband in all oral regions to obtain a synergistic characteristic value; The difference between the collaborative change index between the first spectral data of the first band and the second spectral data of the second band in the selected oral region and the collaborative characteristic value is used as the abnormality credibility between the first spectral data of the first band and the second spectral data of the second band in the selected oral region.

9. The data processing method of the oral health monitoring system based on laser irradiation according to claim 8, characterized in that: Obtaining the response deviation degree of the first spectral data of each oral region in each band based on the abnormal credibility and the coordinated change index between the first spectral data of each band and the second spectral data of all bands in each oral region specifically includes: For the selected oral region, normalizing the synergistic variation index between the first spectral data of the first band and the second spectral data of each band to obtain the feature weight of the second spectral data of each band; The characteristic weights are used to perform weighted averaging on the abnormal credibility between the first spectral data of the first band and the second spectral data of each band to obtain the response deviation degree of the first spectral data of the selected oral region in the first band.

10. An oral health monitoring system based on laser irradiation, characterized in that: The system is used to implement the steps of a data processing method of a laser irradiation-based oral health monitoring system as described in any one of claims 1 to 9, wherein the laser irradiation-based oral health monitoring system comprises: A data acquisition module, configured to acquire spectral data of each oral region at different wavelengths, wherein the spectral data includes first spectral data of teeth and second spectral data of saliva within the same oral region; The residual comparison module is used to obtain the spectral residual contrast of the spectral data of each oral region in each band according to the characteristic distribution of each dimension of the spectral data of each oral region in each band and the data deviation under the healthy state; A collaborative analysis module is configured to obtain a collaborative change index of the first spectral data and the second spectral data in each oral region at each wavelength based on the difference distribution of the spectral residual contrast between the first spectral data and the second spectral data at different wavelengths in each oral region, combined with the correlation of the characteristic distribution of the corresponding first spectral data and the second spectral data in each dimension; An anomaly analysis module is used to obtain the abnormal attention level of the first spectral data of each oral region in each band based on the fluctuation of the coordinated change index of the first spectral data and the second spectral data in each band in each oral region, combined with the spectral residual contrast of the first spectral data in each band; A health monitoring module is used to monitor the dental health status of each oral area based on the abnormal attention level.

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