Oral-cavity age calculation formula derivation method, oral-cavity age calculation formula derivation program, oral-cavity age calculation formula derivation device, oral-cavity age calculation method, oral-cavity age calculation program, and oral-cavity age calculation device

Abstract

This oral-cavity age calculation formula derivation method involves a computer executing processing including: acquiring data on a plurality of subjects tested for oral-cavity age, the data including the actual ages of the plurality of subjects and saliva test results including at least one item obtained from a saliva test performed on the subjects; and deriving, as the oral-cavity age calculation formula, a regression formula calculated by performing a regression analysis in which the objective variable is the actual ages and the explanatory variable is the at least one item included in the saliva test results.

Description

Method for deriving an oral age calculation formula, program for deriving an oral age calculation formula, device for deriving an oral age calculation formula, oral age calculation method, oral age calculation program, and oral age calculation device.

[0001] This disclosure relates to a method for deriving an oral age calculation formula, a program for deriving an oral age calculation formula, an apparatus for deriving an oral age calculation formula, an oral age calculation method, an oral age calculation program, and an oral age calculation apparatus.

[0002] Japanese Patent Publication No. 2000-201953 describes how to obtain a first regression equation showing the relationship between actual age and the number of healthy teeth based on statistical data on the age-specific average number of healthy teeth, a second regression equation showing the relationship between actual age and the number of healthy gum sextants based on statistical data on the age-specific average number of healthy gum sextants, and a third regression equation showing the relationship between the actual age and two predicted ages calculated from the above two regression equations, and how to obtain a third regression equation showing the relationship between actual age and the number of healthy teeth X for each person. １ and the number of healthy gum sextants X ２ Based on the first and second regression equations, two predicted ages y1 and y2 are calculated for each person, and the two predicted ages y １ , y ２ A method for determining oral health is disclosed, which calculates an overall predicted age Y for each person based on the aforementioned third regression equation, and then determines the oral health condition of each person based on the difference between this overall predicted age Y and their actual age.

[0003] Japanese Patent Publication No. 2021-10343 discloses a method for evaluating or predicting the health status of a subject based on information about the abundance of bacteria selected from the phyla Actinobacteria, Bacteroidetes, Canidate division SR1, Firmicutes, Fusobacteria, Proteobacteria, and Spirochaetes contained in an oral sample taken from the subject, and the total amount of bacteria contained in the oral sample.

[0004] The technologies described in the above-mentioned Patent Documents 1 and 2 had the problem that predicting oral age was time-consuming.

[0005] The purpose of this disclosure is to provide a method for deriving an oral age calculation formula, a program for deriving an oral age calculation formula, a device for deriving an oral age calculation formula, an oral age calculation method, an oral age calculation program, and an oral age calculation device that can easily and quickly calculate oral age from the results of a saliva test.

[0006] To achieve the above objective, a method for deriving an oral age calculation formula according to one aspect of the present disclosure includes a process in which a computer acquires subject data including saliva test results that include at least one item obtained from saliva tests conducted on multiple subjects, and the actual ages of the multiple subjects, and performs regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable to derive a regression equation as an oral age calculation formula.

[0007] According to this disclosure, the oral age can be easily and quickly calculated from the results of a saliva test.

[0008] This is a block diagram showing the hardware configuration of the oral age calculation formula derivation device. This is a block diagram showing the functional configuration of the oral age calculation formula derivation device. This is a graph showing an example of the correspondence between oral age calculated by the oral age calculation formula and actual age. This is a graph showing an example of the correspondence between the prediction error between oral age calculated by the oral age calculation formula and actual age, and actual age. This is a graph showing an example of the correspondence between oral age calculated by the oral age calculation formula and actual age after correcting the oral age with actual age. This is a flowchart of the oral age calculation formula derivation process executed by the oral age calculation formula derivation program. This is a block diagram showing the hardware configuration of the oral age calculation device. This is a block diagram showing the functional configuration of the oral age calculation device. This is a flowchart of the oral age calculation process executed by the oral age calculation program. This is a diagram showing an example of how oral age is displayed.

[0009] Hereinafter, an example of an embodiment for carrying out the technology of this disclosure will be described in detail with reference to the drawings. Components and processes that perform the same operation, action, or function are given the same reference numerals throughout the drawings, and redundant explanations may be omitted as appropriate. Each drawing is only a schematic representation to the extent that the technology of this disclosure can be fully understood. Therefore, the technology of this disclosure is not limited to the illustrated examples. Furthermore, in this embodiment, explanations of configurations not directly related to this disclosure or well-known configurations may be omitted.

[0010] <Device for deriving the formula for calculating oral age>

[0011] Figure 1 shows the hardware configuration of the oral age calculation formula derivation device 10 according to this embodiment. As shown in Figure 1, the oral age calculation formula derivation device 10 includes a CPU (Central Processing Unit) 10A, a ROM (Read Only Memory) 10B, a RAM (Random Access Memory) 10C, and an input / output interface (I / O) 10D. The CPU 10A, ROM 10B, RAM 10C, and I / O 10D are connected to each other via a system bus 10E. The system bus 10E includes a control bus, an address bus, and a data bus.

[0012] CPU 10A is an example of a computer. Here, "computer" refers to a processor in a broad sense, including general-purpose processors (e.g., CPUs) or specialized processors (e.g., GPUs: Graphics Processing Units, ASICs: Application Specific Integrated Circuits, FPGAs: Field Programmable Gate Arrays, Programmable Logical Devices, etc.).

[0013] Furthermore, the I / O 10D is connected to the operation unit 11, the display unit 12, the communication unit 13, and the storage unit 14.

[0014] The operating unit 11 is comprised of, for example, a mouse and a keyboard.

[0015] The display unit 12 is composed of, for example, a liquid crystal display. Alternatively, the liquid crystal display may be configured as a touch panel, so that the display unit 12 also functions as the operation unit 11.

[0016] The communication unit 13 is an interface for data communication with external devices.

[0017] The memory unit 14 is composed of a non-volatile external storage device such as a hard disk, and stores the oral age calculation formula derivation program 15 and subject data 16, etc. The CPU 10A reads the oral age calculation formula derivation program 15 stored in the memory unit 14 into the RAM 10C and executes it.

[0018] The oral age calculation formula derivation program 15 may be stored on a non-volatile, non-transitory recording medium, or distributed via a network, and appropriately installed on the oral age calculation formula derivation device 10.

[0019] Examples of non-volatile, non-transition recording media include CD-ROMs (Compact Disc Read Only Memory), magneto-optical disks, HDDs (hard disk drives), DVD-ROMs (Digital Versatile Disc Read Only Memory), flash memory, and memory cards.

[0020] The CPU 10A functions as one of the functional units shown in Figure 2 by reading and executing the oral age calculation formula derivation program 15 stored in the memory unit 14.

[0021] Figure 2 is a block diagram showing the functional configuration of the CPU 10A. As shown in Figure 2, the CPU 10A functionally includes an acquisition unit 20 and an output unit 21.

[0022] The acquisition unit 20 acquires subject data 16, which includes saliva test results containing at least one item obtained from saliva tests conducted on multiple subjects, and the actual ages of multiple subjects, by reading them from the storage unit 14.

[0023] For the saliva test, known saliva tests are applied. For example, in a saliva test, first the mouth is rinsed with mouthwash for 10 seconds. Next, the rinsed liquid (mouthwash discharge) is dropped onto the test piece using a dropper. Then, the test piece is set in a saliva testing device and measured for at least one item. Alternatively, instead of dropping the mouthwash discharge onto the test piece with a dropper, the test piece may be dipped in the mouthwash discharge. Multiple reagent pads are attached to the test piece. Each reagent pad contains a reagent that reacts with different components in the mouthwash discharge containing saliva and develops a color corresponding to the concentration of the component. The person performing the saliva test drops the mouthwash discharge onto each reagent pad. When the mouthwash discharge is applied to the reagent pad, it changes color according to the type of reagent. The saliva testing device optically detects the color change. As for the test piece and saliva testing device, for example, those described in Patent Document 3 are known.

[0024] Specifically, the saliva test results include at least one of seven items, such as caries-causing bacteria (number of caries-causing bacteria), acidity, buffering capacity, white blood cells (number of white blood cells), protein (protein concentration), ammonia (ammonia concentration), and occult blood (amount of occult blood), as indicators for comprehensively evaluating the oral health status of the subject. Each of these items reflects a specific physiological and biochemical state in the oral cavity, and by using them individually or in combination as explanatory variables, it is possible to accurately capture the correlation between the overall oral condition and chronological age. It is preferable that the saliva test results include at least one of the following items: protein, ammonia, and acidity, and it is particularly preferable that the item for protein be included.

[0025] Such saliva tests are performed on a large number of subjects, for example, from tens to thousands of people, and subject data 16, which associates the saliva test results (including at least one item) with the subjects' actual ages, is stored in the storage unit 14 beforehand. Subject data 16 may be added sequentially. In this case, the oral age calculation formula, described later, may be updated automatically or manually each time subject data 16 is added.

[0026] The derivation unit 21 derives a regression equation calculated by performing regression analysis with, for example, the actual age as the target variable and at least one item included in the saliva test result as the explanatory variable, as the oral age calculation formula. As described above, the saliva test result preferably includes at least one item of protein, ammonia, and acidity, and particularly preferably includes the protein item. Since these items sensitively reflect the changes in the oral environment associated with aging, by using them as explanatory variables, an oral age calculation formula with a higher correlation coefficient with the actual age can be derived.

[0027] For example, the derivation unit 21 includes a regression equation calculation unit 22, a correlation coefficient calculation unit 23, a selection unit 24, and a correction coefficient calculation unit 25.

[0028] The regression equation calculation unit 22 performs regression analysis with the actual age as the target variable and all combinations of items included in the saliva test result as the explanatory variables to calculate each regression equation.

[0029] For example, if all of the above 7 items are included in the saliva test result, and the actual age as the target variable is y, the decayed tooth bacteria as the explanatory variable is x1, the acidity is x2, the buffering capacity is x3, the white blood cells are x4, the protein is x5, the ammonia is x6, and the occult blood is x7, the regression equation calculated by performing regression analysis is represented by the following equation.

[0030] y = a1×x1 + a2×x2 + a3×x3 + a4×x4 + a5×x5 + a6×x6 + a7×x7 + b ··· (1)

[0031] Here, a1, a2, a3, a4, a5, a6, and a7 are non-standardized coefficients (partial regression coefficients). Also, b is a constant.

[0032] The regression equation calculation unit 22 calculates regression equations for all combinations of items. For example, when all of the above 7 items are included in the saliva test result, ７ C １ + ７ C ２ + ７ C ３ + ７ C ４ + ７ C ５ + ７ C ６ +７ C ７ Calculate 127 regression equations where C = 7 + 21 + 35 + 35 + 21 + 7 + 1 = 127.

[0033] The correlation coefficient calculation unit 23 calculates the correlation coefficient between the oral age and the actual age for each of the regression equations calculated by the regression equation calculation unit 22 for each of the plurality of subjects, based on the oral age calculated using each of the regression equations and the actual age of the plurality of subjects. That is, the correlation coefficient calculation unit 23 calculates the oral age for each of the plurality of subjects who have undergone the saliva test, using the regression equation calculated by the regression equation calculation unit 22, for each regression equation. Then, the correlation coefficient between the oral age calculated using the regression equation and the actual age of the plurality of subjects who have undergone the saliva test is calculated for all the regression equations.

[0034] The correlation coefficient R is calculated by the following formula when the actual age is X and the oral age is Y.

[0035] R = (covariance of X and Y) / {(standard deviation of X) × (standard deviation of Y)} ··· (2)

[0036] Here, -1 ≤ R ≤ 1. The closer R is to 1, the stronger the positive correlation, and the closer R is to -1, the stronger the negative correlation.

[0037] The selection unit 24 selects, as the oral age calculation formula, a regression equation having a correlation coefficient equal to or greater than a predetermined threshold value (for example, 0.5) among the correlation coefficients calculated for each of the regression equations. For example, when there are a plurality of regression equations having a correlation coefficient equal to or greater than the predetermined threshold value, the regression equation with the highest correlation coefficient is selected as the oral age calculation formula.

[0038] Fig. 3 shows a graph in which the actual age corresponding to the oral age calculated by the above formula (1) for each of a large number of subjects is plotted, and also shows a straight line L1 representing the relational expression between the oral age and the actual age calculated by regression analysis.

[0039] Furthermore, Figure 4 shows a graph plotting the actual age corresponding to the prediction error between oral age and actual age (= oral age - actual age) in the graph shown in Figure 3, and also shows a straight line L2 representing the relationship between the prediction error and actual age calculated by regression analysis. As shown by the straight line L2 in Figure 4, the prediction error is smallest when the actual age is around 60 years old, and the absolute value of the prediction error increases as the age increases from around 60 years old, and also as the age decreases from around 60 years old.

[0040] Therefore, it is also possible to calculate a correction coefficient to adjust the oral age calculated using the oral age calculation formula selected above to the actual age.

[0041] Specifically, the correction coefficient calculation unit 25 calculates the prediction error between the oral age calculated using the oral age calculation formula for each of the multiple subjects and the actual age of the multiple subjects, calculates a relational expression (for example, the relational expression representing the straight line L2 in Figure 4) that shows the relationship between the calculated prediction error and the actual age, and calculates a correction coefficient to correct the oral age calculated by the oral age calculation formula based on the calculated relational expression and the actual age. For example, the correction coefficient calculation unit 25 calculates the prediction error for each actual age X based on the above relational expression and uses this as the correction coefficient c Ｘ Let's assume that.

[0042] When correcting the oral age y calculated using the above formula (1) with the actual age X, the formula is expressed as follows, where y1 is the corrected oral age.

[0043] y1 = y - c Ｘ ... (3)

[0044] Figure 5 shows a graph plotting the actual age corresponding to the corrected oral age, which has been corrected using equation (3) above, and also shows a straight line L3 representing the relationship between the corrected oral age and actual age, which was calculated by regression analysis. As shown by the straight line L3 in Figure 5, the corrected oral age is closer to the actual age in all age groups compared to the oral age shown in Figure 3.

[0045] Thus, as shown in equation (3) above, the accuracy of oral age prediction can be improved by correcting oral age with actual age.

[0046] Next, the process of deriving the oral age calculation formula, which is performed by the CPU 10A of the oral age calculation formula derivation device 10, will be explained with reference to the flowchart shown in Figure 6.

[0047] In step S100, the CPU 10A acquires the subject data 16 by reading it from the storage unit 14.

[0048] In step S101, CPU 10A performs regression analysis with actual age as the dependent variable and all combinations of items included in the saliva test results as independent variables, and calculates a regression equation for each.

[0049] In step S102, the CPU 10A calculates the correlation coefficient between oral age and chronological age for each of the regression equations, based on the oral age calculated for each of the multiple subjects using the regression equations calculated in step S101, and the chronological age of the multiple subjects.

[0050] In step S103, the CPU 10A selects, for example, the regression equation with the highest correlation coefficient from among the correlation coefficients calculated for each regression equation that are equal to or greater than a predetermined threshold, as the oral age calculation equation.

[0051] In step S104, the CPU 10A calculates the prediction error for each of the multiple subjects between the oral age calculated using the oral age calculation formula selected in step S103 and the actual age of each of the multiple subjects, and calculates a relational expression showing the relationship between the calculated prediction error and the actual age. Then, based on the calculated relational expression and the actual age, it calculates a correction coefficient for each actual age to correct the oral age calculated using the oral age calculation formula selected in step S103. Finally, the regression formula and correction coefficient selected in step S103 are stored in the storage unit 14.

[0052] Thus, in this embodiment, an oral age calculation formula can be derived that can calculate oral age from the results of a saliva test. Furthermore, based on the prediction error between the oral age calculated using the oral age calculation formula and the actual age, a correction coefficient is calculated for each actual age to correct the oral age calculated using the oral age calculation formula, thereby obtaining an oral age calculation formula with improved prediction accuracy for oral age.

[0053] <Oral Age Calculation Device>

[0054] Next, we will describe an oral age calculation device that calculates oral age using the oral age calculation formula derived by the oral age calculation formula derivation device 10.

[0055] Figure 7 shows the hardware configuration of the oral age calculation device 30 according to this embodiment. As shown in Figure 7, the oral age calculation device 30 includes a CPU (Central Processing Unit) 30A, a ROM (Read Only Memory) 30B, a RAM (Random Access Memory) 30C, and an input / output interface (I / O) 30D. The CPU 30A, ROM 30B, RAM 30C, and I / O 30D are connected to each other via a system bus 30E. The system bus 30E includes a control bus, an address bus, and a data bus.

[0056] CPU 30A is an example of a computer. Here, "computer" refers to a processor in a broad sense, including general-purpose processors (e.g., CPUs) or specialized processors (e.g., GPUs: Graphics Processing Units, ASICs: Application Specific Integrated Circuits, FPGAs: Field Programmable Gate Arrays, programmable logic devices, etc.).

[0057] Furthermore, the I / O 30D is connected to an operation display unit 31, a communication unit 32, and a storage unit 33.

[0058] The operation display unit 31 is composed of, for example, a touch panel. Alternatively, the operation unit and the display unit may be configured separately. In this case, the operation unit may include, for example, a mouse and keyboard, or a touch panel. The display unit may be composed of, for example, a liquid crystal display.

[0059] The communication unit 32 is an interface for data communication with external devices.

[0060] The memory unit 33 is composed of a non-volatile external storage device such as a hard disk, and contains an oral age calculation program 34, an oral age calculation formula 35, and a correction coefficient c. Ｘ Remember things like that.

[0061] The oral age calculation formula 35 is a calculation formula selected from among the oral age calculation formulas derived by the oral age calculation formula derivation device 10 described above, and is stored in the memory unit 33 in advance.

[0062] Correction coefficient c Ｘ This is a correction coefficient calculated by the oral age calculation formula derivation device 10 described above, and is stored in the memory unit 33 beforehand.

[0063] The CPU 30A reads the oral age calculation program 34 stored in the memory unit 33 into the RAM 30C and executes it.

[0064] The oral age calculation program 34 may be stored on a non-volatile, non-transitory recording medium, or distributed via a network, and installed in the oral age calculation device 30 as appropriate.

[0065] Examples of non-volatile, non-transition recording media include CD-ROMs (Compact Disc Read Only Memory), magneto-optical disks, HDDs (hard disk drives), DVD-ROMs (Digital Versatile Disc Read Only Memory), flash memory, and memory cards.

[0066] The oral age calculation device 30 may be, for example, a mobile device such as a smartphone or tablet, or it may be a dedicated device for calculating oral age. It may also be a server device that provides a website for calculating oral age. If the oral age calculation device 30 is a server device such as a cloud server, it may be configured without an operation display unit 31. In this case, the person whose oral age is to be calculated can view their oral age by accessing the server device.

[0067] Furthermore, the oral age calculated by the oral age calculation device 30 may be output to a printer (for example, a mobile printer) via the communication unit 32 so that it can be printed on paper.

[0068] Furthermore, if the oral age calculation device 30 is managed by the person performing the oral age calculation rather than the person whose oral age is to be calculated, the oral age calculated by the oral age calculation device 30 may be transmitted to a personal terminal owned by the person whose oral age is to be calculated, so that the oral age is displayed on the personal terminal.

[0069] Furthermore, the aforementioned saliva testing device may be configured to include an oral age calculation device 30.

[0070] The CPU 30A functions as one of the functional units shown in Figure 8 by reading and executing the oral age calculation program 34 stored in the memory unit 33.

[0071] Figure 8 is a block diagram showing the functional configuration of the CPU 30A. As shown in Figure 8, the CPU 30A functionally comprises an acquisition unit 40, a calculation unit 41, and a display control unit 42.

[0072] The acquisition unit 40 acquires the saliva test results, which include at least one item obtained from a saliva test performed on a subject for oral age calculation, and the subject's age. The saliva test results include at least one of seven items, such as caries-causing bacteria, acidity, buffering capacity, white blood cells, protein, ammonia, and occult blood.

[0073] The person performing the saliva test inputs the saliva test results and age of the person to be calculated for oral age calculation by, for example, operating the operation display unit 31. This allows the acquisition unit 40 to acquire the saliva test results and age of the person to be calculated for oral age calculation. Alternatively, the saliva test results and age of the person to be calculated for oral age calculation may be acquired by communicating with a device that stores the saliva test results and age of the person to be calculated for oral age calculation via the communication unit 32. For example, in a self-check, the person performing the saliva test may be the same as the person to be calculated for oral age calculation. Furthermore, for example, in a mass screening, the person performing the saliva test and the person to be calculated for oral age calculation may be different.

[0074] The calculation unit 41 calculates the subject's oral age by inputting the saliva test results acquired by the acquisition unit 40 into the oral age calculation formula 35 stored in the storage unit 33, which is derived by the oral age calculation formula derive device 10. A correction coefficient c is applied according to the subject's age X. Ｘ Oral age may be corrected accordingly.

[0075] The display control unit 42 controls the display to show the subject's oral age, calculated by the calculation unit 41, on the operation display unit 31, which is an example of a display unit.

[0076] Next, the oral age calculation process performed by the CPU 30A of the oral age calculation device 30 will be explained with reference to the flowchart shown in Figure 9.

[0077] In step S200, the CPU 30A obtains the saliva test results, which include at least one item obtained from the saliva test performed on the subject for calculation of oral age, and the subject's age.

[0078] In step S201, the CPU 30A calculates the subject's oral age by inputting the saliva test results obtained in step S200 into the oral age calculation formula 35 stored in the memory unit 33.

[0079] In step S202, the CPU 30A calculates a correction coefficient c from the oral age calculated in step S201, according to the subject's age X obtained in step S200. Ｘ The oral age is corrected by reading the value from the memory unit 33 and subtracting it.

[0080] In step S203, the CPU 30A displays the oral age corrected in step S202 on the operation display unit 31. Figure 10 shows an example of how the oral age is displayed. In the example in Figure 10, the message "Your oral age is 40 years old." is displayed on the operation display unit 31. Alternatively, the difference between the oral age and the subject's actual age may be displayed on the operation display unit 31, or advice corresponding to the difference between the oral age and the subject's actual age may be displayed on the operation display unit 31.

[0081] In this embodiment, the oral age calculated using an oral age calculation formula that can calculate oral age from the results of a saliva test is displayed on the operation display unit 31. This makes it possible to raise awareness of the oral condition of the person being measured for oral age calculation.

[0082] Furthermore, the operation of the processor in the above embodiments may not be performed by a single processor, but may be performed by multiple processors located in physically separate locations working together. Also, the order of the processor's operations is not limited to the order described in each of the above embodiments, but may be changed as appropriate.

[0083] Furthermore, the configuration of the information processing device described in the above embodiment is merely an example, and may be modified as needed without departing from the main purpose.

[0084] For example, the above embodiment described a case in which an oral age calculation formula is derived using regression analysis and correlation coefficients, but it is not limited to this.

[0085] For example, instead of the regression analysis described in the above embodiment, the following model may be used to derive the formula for calculating oral age.

[0086] Logistic regression analysis, Elastic Net regression, Ridge regression, Lasso regression, Decision tree models, Random forest models, Support Vector Machines (SVM), k-nearest neighbors, Neural networks, Convolutional neural networks

[0087] Alternatively, a model may be selected to calculate the oral age formula using the following selection criteria instead of the correlation coefficient described in the above embodiment.

[0088] • Adjusted R-squared coefficient (AIC) (Akaike Information Criterion) • MSE (Mean Squared Error) • MAE (Mean Absolute Error) • RMSE (Mean Squared Root Error)

[0089] Furthermore, although the above embodiment described a case in which the oral age calculation device 30 calculates the oral age using the oral age calculation formula derived by the oral age calculation formula derivation device 10, the oral age calculation formula derived by the oral age calculation formula derivation device 10 may be printed on paper or displayed on electronic media, and the oral age may be calculated manually while referring to the saliva test results.

[0090] Furthermore, the program processing flow described in the above embodiment is just one example, and unnecessary steps may be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0091] Furthermore, although the above embodiment describes a case in which the process according to the embodiment is realized by a software configuration using a computer by executing a program, the embodiment is not limited to this. The embodiment may also be realized by a hardware configuration or a combination of a hardware configuration and a software configuration.

[0092] The following additional information is disclosed regarding the embodiments described above.

[0093] (Note) (Note 1) A method for deriving an oral age calculation formula, which includes a computer acquiring subject data including saliva test results containing at least one item obtained from saliva tests performed on multiple subjects, and the actual age of the multiple subjects, and deriving a regression equation calculated by performing a regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable, as an oral age calculation formula. (Note 2) The method for deriving an oral age calculation formula according to Note 1, wherein the saliva test results include at least one of seven items: caries-causing bacteria, acidity, buffering capacity, white blood cells, protein, ammonia, and occult blood. (Note 3) A method for deriving an oral age calculation formula according to Note 1 or Note 2, which includes the following steps: the computer performs regression analysis with the actual age as the dependent variable and all combinations of items included in the saliva test results as independent variables to calculate a regression equation for each of the multiple subjects; the computer calculates a correlation coefficient between the oral age and the actual age for each of the multiple subjects based on the oral age calculated for each of the multiple subjects using each of the regression equations and the actual age of the multiple subjects; and from among the correlation coefficients calculated for each of the regression equations, the regression equation having a correlation coefficient equal to or greater than a predetermined threshold is selected as the oral age calculation formula. (Note 4) A method for deriving an oral age calculation formula according to any one of Notes 1 to 3, wherein the computer performs a process that includes calculating a prediction error between the oral age calculated for each of the plurality of subjects using the oral age calculation formula and the actual age of the plurality of subjects, calculating a relational expression showing the relationship between the calculated prediction error and the actual age, and calculating a correction coefficient for correcting the oral age calculated by the oral age calculation formula based on the calculated relational expression and the actual age. (Note 5) A program for deriving an oral age calculation formula that causes the computer to perform a process that includes acquiring subject data including saliva test results including at least one item obtained from saliva tests performed on a plurality of subjects and the actual age of the plurality of subjects, and deriving a regression expression calculated by performing a regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable, as an oral age calculation formula.(Note 6) An oral age calculation formula derivation device comprising: an acquisition unit that acquires subject data including saliva test results including at least one item obtained from saliva tests conducted on multiple subjects, and the actual ages of the multiple subjects; and a derivation unit that derives a regression equation calculated by performing regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable, as an oral age calculation formula. (Note 7) An oral age calculation method in which a computer acquires saliva test results including at least one item obtained from saliva tests conducted on subjects for oral age calculation, and the age of the subjects, and calculates the oral age of the subjects by inputting the saliva test results into an oral age calculation formula derived by the oral age calculation formula derivation method described in any one of Notes 1 to 4. (Note 8) The oral age calculation method according to Note 7, wherein the saliva test result includes at least one of seven items: caries-causing bacteria, acidity, buffering capacity, white blood cells, protein, ammonia, and occult blood. (Note 9) The oral age calculation method according to Note 7 or Note 8, wherein the computer performs a process that includes controlling the computer to display the oral age of the subject calculated by the calculation unit on the display unit. (Note 10) An oral age calculation program that causes the computer to perform a process that includes obtaining a saliva test result including at least one item obtained from a saliva test performed on a subject for oral age calculation, and the age of the subject, and inputting the saliva test result into an oral age calculation formula derived by the oral age calculation formula derivation method according to any one of Note 1 to 4, thereby calculating the oral age of the subject. (Note 11) An oral age calculation device comprising: an acquisition unit that acquires saliva test results including at least one item obtained from a saliva test performed on a subject subject for calculation of oral age, and the subject's age; and a calculation unit that calculates the subject's oral age by inputting the saliva test results into an oral age calculation formula derived by the method for deriving an oral age calculation formula described in any one of Notes 1 to 4.

[0094] Furthermore, the disclosure of Japanese Patent Application No. 2024-181368 is incorporated herein by reference in its entirety. In addition, all documents, patent applications, and technical standards described herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

Claims

1. A method for deriving an oral age calculation formula, which includes a computer acquiring subject data including saliva test results containing at least one item obtained from saliva tests conducted on multiple subjects, and the actual ages of the multiple subjects, and deriving a regression equation calculated by performing a regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable, as an oral age calculation formula.

2. The method for deriving an oral age calculation formula according to claim 1, wherein the saliva test result includes at least one of seven items: caries-causing bacteria, acidity, buffering capacity, white blood cells, protein, ammonia, and occult blood.

3. The method for deriving an oral age calculation formula according to claim 1, which includes the following steps: the computer performs regression analysis with the actual age as the dependent variable and all combinations of items included in the saliva test results as independent variables to calculate a regression equation for each of the multiple subjects; the computer calculates a correlation coefficient between the oral age and the actual age for each of the multiple subjects based on the oral age calculated for each of the multiple subjects using each of the regression equations and the actual age of the multiple subjects; and from among the correlation coefficients calculated for each of the regression equations, the regression equation having a correlation coefficient equal to or greater than a predetermined threshold is selected as the oral age calculation formula.

4. The method for deriving an oral age calculation formula according to claim 1, which includes the following steps: the computer calculates a prediction error between the oral age calculated using the oral age calculation formula for each of the plurality of subjects and the actual age of the plurality of subjects; calculates a relational expression showing the relationship between the calculated prediction error and the actual age; and calculates a correction coefficient for correcting the oral age calculated using the oral age calculation formula based on the calculated relational expression and the actual age.

5. A program for deriving an oral age calculation formula, which causes a computer to perform a process that includes acquiring subject data including saliva test results containing at least one item obtained from saliva tests conducted on multiple subjects, and the actual age of the multiple subjects, and deriving a regression equation calculated by performing a regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable, as an oral age calculation formula.

6. An oral age calculation formula derivation device comprising: an acquisition unit that acquires subject data including saliva test results containing at least one item obtained from saliva tests conducted on multiple subjects, and the actual age of the multiple subjects; and a derivation unit that derives an oral age calculation formula from a regression equation calculated by performing a regression analysis with the actual age as the dependent variable and at least one item included in the saliva test results as the independent variable.

7. A method for calculating oral age, comprising a computer obtaining a saliva test result including at least one item obtained from a saliva test performed on a subject for calculation of oral age, and the subject's age, and inputting the saliva test result into an oral age calculation formula derived by the method for deriving an oral age calculation formula described in any one of claims 1 to 4, thereby calculating the subject's oral age.

8. The oral age calculation method according to claim 7, wherein the saliva test result includes at least one of seven items: caries-causing bacteria, acidity, buffering capacity, white blood cells, protein, ammonia, and occult blood.

9. The oral age calculation method according to claim 7, which includes controlling the computer to display the calculated oral age of the subject on the display unit.

10. An oral age calculation program that causes a computer to perform a process including obtaining saliva test results, which include at least one item obtained from a saliva test conducted on a subject for oral age calculation, and the subject's age, and inputting the saliva test results into an oral age calculation formula derived by the method for deriving an oral age calculation formula described in any one of claims 1 to 4, thereby calculating the subject's oral age.

11. An oral age calculation device comprising: an acquisition unit that acquires saliva test results including at least one item obtained from a saliva test performed on a subject for oral age calculation, and the subject's age; and a calculation unit that calculates the subject's oral age by inputting the saliva test results into an oral age calculation formula derived by the oral age calculation formula derivation method described in any one of claims 1 to 4.

Images