Middle ear disease risk assessment device, middle ear disease risk assessment program, device for calculating indicators related to middle ear disease risk, and program for calculating indicators related to middle ear disease risk

Abstract

The object of this invention is to provide a novel device and program for evaluating the risk of middle ear disease in a subject. [Solution] The present invention solves the above problems and is a middle ear disease risk assessment device for a subject, comprising an evaluation unit that evaluates the middle ear disease risk of the subject based on the R-index, an index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and static compliance in the external auditory canal pressure-tympanic membrane displacement data.

Description

Technical Field

[0001] The present invention relates to a middle ear disease risk assessment device, a middle ear disease risk assessment program, an index calculation device related to middle ear disease risk, and an index calculation program related to middle ear disease risk.

Background Art

[0002] Conventionally, impedance audiometry is known as a method for objectively evaluating the state of the middle ear. A typical example thereof, tympanometry, diagnoses the function of the middle ear by applying sound waves from a probe in a state where the external auditory canal is sealed, changing the pressure in the external auditory canal, and measuring the change in acoustic impedance of the tympanic membrane at that time.

[0003] As one of the methods for evaluating the middle ear, Patent Document 1 discloses a device and a method for performing measurement without pressurizing the ear canal and without requiring airtight contact between the device and the ear. This method radiates acoustic waves in a range including the resonance frequency of the constituent organs of the ear such as the tympanic membrane into the ear canal, and electronically measures the shape of the "acoustic reflection curve" generated by combining the incident wave and the reflected wave.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In view of the above prior art, an object of the present invention is to provide a novel device and program for evaluating the middle ear disease risk of a subject. Another object of the present invention is to provide a novel device and program for calculating an index related to middle ear disease risk.

Means for Solving the Problems

[0006] The present invention, which solves the above problems, is as follows [1] to [7].

[0007] [1] A device for evaluating the risk of middle ear disease in a subject, comprising an evaluation unit that evaluates the risk of middle ear disease in a subject based on an index R-index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and static compliance in the external auditory canal pressure-tympanic membrane displacement data.

[0008] [2] The evaluation unit evaluates one or more middle ear diseases that the subject may have by comparing the R-index with one or more thresholds for determining the possibility of the subject having one or more middle ear diseases, as described in [1].

[0009] [3] A middle ear disease risk assessment device for subjects as described in [1] or [2], wherein the middle ear disease is at least one of tympanic membrane thinning, Eustachian tube dysfunction, ossicular detachment, congenital cholesteatoma, acquired cholesteatoma, and congenital middle ear malformation, and the evaluation unit evaluates that the lower the R-index value, the higher the risk of at least one of tympanic membrane thinning, Eustachian tube dysfunction, ossicular detachment, congenital cholesteatoma, acquired cholesteatoma, and congenital middle ear malformation.

[0010] [4] A middle ear disease risk assessment device for a subject according to any one of [1] to [3], wherein the middle ear disease is at least one of tympanic membrane calcification, tympanosclerosis, and otosclerosis, and the evaluation unit evaluates that the higher the R-index value, the higher the risk of at least one of tympanic membrane calcification, tympanosclerosis, and otosclerosis.

[0011] [5] A middle ear disease risk assessment program for a subject, wherein a computer functions as an assessment unit that assesses the middle ear disease risk of the subject based on an R-index, an index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and static compliance in the external auditory canal pressure-tympanic membrane displacement data.

[0012] [6] A calculation device for an index related to the risk of middle ear disease, comprising: a first calculation unit that generates an exponential approximation formula from the external auditory canal pressure-tympanic membrane displacement data of a subject and calculates a time constant from the exponential approximation formula; and a second calculation unit that calculates an index related to the risk of middle ear disease, R-index, using the time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data.

[0013] [7] A calculation program for an index related to the risk of middle ear disease, wherein the computer functions as a first calculation unit that generates an exponential approximation formula from the external auditory canal pressure-tympanic membrane displacement data of a subject and calculates a time constant from the exponential approximation formula, and a second calculation unit that calculates an index related to the risk of middle ear disease, R-index, using the time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. [Effects of the Invention]

[0014] The present invention provides a novel device and program for evaluating the risk of middle ear disease in a subject. Furthermore, the present invention provides a novel device and program for calculating indicators related to the risk of middle ear disease. [Brief explanation of the drawing]

[0015] [Figure 1] This is a system configuration diagram of an evaluation system according to one embodiment of the present invention. [Figure 2] This is a hardware configuration diagram of an evaluation device according to one embodiment of the present invention. [Figure 3] Hardware configuration diagram of the acquisition device according to an embodiment of the present invention. [Figure 4] Functional block diagram of the evaluation device according to an embodiment of the present invention. [Figure 5] Figure showing a flowchart of an evaluation method using the evaluation device according to an embodiment of the present invention. [Figure 6] Figure showing a method for calculating the τ-index according to an embodiment of the present invention. [Figure 7] Box plot showing the distribution of the R-index in the control group, the otosclerosis disease group, and the otosclerosis disease group.

Mode for carrying out the invention

[0016] <1> Middle ear disease risk assessment device for a subject The middle ear disease risk assessment device for a subject of the present invention (hereinafter simply referred to as the "evaluation device") uses a time constant calculated by an exponential approximation formula generated from the external auditory canal pressure-tympanic membrane displacement data of the subject and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. An evaluation unit that evaluates the middle ear disease risk of the subject based on the index R-index related to the middle ear disease risk calculated using the above is provided.

[0017] 1. System configuration Hereinafter, the system configuration of the evaluation system 1 of the present embodiment will be described with reference to FIG. 1. In the present embodiment, the evaluation system 1 includes an evaluation device 2 and an acquisition device 3.

[0018] The evaluation device 2 is configured to be communicable with the acquisition device 3 via the network 4 and can receive the external auditory canal pressure-tympanic membrane displacement data of the subject acquired by the acquisition device 3. The evaluation device 2 may be configured to receive the external auditory canal pressure-tympanic membrane displacement data of the subject acquired by the acquisition device 3 via a storage medium or the like.

[0019] Network 4 may be a global or local Internet Protocol (IP) network, or may be configured by wireless communication using other communication protocols such as Bluetooth (registered trademark). Also, the network may be configured by wired connection via a network cable or other data communication cable.

[0020] 2. Hardware Configuration FIG. 2 is a hardware configuration diagram of the evaluation device 2. In the present embodiment, the evaluation device 2 includes a processing unit 21, a storage unit 22, and a display unit 23.

[0021] The processing unit 21 has a processor such as a Central Processing Unit (CPU) capable of executing an instruction set, and controls the entire operation processing of the evaluation device 2 by executing a computer program, an OS, and other applications.

[0022] The storage unit 22 has a volatile memory such as a Random Access Memory (RAM) capable of storing an instruction set, and a non-volatile recording medium such as a Hard Disk Drive (HDD) or a Solid State Drive (SSD) capable of recording an Operating System (OS) and the above-mentioned computer program. Also, the storage unit 22 realizes functional components described later and stores a program for causing a computer to execute arithmetic processing and evaluation. Also, the storage unit 22 stores various data.

[0023] The various data stored in the storage unit 22 includes standard thresholds calculated using statistical methods based on clinical data obtained in advance from a large number of subjects. This threshold is set as a cut-off value that can optimally distinguish between a group having a specific middle ear disease and a normal group.

[0024] Also, the various data stored in the storage unit 22 may store a huge amount of past case data and model data of healthy persons as a data set.

[0025] The display unit 23 has a display device capable of display processing, such as a display.

[0026] Acquisition device 3 is a device that measures eardrum movement such as compliance while changing the pressure inside the subject's external auditory canal, and generates external auditory canal pressure-eardrum displacement data.

[0027] External auditory canal pressure-tympanic membrane displacement data is a set of data that shows changes in acoustic movement, such as compliance, of the tympanic membrane and middle ear system when the static pressure in the external auditory canal is continuously or stepwise changed. Specifically, it is constructed as a two-dimensional dataset with external auditory canal pressure on the horizontal axis and displacement, which represents the mobility of the tympanic membrane, on the vertical axis.

[0028] As the acquisition device 3, for example, a stationary or handheld impedance audiometer (such as one manufactured by Rion Corporation) used in medical institutions can be suitably used. Alternatively, the acquisition device 3 may be an earphone-type wearable device that integrates an ear tip capable of sealing the ear canal, a micropump, an acoustic device (speaker and microphone), and a control circuit, or a composite device that has other examination functions such as an electronic stethoscope or an ear endoscope.

[0029] Figure 3 is a hardware configuration diagram of the acquisition device 3. In this embodiment, the acquisition device 3 comprises a probe 31, a pressure control unit 32, an acoustic drive unit 33, an acoustic listening unit 34, a control unit 35, and a communication unit 36.

[0030] The probe 31 has an ear tip for sealing the subject's ear canal, and has a conduit formed inside the ear canal for irradiating an acoustic signal and picking up the reflected sound.

[0031] The pressure control unit 32 includes a pump and a pressure sensor, and based on commands from the control unit 35, it pressurizes or depressurizes the air in the ear canal sealed by the probe 31, thereby changing the ear canal pressure.

[0032] The acoustic drive unit 33 includes a speaker and the like, and irradiates the ear canal with a measurement sound (for example, a 226Hz pure tone) via the probe 31 to measure the mobility of the eardrum.

[0033] The acoustic receiving unit 34 includes a microphone and the like, detects the measured sound reflected in the ear canal, converts it into an electrical signal, and outputs it to the control unit 35.

[0034] The control unit 35 calculates the displacement of the eardrum, such as compliance, from the changes in reflected sound obtained from the acoustic listening unit 34, in synchronization with the pressure sweep performed by the pressure control unit 32. The control unit 35 generates eardrum pressure-eardrum displacement data that associates the fluctuation value of the eardrum pressure with the corresponding value of the eardrum displacement.

[0035] The communication unit 36 ​​is an interface for communicating with the evaluation device 2 via the network 4. The external auditory canal pressure-tympanic membrane displacement data generated by the control unit 35 is transmitted to the evaluation device 2 via the communication unit 36. The acquisition device 3 may also be configured to write the data to a removable storage medium.

[0036] 3. Functional Components Figure 4 is a functional block diagram of the evaluation device 2. The processing unit 21 of the evaluation device 2 functions as an acquisition unit 211, a first calculation unit 212, a second calculation unit 213, and an evaluation unit 214 by executing the computer program described above.

[0037] 4. Processing Procedure The overall processing flow of the evaluation device 2 in this embodiment will be explained below with reference to Figure 5. Figure 5 shows the processing flowchart of the evaluation method using the evaluation device 2.

[0038] First, in the acquisition process (process S101), the acquisition unit 211 acquires the subject's external auditory canal pressure-tympanic membrane displacement data. In this embodiment, the external auditory canal pressure-tympanic membrane displacement data includes static compliance and time-series data including peak pressure values.

[0039] Next, in the first calculation step (step S102), the first calculation unit 212 generates an exponential approximation formula from the subject's external auditory canal pressure-tympanic membrane displacement data, and calculates a time constant from the exponential approximation formula.

[0040] Specifically, the first calculation unit 212 takes the peak pressure value of the subject's external auditory canal pressure-tympanic membrane displacement data obtained by the acquisition unit 211 as the initial value P0, and approximates the time change of the subsequent decay process with the exponential function model shown in equation (1) below.

[0041]

number

[0042] Here, τ is a time constant that defines the compliance decay rate. The first calculation unit 212 applies the least squares method to a plurality of sampled external auditory canal pressure-tympanic membrane displacement data points to calculate the time constant τ in equation (1) above. In this embodiment, the obtained time constant τ is defined as τ-index.

[0043] In one embodiment, the first calculation unit 212 extracts at least three representative points from the extracted valid data interval in order to reduce the computational load and stabilize convergence. For example, as shown in Figure 6, the peak pressure value P0 is used as a reference, and multiple representative points corresponding to a predetermined pressure difference from the peak pressure are extracted. In one embodiment, three points corresponding to 50 daPa, 100 daPa, and 150 daPa are extracted from the peak pressure, and the time constant τ is calculated using exponential approximation with this data.

[0044] In one embodiment, the first calculation unit 212 automatically identifies the data range in order to mechanically ensure fitting accuracy. For example, the time when the peak pressure value P0 is detected is set as the reference point (t=0), and the period until the signal intensity decays from 90% to 10% of P0 is extracted as the valid data interval. This excludes unstable behavior immediately after the rise of the signal and noise at the end of decay from the calculation target, and extracts only the interval that reflects the physical characteristics.

[0045] In one embodiment, the first calculation unit 212 applies the least squares method to a plurality of sampled external auditory canal pressure-tympanic membrane displacement data points to calculate the time constant τ in equation (1) above. Specifically, a nonlinear least squares method (e.g., Levenberg-Marquardt method) is used to iteratively update the time constant τ so as to minimize the sum of squared residuals between the measured value and the model equation. At this time, a weighting coefficient according to the signal intensity may be assigned to each data point to control the process so that data with a high S / N ratio contribute strongly to the fitting result.

[0046] Then, in the second calculation step (step S103), the second calculation unit 213 calculates the R-index, an index related to the risk of middle ear disease, using the τ-index and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. Static compliance (SC) is the compliance in the peak pressure value of the subject's external auditory canal pressure-tympanic membrane displacement data.

[0047] The R-index, an index related to the risk of middle ear disease, is defined by the following equation (2).

[0048]

number

[0049] In equation (2) above, τ-index is the time constant calculated in equation (1) above, and SC is the static compliance in the external auditory canal pressure-tympanic membrane displacement data. The R-index, an index related to the risk of middle ear disease, is an objective physical quantity that continuously quantifies the effective elastic stiffness of the middle ear closure system and does not depend on specific subjective symptoms or physiological interpretations.

[0050] Finally, in the evaluation process (process S104), the evaluation unit 214 evaluates the subject's risk of middle ear disease based on the R-index.

[0051] The target middle ear diseases are not particularly limited, but in this embodiment they are one or more selected from tympanic membrane thinning, Eustachian tube dysfunction, ossicular transection, congenital cholesteatoma, acquired cholesteatoma, congenital middle ear malformation, tympanic membrane calcification, tympanosclerosis, and otosclerosis.

[0052] In one embodiment, the evaluation unit 214 evaluates that the lower the R-index value, the higher the risk that the subject has at least one of the following conditions: thinning of the tympanic membrane, patulous Eustachian tube, ossicular transection, congenital cholesteatoma, acquired cholesteatoma, and congenital middle ear malformation.

[0053] In one embodiment, the evaluation unit 214 evaluates that the higher the R-index value, the higher the risk that the subject has at least one of the following conditions: tympanic membrane calcification, tympanosclerosis, and otosclerosis.

[0054] In one embodiment, the evaluation unit 214 evaluates one or more middle ear diseases that the subject may have by comparing one or more threshold values ​​stored in the memory unit 22 with the R-index value.

[0055] Specifically, the evaluation unit 214 determines that a subject has a specific middle ear disorder if the calculated index is below a predetermined threshold. Alternatively, by using multiple thresholds corresponding to multiple middle ear disorders in a stepwise manner, the control group and the group with multiple middle ear disorders can be distinguished from each other, and the subject's specific middle ear disorder can be identified.

[0056] In one embodiment, the evaluation unit 214 evaluates that the subject has a high risk of being in the middle ear softening disease group when the R-index ≤ 91.91.

[0057] In one embodiment, the evaluation unit 214 selects the optimal threshold suitable for the subject from among a plurality of threshold candidates stored in the storage unit 22, based on attribute information of the subject input via an external input device, such as age, gender, height, weight, or information on individual differences such as smoking history, and uses it for determination.

[0058] In one embodiment, the evaluation unit 214 evaluates one or more middle ear diseases that the subject may have by performing pattern matching and statistical similarity calculation between the R-index and each data point included in the dataset stored in the storage unit 22.

[0059] In one embodiment, the evaluation unit 214 pre-creates an equation or model that shows the correlation between the R-index and the risk of middle ear disease, and evaluates the risk of middle ear disease of the subject by comparing the calculated R-index with the said equation or model.

[0060] Here, the correlation between the R-index and the risk of middle ear disease can be determined in advance by multivariate analysis such as regression analysis. Preferably, the multivariate analysis should utilize the relationship between the dependent variable and the independent variables, and discriminant analysis and regression analysis (MLR, PLS, PCR, logistic regression analysis) are good examples. Of these, multiple regression analysis (MLR) and nonlinear regression analysis (PLS) are particularly preferred. For example, by performing multiple regression analysis with the R-index as the independent variable and the severity or presence of middle ear disease as the dependent variable, a multiple regression equation can be obtained. Similarly, by performing PLS, a prediction equation (predictive model) can be obtained.

[0061] To obtain accurate formulas or models, various multivariate analyses such as principal component analysis, factor analysis, quantification theory type I, quantification theory type II, quantification theory type III, multidimensional scaling, supervised clustering, neural networks, and ensemble learning methods can be used as appropriate. Among these, neural networks, discriminant analysis, and quantification theory type I are preferred.

[0062] Furthermore, in order to obtain a highly accurate formula or model, a learning device can be used that includes a storage unit that stores the correlation between the R-index and the severity or presence or absence of middle ear disease, which has been determined in advance by a doctor's diagnosis, etc., as input information, and a learning unit that uses the correlation as training data to generate a trained model in which variables for evaluating the risk of middle ear disease for a subject have been machine-learned from the subject's R-index.

[0063] To create an equation or model that shows the correlation between the R-index and the risk of middle ear disease, it is preferable to create a database (DB) in the storage unit as a dataset that associates the R-index with the status of middle ear disease. Here, the number of cases stored in the database is preferably 10 or more, more preferably 20 or more, more preferably 50 or more, more preferably 100 or more, and even more preferably 200 or more. As for the structure of the database, for example, if it is in matrix format, the R-index can be entered in the rows and the type and severity of the middle ear disease can be entered in the columns.

[0064] This database may be updated as needed by estimating the correlation between the R-index of newly acquired subjects and the evaluation results of middle ear diseases, and then adding the estimated values ​​or retrospectively confirmed diagnostic results. If necessary, the multivariate analysis described above can be performed on the updated database to update the formulas or models. This configuration makes it possible to continuously improve the accuracy of middle ear disease risk assessment as data accumulates over time.

[0065] According to the present invention, it is possible to evaluate the risk of middle ear disease in a subject based on the R-index.

[0066] In one embodiment, the display unit 23 displays the results evaluated by the evaluation unit 214 on a display device capable of display processing, such as a display.

[0067] In one embodiment, the evaluation device 2 and the acquisition device 3 may be configured as a single unit.

[0068] In one embodiment, the processing unit 21 may include a suggestion unit that proposes information regarding treatment methods, improvement measures, or lifestyle guidance suitable for the subject, based on the above-mentioned middle ear disease risk assessment results.

[0069] <2> Middle ear disease risk assessment program for target individuals The present invention also relates to a program for evaluating the risk of middle ear disease in subjects. The program of the present invention is as described above. <1> This corresponds to the processing unit 21 of the evaluation device 2 described in the section above. <1> The explanation for this item also applies to the following programs.

[0070] The present invention is a program for evaluating the risk of middle ear disease in a subject, which causes a computer to function as an evaluation unit that evaluates the risk of middle ear disease in a subject based on the R-index, an index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and static compliance in the external auditory canal pressure-tympanic membrane displacement data.

[0071] <3> Calculator for indicators related to middle ear disease risk The present invention also relates to a calculation device for indicators related to the risk of middle ear disease. The calculation device of the present invention is as described above. <1> This corresponds to the first calculation unit 212 and the second calculation unit 213 of the evaluation device 2 described in the section above. <1> The description of this item also applies to the following devices.

[0072] The present invention provides a calculation device for an index related to the risk of middle ear disease, comprising: a first calculation unit that generates an exponential approximation formula from the external auditory canal pressure-tympanic membrane displacement data of a subject and calculates a time constant from the exponential approximation formula; and a second calculation unit that calculates an index R-index related to the risk of middle ear disease using the time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data.

[0073] <4> Program for calculating indicators related to middle ear disease risk The present invention also relates to a calculation program for indicators related to the risk of middle ear disease. The calculation program of the present invention is as described above. <1> This corresponds to the first calculation unit 212 and the second calculation unit 213 of the evaluation device 2 described in the section above. <1> The explanation for this item also applies to the following programs.

[0074] The present invention is a calculation program for an index related to the risk of middle ear disease, wherein the computer functions as a first calculation unit that generates an exponential approximation formula from the external auditory canal pressure-tympanic membrane displacement data of a subject and calculates a time constant from the exponential approximation formula, and a second calculation unit that calculates the R-index, an index related to the risk of middle ear disease, using the time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. [Examples]

[0075] The following describes an example of investigating the relationship between the R-index, an index related to the risk of middle ear disease, and middle ear disease itself.

[0076] <Test Example 1> Acquisition of external auditory canal pressure-tympanic membrane displacement data and calculation of R-index External auditory canal pressure-tympanic membrane displacement data were acquired using 226Hz tympanometry (Rion Co., Ltd.) in 15 subjects without middle ear disease (control group, 30 ears), 41 subjects with middle ear softening disease (62 ears), and 11 subjects with middle ear sclerosis disease (12 ears).

[0077] The subjects in the middle ear softening disease group were as follows: thinning of the tympanic membrane (20 subjects, 40 ears), acquired cholesteatoma (1 subject, 1 ear), patulous Eustachian tube (7 subjects, 8 ears), ossicular detachment (5 subjects, 5 ears), congenital middle ear malformation (7 subjects, 7 ears), and congenital cholesteatoma (1 subject, 1 ear). The subjects in the middle ear sclerosis group were as follows: tympanic membrane calcification (1 person, 1 ear), tympanosclerosis (2 people, 2 ears), unknown cause (1 person, 1 ear), and otosclerosis (7 people, 8 ears).

[0078] An exponential approximation formula was generated from the external auditory canal pressure-tympanic membrane displacement data obtained for each subject, and the time constant τ-index was calculated from this exponential approximation formula. Then, the R-index, an index related to the risk of middle ear disease, was calculated using the time constant τ-index and the static compliance (SC) in the external auditory canal pressure-tympanic membrane displacement data. The results are shown in Tables 1 to 4.

[0079] [Table 1]

[0080] [Table 2]

[0081] [Table 3]

[0082] [Table 4]

[0083] <Example 2> Analysis of intergroup differences in the R-index for each group The distribution of R-index for the control group, the otitis medial softening group, and the otitis media sclerosis group is shown in box plots (Figure 7). As a result, the R-index was low in the group with otomalacia and high in the group with otitis sclerosis.

[0084] (Kruskal-Wallis Certification Exam) Next, the Kruskal-Wallis test was performed on the R-index, an index obtained for each group (control group, otomalacia group, and otosclerosis group), to evaluate the differences between multiple groups. A p-value of less than 0.05 was considered statistically significant.

[0085] As a result, the statistic H = 78.26, and the significance probability p = 1.01 × 10⁻¹⁶. -17 The results showed extremely significant statistical differences across all groups. From these results, it was confirmed that the R-index, an index according to this embodiment, showed clearly different values ​​in the control group, the otitis medial softening disease group, and the otitis media sclerosis disease group, and that it accurately reflects the state of each disease.

[0086] (Dunn method, Bonferroni correction) Since a significant difference was found in the Kruskal-Wallis test, Dunn's method with Bonferroni correction was performed as a multiple comparison test to conduct a detailed comparison between each group. The significance level after correction for multiple comparisons was determined based on the aforementioned criteria (p<0.05). The results are shown in Table 5.

[0087] [Table 5]

[0088] The analysis revealed statistically significant differences between the groups, as shown in Table 5. From these results, it was confirmed that the R-index, an index according to this embodiment, can be used to distinguish between a healthy control group and each disease group.

[0089] (ROC analysis) To evaluate the performance of the R-index, an index according to this embodiment, as an index for distinguishing between the control group and the group with otomalacia, ROC analysis was performed.

[0090] As a result, the AUC was 1.000, demonstrating that the R-index index has extremely high discriminative ability. Furthermore, the optimal threshold (cutoff value) calculated based on the Youden Index was R-index ≤ 91.91. When the group with otomalacia disease was identified using this threshold, the sensitivity was 1.00 and the specificity was 1.00 within the range of the data analyzed, indicating that the control group and the group with otomalacia disease could be completely distinguished.

[0091] These results demonstrate that by using the R-index as an indicator, it is possible to determine and distinguish the middle ear softening disease group from the control group with extremely high accuracy, using a specific threshold as the boundary. [Industrial applicability]

[0092] This invention is widely applicable in the medical and healthcare fields, as well as in the diagnostic equipment manufacturing industry. [Explanation of symbols]

[0093] 1. Evaluation System 2. Evaluation device 21 Processing Unit 211 Acquisition Department 212 1st calculation section 213 2nd calculation section 214 Evaluation Department 22 Memory section 23 Display section 3 Acquisition device 31 probes 32 Pressure Control Unit 33 Acoustic drive unit 34 Acoustic listening section 35 Control Unit 36 Communications Department 4 Network

Claims

1. A device for assessing the risk of middle ear disease in subjects, The system includes an evaluation unit that evaluates the risk of middle ear disease in the subject based on the R-index, an index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. A device for assessing the risk of middle ear disease in the target population.

2. The evaluation unit evaluates one or more middle ear diseases that the subject may have by comparing the R-index with one or more thresholds for determining the possibility of having one or more of the middle ear diseases. A device for evaluating the risk of middle ear disease in a subject according to claim 1.

3. The aforementioned middle ear disorder is at least one of the following: thinning of the tympanic membrane, patulous Eustachian tube, ossicular transection, congenital cholesteatoma, acquired cholesteatoma, and congenital middle ear malformation. The evaluation unit assesses that the lower the R-index value, the higher the risk of at least one of the following: thinning of the tympanic membrane, patulous Eustachian tube, ossicular detachment, congenital cholesteatoma, acquired cholesteatoma, and congenital middle ear malformation. A device for evaluating the risk of middle ear disease in a subject according to claim 1.

4. The aforementioned middle ear disease is at least one of tympanic membrane calcification, tympanosclerosis, and otosclerosis. The evaluation unit assesses that the higher the R-index value, the higher the risk of at least one of the following: tympanic membrane calcification, tympanosclerosis, and otosclerosis. A device for evaluating the risk of middle ear disease in a subject according to claim 1.

5. This is a middle ear disease risk assessment program for the target group. Computers, This unit functions as an evaluation unit that evaluates the risk of middle ear disease in the subject based on the R-index, an index related to the risk of middle ear disease, calculated using a time constant calculated by an exponential approximation formula generated from the subject's external auditory canal pressure-tympanic membrane displacement data and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. A middle ear disease risk assessment program for the target group.

6. A calculator for indicators related to the risk of middle ear disease, A first calculation unit generates an exponential approximation formula from the subject's external auditory canal pressure-tympanic membrane displacement data and calculates a time constant from the said exponential approximation formula. The system includes a second calculation unit that calculates an index R-index related to the risk of middle ear disease using the aforementioned time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. A calculator for indicators related to the risk of middle ear disease.

7. A program for calculating indicators related to the risk of middle ear disease, Computers, A first calculation unit generates an exponential approximation formula from the subject's external auditory canal pressure-tympanic membrane displacement data and calculates a time constant from the said exponential approximation formula. A second calculation unit is configured to calculate an index R-index related to the risk of middle ear disease using the aforementioned time constant and the static compliance in the external auditory canal pressure-tympanic membrane displacement data. A program for calculating indicators related to the risk of middle ear disease.

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