Dry eye detection and analysis system based on nasal endoscope

Abstract

The invention relates to the field of medical instruments, in particular to a dry eye detection and analysis system based on a nasal endoscope, which comprises a user interaction module, a dry eye detection module, a data processing module and a dry eye analysis module, the user interaction module is used for receiving a user instruction and patient data, the dry eye detection module is used for carrying out various dry eye detection on a patient and collecting detection data, and the data processing module is used for carrying out data processing on the detection data of the dry eye detection module and the patient data of the user interaction module. And the dry eye analysis module is used for analyzing the dry eye condition of the patient according to the processed data and outputting a disease treatment effect index of the patient. According to the scheme, the operation effect is analyzed by comparing the dry eye conditions before and after the operation, and the corresponding disease treatment effect index is obtained, so that the postoperative rehabilitation condition of the patient is judged, a subsequent treatment scheme is formulated according to the rehabilitation condition, and the risk of disease relapse is reduced.

Description

Technical Field

[0001] This invention relates to the field of medical devices, and more particularly to a dry eye detection and analysis system based on a nasal endoscope. Background Technology

[0002] Dry eye syndrome is a common ophthalmic disease, mainly characterized by symptoms such as dry eyes, foreign body sensation, and blurred vision, which seriously affects patients' quality of life. The occurrence of dry eye syndrome is related to multiple factors, including insufficient tear secretion, excessive tear evaporation, and ocular surface diseases. Traditional methods for detecting dry eye mainly include:

[0003] For example, prior art CN105595960A discloses a head-mounted dry eye detection device, including an image acquisition unit, a DSP image processor, an ARM processor, an illumination unit, a touch screen, and a fixing unit. The ARM processor is used to display detection item options through the touch screen, determine the detection item selected by the user based on the touch information, generate an image acquisition command based on the user's selected detection item, and send the image acquisition command to the image acquisition unit. The image acquisition unit is used to acquire images according to the image acquisition command and send the acquired images to the DSP image processor. The DSP image processor is used to perform dry eye detection processing based on the images and send the detection result information to the ARM processor. The ARM processor is used to display the detection result information through the touch screen. The fixing unit is used to fix the device to the patient's head.

[0004] Another typical example is the dry eye treatment device disclosed in prior art CN118948523A, which solves the problem of high labor intensity for operators when pressing the tarsal plate in existing technologies. This invention includes a housing, a tarsal plate support inserted into the outer wall of the housing for supporting the tarsal plate, a pressing mechanism slidably disposed within the housing for pressing the meibomian glands, a driving mechanism disposed within the housing for driving the pressing mechanism, and a first heating mechanism disposed on the pressing mechanism for applying heat to the tarsal plate; the pressing mechanism is equipped with a pressure sensor for detecting the pressure applied to the tarsal plate, the tarsal plate support is equipped with a second heating mechanism, and the housing contains a microprocessor and a battery interconnected therewith.

[0005] Let's look at a novel dry eye detection device disclosed in the prior art, such as CN110101359B, which includes a novel Placido disc, an arc-shaped illumination source, an imaging objective lens, a dichroic mirror, a white light camera, and an infrared camera. The novel Placido disc is provided with several concentric black and white rings, wherein the white rings are light-transmitting parts, and the black rings are parts that transmit infrared light above 850nm but not visible light. The illumination source is used to provide a white light source and an infrared light source.

[0006] Currently, existing dry eye detection technologies are generally used before endoscopic dacryocystorhinostomy, without analyzing the surgical effect by comparing the dry eye condition before and after surgery, which can easily lead to recurrence. In addition, in most cases, nasal endoscopy is only used during surgery, without using nasal endoscopy to detect the postoperative effect. In order to solve the common problems in this field, this invention was made. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of current systems by proposing a dry eye detection and analysis system based on nasal endoscopy.

[0008] To overcome the shortcomings of the prior art, the present invention adopts the following technical solution:

[0009] A dry eye detection and analysis system based on nasal endoscopy, characterized in that it includes a user interaction module, a dry eye detection module, a data processing module, and a dry eye analysis module; the user interaction module is used to receive user instructions and patient data; the dry eye detection module is used to perform various dry eye tests on the patient and collect test data; the data processing module is used to process the test data from the dry eye detection module and the patient data from the user interaction module; and the dry eye analysis module is used to analyze the patient's dry eye condition based on the processed data and output indicators of the patient's disease treatment effect.

[0010] Furthermore, the user interaction module includes an input unit, a patient data storage unit, and a command generation unit. The input unit is used to receive and input user input, the patient data storage unit is used to store patient data input by the user through the input unit, and the command generation unit is used to generate detection commands based on user input and send them to the dry eye detection module.

[0011] Furthermore, the dry eye detection module includes a tear meniscus height detection unit, a tear secretion detection unit, and a microscopic imaging unit. The tear meniscus height detection unit is used to detect the tear meniscus height of the patient's lower eyelid margin, the tear secretion detection unit is used to detect the amount of tears secreted by the patient, and the microscopic imaging unit is used to obtain the total length of conjunctival microvessels per unit area, the length of nerve fibers per unit corneal area, and the average blood flow velocity in the main conjunctival vessels.

[0012] Furthermore, the data processing module includes a data input unit, a format conversion unit, and a noise reduction unit. The data input unit is used to input various detection data and user data from the user interaction module and the dry eye detection module. The format conversion unit is used to convert the format of the input data into a format that conforms to the input format of the dry eye analysis module. The noise reduction unit is used to reduce the noise of the format-converted data.

[0013] Furthermore, the dry eye analysis module includes a calculation unit, a calculation result storage unit, and a conclusion output unit. The calculation unit is used to calculate disease treatment effect indicators based on the data processed by the data processing module. The calculation result storage unit is used to save the calculation results and various parameters used for calculation. The conclusion output unit is used to output the disease treatment effect evaluation to the user interaction module based on the calculation results. The disease treatment effect indicators are calculated based on the total length of conjunctival microvessels per unit area, the average blood flow velocity in the main conjunctival vessels, the length of nerve fibers per unit corneal area, the number of nerve branches per unit corneal area, the current lacrimal meniscus height, the amount of staining fluid in the lower lacrimal punctum, the number of days since surgery, and the current wetting length.

[0014] Furthermore, the system's workflow includes the following steps:

[0015] S1, the user inputs patient data and test start command through the user interaction module, and the command generation unit generates a test command according to the test start command and sends it to the dry eye test module;

[0016] S2, the dry eye detection module performs various tests on the patient and sends the test data to the data processing module;

[0017] S3, the data processing module processes the test data and patient data;

[0018] S4, the dry eye analysis module calculates disease treatment effect indicators based on the processed test data and patient data, and outputs the corresponding evaluation to the user interaction module.

[0019] Furthermore, the dry eye analysis module calculates disease treatment effectiveness indicators and outputs corresponding evaluations to the user interaction module, including the following steps:

[0020] S41, The calculation unit calculates the disease treatment effect index based on the processed data;

[0021] S42, The calculation result storage unit saves the calculation results;

[0022] S43, The conclusion output unit is used to compare the calculation results with the threshold values ​​of each treatment effect index, and obtain the treatment effect level based on the threshold range in which the calculation results are located.

[0023] S44, The conclusion output unit outputs the calculation results and the obtained treatment effect level to the user interaction module.

[0024] The beneficial effects of this approach are: 1. By comparing the dry eye condition before and after surgery, the surgical effect can be analyzed and corresponding disease treatment effect indicators can be obtained. This helps to determine the patient's postoperative recovery and formulate subsequent treatment plans based on the recovery status, thus reducing the risk of disease recurrence.

[0025] 2. During the postoperative monitoring process, nasal endoscopy was used to detect the patient's dry eye condition. The combination of staining and image recognition technologies was used to assess the patient's condition, which helped to further understand the patient's condition on the basis of routine testing and further reduced the risk of recurrence. Attached Figure Description

[0026] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0027] Figure 1 This is a schematic diagram of the structure of the present invention.

[0028] Figure 2 This is a flowchart of the process of the present invention.

[0029] Figure 3 This is a graph showing the relationship between the current tear river height parameter and the current tear river height in this invention. The horizontal axis of the graph represents the current tear river height, and the vertical axis represents the current tear river height parameter. Detailed Implementation

[0030] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0031] Example 1: According to Figure 1 , Figure 2 , Figure 3 This embodiment provides a dry eye detection and analysis system based on nasal endoscopy, including a user interaction module, a dry eye detection module, a data processing module, and a dry eye analysis module. The user interaction module is used to receive user instructions and patient data. The dry eye detection module is used to perform various dry eye tests on the patient and collect test data. The data processing module is used to process the test data from the dry eye detection module and the patient data from the user interaction module. The dry eye analysis module is used to analyze the patient's dry eye condition based on the processed data and output the patient's disease treatment effect indicators.

[0032] Furthermore, the user interaction module includes an input unit, a patient data storage unit, and a command generation unit. The input unit is used to receive and input user input, the patient data storage unit is used to store patient data input by the user through the input unit, and the command generation unit is used to generate detection commands based on user input and send them to the dry eye detection module.

[0033] Specifically, the patient data includes the patient's past test data, as well as the patient's identity data and surgical data.

[0034] Furthermore, the dry eye detection module includes a tear meniscus height detection unit, a tear secretion detection unit, and a microscopic imaging unit. The tear meniscus height detection unit is used to detect the tear meniscus height of the patient's lower eyelid margin, the tear secretion detection unit is used to detect the amount of tears secreted by the patient, and the microscopic imaging unit is used to acquire microscopic images of the patient's eye.

[0035] Specifically, the tear meniscus height detection unit measures the tear meniscus height using an ocular surface analyzer; the tear secretion detection unit uses a Schirmer test strip (5mm*35mm), with the tip folded inward and inserted into the conjunctival sac at the junction of the outer and middle thirds of the lower eyelid, to measure the length of the test strip soaked with tear fluid within 5 minutes, thereby detecting the amount of tear secretion. Those skilled in the art can also use other tear secretion detection units to detect the tear meniscus height as needed; the microscopic imaging unit includes a laser speckle blood flow imager and a digital slit lamp.

[0036] Furthermore, the data processing module includes a data input unit, a format conversion unit, and a noise reduction unit. The data input unit is used to input various detection data and user data from the user interaction module and the dry eye detection module. The format conversion unit is used to convert the format of the input data into a format that conforms to the input format of the dry eye analysis module. The noise reduction unit is used to reduce the noise of the format-converted data.

[0037] Furthermore, the dry eye analysis module includes a calculation unit, a calculation result storage unit, and a conclusion output unit. The calculation unit is used to calculate disease treatment effect indicators based on the data processed by the data processing module. The calculation result storage unit is used to save the calculation results and various parameters used for calculation. The conclusion output unit is used to output the disease treatment effect evaluation to the user interaction module based on the calculation results.

[0038] Specifically, the conclusion output unit stores multiple disease treatment effect index thresholds. These thresholds are set by those skilled in the art based on experience and reference to past data. The conclusion output unit determines the threshold range in which the calculated disease treatment effect index falls, and then outputs the corresponding treatment effect level (e.g., level one, two, three, four, or excellent, good, moderate, poor, etc., which can be set by those skilled in the art as needed) as an evaluation. When the disease treatment effect evaluation is output to the user interaction module, the user module displays the calculation results and the corresponding disease treatment effect evaluation so that the user can understand the situation.

[0039] Furthermore, the system's workflow includes the following steps:

[0040] S1, the user inputs patient data and test start command through the user interaction module, and the command generation unit generates a test command according to the test start command and sends it to the dry eye test module;

[0041] S2, the dry eye detection module performs various tests on the patient and sends the test data to the data processing module;

[0042] S3, the data processing module processes the test data and patient data;

[0043] S4, the dry eye analysis module calculates disease treatment effect indicators based on the processed test data and patient data, and outputs the corresponding evaluation to the user interaction module.

[0044] Furthermore, the dry eye analysis module calculates disease treatment effectiveness indicators and outputs corresponding evaluations to the user interaction module, including the following steps:

[0045] S41, The calculation unit calculates the disease treatment effect index based on the processed data;

[0046] S42, The calculation result storage unit saves the calculation results;

[0047] S43, The conclusion output unit is used to compare the calculation results with the threshold values ​​of each treatment effect index, and obtain the treatment effect level based on the threshold range in which the calculation results are located.

[0048] S44, The conclusion output unit outputs the calculation results and the obtained treatment effect level to the user interaction module.

[0049] Specifically, the indicators of disease treatment effectiveness can be calculated using the following formula:

[0050] When HIGH≥H set And LONG ≥ LONG set1 hour:

[0051] ZB = zb

[0052] When HIGH <H set or LONG <LONG set1 hour:

[0053]

[0054] Among them, ZB is an indicator of disease treatment efficacy; the higher the value, the better the treatment effect. BLOODL is the total length of conjunctival microvessels per unit area. set For its corresponding reference value, BLOODV is the mean blood flow velocity in the main conjunctival vessels. set For its corresponding reference value, RAMUSL is the length of nerve fibers per unit corneal area. set For its corresponding reference value, RAMUSN is the number of nerve branches per unit corneal area. set Its corresponding reference value, which can be obtained through clinical trials on healthy individuals, is zb, representing the disease treatment effect parameter; when HIGH <H set or LONG <LONG set1 At this point, it is believed that the patient still has some degree of dry eye. Since there may be inflammation in the eye, combining BLOODL, BLOODV, RAMUSL, and RAMUSN can more accurately reflect the treatment effect. When HIGH ≥ H... set And LONG ≥ LONG set1 At that time, it was considered that the probability of the patient having dry eye was relatively small, and the values ​​of BLOODL, BLOODV, RAMUSL, and RAMUSN depended on the patient's own physical condition. In this case, BLOODL, BLOODV, RAMUSL, and RAMUSN were disregarded, and zb was directly considered to judge the treatment effect. In the above calculations... This method is used to comprehensively characterize the degree of vascular inflammation in patients. The higher the result, the more severe the inflammation and the worse the treatment effect. Dry eye is closely related to conjunctival microcirculation disorder. However, in existing technologies, the microcirculation capacity of the conjunctiva is generally not quantified and used to evaluate the treatment effect. This method introduces quantified conjunctival microcirculation capacity into the calculation of dry eye treatment effect, which is conducive to improving the comprehensiveness of the evaluation. This method is used to characterize the recovery of corneal nerve function, which is an important implicit indicator of dry eye treatment. However, it is not fully incorporated into the current routine assessment of dry eye treatment effectiveness. This scheme incorporates corneal nerve function recovery into the calculation of dry eye treatment effectiveness, which helps to improve the comprehensiveness of the evaluation. Specifically, by introducing a square root plus logarithm, the two parameters within the square root contribute to the calculation of the indicator. By using the square root, the product of the two parameters is prevented from being too large, which would have an excessive impact on the indicator. At the same time, by introducing the logarithm, the growth is faster when the logarithm is small and slower when it is large, preventing a single parameter from being too high and inflating the overall contribution. The 1 in the logarithm is to ensure that the logarithmic result starts from 0. BLOODL and BLOODV can be obtained through laser speckle flow imaging technology, while RAMUS and RAMUSN can be identified using a digital slit lamp with Class II medical device certification combined with the YOLO8 model.

[0055] H represents the current tear meniscus height parameter, H0 represents the tear meniscus height parameter detected before surgery (H0 can be obtained by referring to the method of H), HIGH represents the current tear meniscus height, and H... set The tear river height threshold is defined as follows: when the tear river height is less than this threshold, dry eye is considered to be present. This threshold is set by those skilled in the art based on experience and common knowledge in the field, and one possible setting is 0.2 mm. The value of H is set by classification: when HIGH is normal, H is set to 1, minimizing its contribution to ZB; when HIGH is abnormal, H is set to... Let the value of H increase its contribution to ZB based on HIGH; for example Figure 3 As shown, Figure 3 This is a graph showing the relationship between the current height parameter of the Tears River and the current height of the Tears River. The horizontal axis of the graph represents the current height of the Tears River, and the vertical axis represents the current height parameter of the Tears River.

[0056] L represents the current wetting length parameter, i.e., the detection result of the tear secretion detection unit. L0 represents the wetting length parameter detected before surgery; L0 can be obtained by referring to the method of L. LONG represents the current wetting length of the Schirmer test strip. set1 The first immersion length threshold, LONG set2The second immersion length threshold is defined as follows: when the immersion length is greater than the first immersion length threshold, dry eye is considered absent; when the immersion length is less than the first immersion length threshold but greater than the second immersion length threshold, mild dry eye is considered present; and when the immersion length is less than the second immersion length threshold, severe dry eye is considered present. This threshold is set by those skilled in the art based on experience and common knowledge. One possible setting for the first immersion length threshold is 10 mm, and one possible setting for the second immersion length threshold is 5 mm. By classifying and setting LONG, when LONG is normal, the value of L is set to 1; when LONG is slightly abnormal (mild dry eye), (LONG...) set2 ≤LONG <LONG set1 ), L is set to Let the value of L increase its contribution to ZB based on LONG; let LONG be severely abnormal (severe dry eye).

[0057] (LONG set2 >LONG), where L is set to... Further increase LONG to enhance its contribution to ZB, where the numerator is smaller than the denominator. Less than Using radicals can increase LONG's contribution to ZB.

[0058] e is a natural constant, and day is the number of days elapsed after the surgery. This can be achieved by setting... This approach facilitates the use of postoperative days (days) as a criterion for assessing treatment effectiveness. With other parameters remaining constant, a smaller day indicates faster patient recovery and better treatment outcomes. Furthermore, setting an exponential function helps to limit the influence of days on the indicator to between 1 and e^(-1 / 2). -1 It should be neither too large nor too small.

[0059] The units used in this embodiment are all conventional units in the art. When implementing this solution, those skilled in the art can make different designs and adopt corresponding units according to actual needs under the design concept of this application.

[0060] The beneficial effects of this approach are: 1. By comparing the dry eye condition before and after surgery, the surgical effect can be analyzed and corresponding disease treatment effect indicators can be obtained. This helps to determine the patient's postoperative recovery and formulate subsequent treatment plans based on the recovery status, thus reducing the risk of disease recurrence.

[0061] 2. During the postoperative monitoring process, nasal endoscopy was used to detect the patient's dry eye condition. The combination of staining and image recognition technologies was used to assess the patient's condition, which helped to further understand the patient's condition on the basis of routine testing and further reduced the risk of recurrence.

[0062] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them. It also includes a method for calculating a disease recurrence risk index based on treatment efficacy indicators. The disease recurrence risk index is used to evaluate the patient's disease recurrence risk; the higher the index value, the greater the patient's disease recurrence risk. This method calculates the disease recurrence risk index using the following formula:

[0063]

[0064] Among them, JBZB is a disease recurrence risk index, used to characterize the patient's risk of disease recurrence, ZB1 is a disease treatment effect index obtained at the first postoperative test, and ZB... x YZ3 is the threshold for the treatment effect index obtained at the xth postoperative test, where X is the number of postoperative tests, YZ2 is the threshold for the treatment effect index of the third-level disease, and YZ3 is the threshold for the treatment effect index of the second-level disease. When the treatment effect index is greater than YZ2, the treatment effect level is one; when the treatment effect index is less than YZ2 but greater than YZ3, the treatment effect level is two; and when the treatment effect index is less than YZ3, the treatment effect level is three. The thresholds for the treatment effect index are set by those skilled in the art based on experience and reference to past data. A value greater than 1 indicates that the patient's disease treatment effect is improving after each treatment, and the risk of disease recurrence is low. Mix YZ2*YZ3 with ZB1*ZB X For comparison, using YZ2*YZ3 as a reference, when ZB1 is larger, it is considered that the first treatment has a good effect and the risk of disease recurrence is low. X When the levels are relatively high, it is considered that the treatment effect is very good and the risk of disease recurrence is very small. When ZB1 and ZB... X When both ZB1 and ZB are relatively large, it is believed that the initial treatment and current treatment have been very effective, and the risk of disease recurrence is very small. When ZB1 is small and ZB is relatively large... X When ZB1 is large, it indicates a poor initial treatment outcome but good subsequent recovery, with a low risk of relapse. X When the difference between ZB1 and ZB1 is not significant (under normal treatment conditions, ZB1 is not significantly different). X According to ZB1, although the disease recovers slowly, the initial treatment has already achieved good results, and the risk of disease recurrence is low.

[0065] Specifically, when the value of X is 1, ZB x-1 The value is set to be the same as ZB1.

[0066] The beneficial effects of this embodiment are: by setting disease recurrence risk indicators, it is helpful to judge the risk of disease recurrence based on the patient's disease treatment effect indicators and the degree of change of disease treatment effect indicators at each follow-up examination, which helps doctors to formulate the next treatment plan based on the recurrence risk.

[0067] The above-disclosed content is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the description and drawings of the present invention are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops. The above units are merely examples, and those skilled in the art can adopt corresponding units according to actual needs when implementing this solution.

Claims

1. A dry eye detection and analysis system based on nasal endoscopy, characterized in that, It includes a user interaction module, a dry eye detection module, a data processing module, and a dry eye analysis module. The user interaction module is used to receive user instructions and patient data. The dry eye detection module is used to perform various dry eye tests on the patient and collect test data. The data processing module is used to process the test data from the dry eye detection module and the patient data from the user interaction module. The dry eye analysis module is used to analyze the patient's dry eye condition based on the processed data and output the patient's disease treatment effect indicators.

2. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The user interaction module includes an input unit, a patient data storage unit, and a command generation unit. The input unit is used to receive and input user input. The patient data storage unit is used to store patient data input by the user through the input unit. The command generation unit is used to generate detection commands based on user input and send them to the dry eye detection module.

3. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The dry eye detection module includes a tear meniscus height detection unit, a tear secretion detection unit, and a microscopic imaging unit. The tear meniscus height detection unit is used to detect the tear meniscus height of the patient's lower eyelid margin. The tear secretion detection unit is used to detect the amount of tears secreted by the patient. The microscopic imaging unit is used to obtain the total length of conjunctival microvessels per unit area, the length of nerve fibers per unit corneal area, and the average blood flow velocity in the main conjunctival vessels.

4. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The data processing module includes a data input unit, a format conversion unit, and a noise reduction unit. The data input unit is used to input various detection data and user data from the user interaction module and the dry eye detection module. The format conversion unit is used to convert the format of the input data into a format that conforms to the input format of the dry eye analysis module. The noise reduction unit is used to reduce the noise of the format-converted data.

5. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The dry eye analysis module includes a calculation unit, a calculation result storage unit, and a conclusion output unit. The calculation unit is used to calculate disease treatment effect indicators based on the data processed by the data processing module. The calculation result storage unit is used to save the calculation results and various parameters used in the calculation. The conclusion output unit is used to output the disease treatment effect evaluation to the user interaction module based on the calculation results. The disease treatment effect indicators are calculated based on the total length of conjunctival microvessels per unit area, the average blood flow velocity in the main conjunctival vessels, the length of nerve fibers per unit corneal area, the number of nerve branches per unit corneal area, the current lacrimal river height, the amount of staining fluid in the lower lacrimal punctum, the number of days since surgery, and the current wetting length.

6. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The system's workflow includes the following steps: S1, the user inputs patient data and test start command through the user interaction module, and the command generation unit generates a test command according to the test start command and sends it to the dry eye test module; S2, the dry eye detection module performs various tests on the patient and sends the test data to the data processing module; S3, the data processing module processes the test data and patient data; S4, the dry eye analysis module calculates disease treatment effect indicators based on the processed test data and patient data, and outputs the corresponding evaluation to the user interaction module.

7. The dry eye detection and analysis system based on nasal endoscopy according to claim 1, characterized in that, The dry eye analysis module calculates disease treatment effectiveness indicators and outputs corresponding evaluations to the user interaction module, including the following steps: S41, The calculation unit calculates the disease treatment effect index based on the processed data; S42, The calculation result storage unit saves the calculation results; S43, The conclusion output unit is used to compare the calculation results with the threshold values ​​of each treatment effect index, and obtain the treatment effect level based on the threshold range in which the calculation results are located. S44, The conclusion output unit outputs the calculation results and the obtained treatment effect level to the user interaction module.

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