Equipment for corneal mechanics real-time non-contact detection

Abstract

Equipment for corneal mechanics real-time non-contact detection comprises an excitation device, a detection device and an auxiliary device, and the excitation device, the detection device and the auxiliary device work cooperatively to achieve real-time, non-contact and high-precision detection of corneal biomechanical characteristics. According to the equipment for corneal mechanics real-time non-contact detection, a high-precision excitation device is adopted, elastic waves are formed inside and outside the cornea by controlling airflow parameters, the wave velocity is detected and a corneal mechanics characteristic image is reconstructed by utilizing an optical coherence technology, and the resolution reaches the micron level. And a three-dimensional mechanical arm group in the auxiliary device ensures the accuracy and individuation of detection. The cornea wave velocity-modulus model comprehensively considers various biophysical characteristics of the cornea, cornea modulus information is accurately calculated, a visual three-dimensional image is generated through a three-dimensional reconstruction algorithm, and a quantitative basis is provided for cornea mechanical characteristic evaluation. The equipment improves the accuracy and reliability of a detection result, and is beneficial to early diagnosis and treatment of cornea related diseases.

Description

Technical Field

[0001] The invention relates to eye medical equipment, in particular to a device for real-time contactless detection of corneal mechanics. Background Art

[0002] The cornea is an important part of the eye, which plays a role in maintaining the structure and shape of the eyeball and protecting the eye. More importantly, the cornea is an important refractive matrix of the eye, contributing about 70% of the refraction of the eye. Corneal lesions can seriously affect vision. Therefore, it is very important to conduct relevant research on the cornea, which is an important part of understanding and treating corneal-related diseases.

[0003] According to current medical research, the normal cornea is composed of five layers. They are the epithelial cell layer (50 )、Anterior Descemet's membrane(15 ), matrix layer (450 )、Descemet's membrane(5 ) and endothelial cell layer (5 ). Each layer is composed of certain collagen fibers. The orientation and arrangement of these collagen fibers directly affect the mechanical properties of the cornea. The collagen fibers near the center of the cornea are denser than those in the peripheral cornea. At the same time, the density of collagen fibers decreases from front to back.

[0004] Research on diseases related to corneal mechanics is also driving the development of this field. Keratoconus is an eye disease characterized by the thinning of the central or paracentral cornea and its forward cone-shaped protrusion. The thinning and protrusion (cone) are related to the loss of the normal lamellar structure of the stromal tissue, resulting in a significant decrease in mechanical stability. For keratoconus, corneal cross-linking (CXL) is a treatment that has been shown to prevent the progression of keratoconus by directly increasing the biomechanical stability and stiffness of the cornea. Riboflavin is dripped onto the corneal surface and ultraviolet light is used to increase the mechanical properties of the cornea. However, the mechanical properties of the cornea before and after cross-linking cannot be evaluated in vivo, making it impossible for doctors to directly evaluate the surgical effect of collagen cross-linking. In addition, CXL needs to be performed in the early stages of keratoconus. The early diagnosis of keratoconus relies on the judgment of corneal biomechanics. Myopia is characterized by an increase in axial length, which prevents the eye from focusing light on the retina, resulting in blurred images. The more mature method of treating myopia is to perform femtosecond surgery on patients. Femtosecond laser surgery involves opening a corneal flap and cutting off a portion of it with a laser. Preoperative mechanical evaluation of the cornea can help doctors understand the condition of the cornea and facilitate the operation. Myopic refractive surgery may reduce the tensile strength of the cornea, leading to long-term instability and the risk of iatrogenic corneal ectasia, a rare but serious complication. Postoperative mechanical evaluation of the cornea can detect corneal ectasia in time and nip the disease in the bud. Glaucoma is mainly caused by damage to the optic nerve due to increased intraocular pressure. At present, the measurement of intraocular pressure is based on the standard proposed by Goldman: it is assumed that the pressure in an ideal dry thin-walled sphere is equal to the force required to flatten its surface divided by its area, and the tonometer developed in this way is used to detect intraocular pressure.

[0005] In recent years, with the development of optical coherence tomography (OCT), it has become a reality to use OCT to observe corneal features. Compared with atomic force microscopy (AFM), ultrasonic imaging (UE), etc., the relative spatial resolution and penetration depth of OCT are more suitable for corneal observation and detection. The spatial resolution and imaging depth of optical coherence elastography (OCE) are both suitable for relevant research on the cornea.

[0006] OCT uses the principle of Michelson interferometer to obtain the depth information of the sample under test by using the interference information of coherent light. The excitation device is used to generate excitation, and then OCT is used for observation. This is optical coherence elastic imaging technology (OCE for short).

[0007] Therefore, the measurement of corneal mechanical properties is crucial. The development of instruments for measuring corneal mechanical properties is of great significance for doctors' preoperative diagnosis and evaluation, as well as the planning of postoperative treatment plans. The more mature instruments on the market are non-contact intraocular pressure analyzers (ORA and Corvis ST). ORA uses air jets to induce the cornea to move inward, through a small concave surface, and then returns to the normal curvature, and then uses a photoelectric infrared (IR) detection device to record corneal deformation. ORA's measurement is based on the pressure of two applanation events. At the same time, hospitals currently use the form of intraocular pressure testing to evaluate the mechanical properties of the cornea. However, such an evaluation is inaccurate. Because intraocular pressure and corneal mechanics cannot be equated. There is a complex coupling relationship between them.

[0008] The instrument currently on the market for evaluating corneal biomechanics is Corvis ST, which introduces the use of a high-speed (4300 frames / second) Scheimpflug camera to capture air-induced (maximum pressure: 25 kpa) corneal deformation. Based on the collected multi-frame images, multiple parameters are obtained. Based on a large amount of medical data and regression analysis, a new evaluation index SSI is proposed. The SSI index is calculated by linear equations based on the parameters in the multi-frame images. The specific regression data is based on the corneal hardness of a 50-year-old healthy sampler as an indicator, defined as SSI=1, so that the patient's corneal hardness is judged based on the size relationship between SSI and 1. This result has a large inaccuracy. Secondly, the SSI index obtained with the help of the Corvis ST device can only evaluate the overall value of the cornea, and cannot evaluate different areas of the cornea, nor can it evaluate the depth direction of the cornea. Secondly, Corvis ST needs to use air blowing to cause the cornea to deform at the millimeter level, and the patient's experience is poor. Therefore, it is necessary to develop a more accurate, real-time, safe and comfortable corneal mechanics measurement instrument.

[0009] CN110974148A discloses a detection method based on jet optical coherence elastic imaging technology. Its detection accuracy is still at the millimeter level, and no two-dimensional elastic image of the cornea is obtained. At the same time, the current corneal mechanical evaluation methods all use the corneal Lamb wave model, substitute the corneal wave velocity, and obtain the corneal elastic model according to the derivation formula. The model is derived by assuming that the corneal model is a linear elastic model. In fact, the corneal material is an anisotropic, viscoelastic, and inhomogeneous material. The estimation result using the linear elastic model is inaccurate. In addition, the model does not take into account the influence of the internal pressure of the cornea.

[0010] From the results of experiments and simulations, it can be seen that factors such as the curvature, thickness, elastic modulus and internal pressure hydration degree of the cornea will affect the wave velocity of the cornea. Therefore, it is inaccurate to directly substitute the experimentally measured corneal wave velocity data into the corneal Lamb wave model.

[0011] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0012] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a device for real-time contactless detection of corneal mechanics.

[0013] To achieve the above object, the present invention adopts the following technical solutions: A device for real-time non-contact detection of corneal mechanics, comprising: The excitation device includes an air pump, a pressure regulating valve, a high-frequency electromagnetic valve and a microliter nozzle needle, which are sequentially connected through an air pipe and are used to accurately control the pressure, frequency and time of the airflow. The airflow ejected by the microliter nozzle needle acts on the cornea to form elastic waves inside and on the surface of the cornea; A detection device is used to detect the propagation velocity of the elastic wave and reconstruct the mechanical property image of the cornea; the detection device comprises a light source, an optical fiber coupler, a balanced detector, a high-pass filter, a sample arm mirror group and a reference arm mirror group; the light generated by the light source is divided into two paths by the optical fiber coupler, and after being reflected by the sample arm mirror group and the reference arm mirror group respectively, it is received by the balanced detector and differentially processed, and then the high-frequency noise is filtered out by the high-pass filter, and finally the data acquisition card collects the signal and reconstructs the corneal image; An auxiliary device is used to control the position and angle of the microliter nozzle needle of the excitation device; the auxiliary device includes a three-dimensional mechanical arm group with six degrees of freedom, which is used to accurately adjust the excitation site according to the position of the cornea.

[0014] Furthermore, the microliter nozzle needle uses a needle pulling instrument to pull the glass tube into a needle with a tip, the tip size is controlled at 50-100 microns, and is observed and opened through an optical microscope to ensure that the airflow acts accurately on the cornea.

[0015] Furthermore, the starting frequency of the high-frequency solenoid valve can reach 1200 Hz, and continuous excitation is achieved by controlling the duration of the high level to ensure stable generation of elastic waves.

[0016] Furthermore, the pressure regulating valve is connected to the throttle valve via an air pipe, and the throttle valve is connected to the microliter nozzle needle via an air pipe. The pressure regulating valve and the throttle valve work together to adjust the airflow pressure at the microliter nozzle needle end to achieve personalized and precise control of the output pressure.

[0017] Furthermore, the detection device adopts a frequency domain form to convert the phase information in the corneal motion electrical signal into the motion information of the cornea, and then calculates the Young's modulus of the cornea.

[0018] Furthermore, the robotic arm group in the auxiliary device includes six servos, which enable the microliter nozzle needle to have six degrees of freedom and can accurately control the excitation site according to the required position and angle.

[0019] Furthermore, the balanced detector processes the light reflected by the sample arm mirror group and the reference arm mirror group through a differential technology to improve the sensitivity and anti-interference ability of the photoelectric detector.

[0020] Furthermore, the detection device reconstructs a corneal mechanical wave propagation image under synchronous excitation state through multiple frames of static corneal images; calculates the modulus information of the cornea according to the wave velocity information in the corneal mechanical wave propagation image and the biophysical characteristics of the cornea through a corneal wave velocity-modulus reconstruction model; based on the output of the corneal wave velocity-modulus reconstruction model, the modulus information is spatially visualized through a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastic mechanical image.

[0021] Furthermore, the detection device constructs a corneal wave velocity-modulus reconstruction model and is configured to achieve corneal wave velocity-modulus reconstruction through the following processing: De-noising: average the collected electrical signals of corneal movement, average the signal information at multiple times to remove high-frequency noise and obtain the real information of the detection point in the previous time period; Artifact removal: Perform Fourier transform on the denoised signal to obtain a complex-valued signal, and then use high-pass filtering to remove low-frequency noise below a specific cutoff frequency. This low-frequency noise is caused by physiological factors during the detection process and is significantly different from the excitation frequency. Speckle tracking: Based on the laser speckle phenomenon, the movement or deformation of the cornea is tracked and measured by analyzing the changes in the speckle pattern, thereby obtaining information related to the movement of the cornea; Phase information extraction: Perform autocorrelation analysis on the complex-valued signal to obtain the phase information of the signal; Displacement information calculation: Based on the thickness information and boundary conditions of the cornea, combined with the propagation form of the elastic wave generated by the excitation, the displacement information of the cornea is calculated using the phase information; Wave velocity information acquisition: Fourier transform the displacement information to obtain the wave number information, and then solve it to obtain the phase velocity; Modulus image reconstruction: The wave velocity information obtained is combined with the biophysical characteristics of the cornea, and the specific modulus of the cornea is calculated with the help of a neural network, and assigned to the original phase velocity image to generate an elastic image of the cornea; the biophysical characteristics of the cornea include corneal thickness, corneal curvature, internal pressure and degree of hydration.

[0022] Furthermore, the neural network includes a convolutional neural network (CNN), a long short-term memory network (LSTM), and a multi-layer perceptron (MLP). CNN is used to extract features of input data and convert feature maps into one-dimensional feature vectors through a flattening operation. LSTM is used to process time series data, further extract time series features, and convert them into one-dimensional vectors through a flattening operation. The output feature vectors of CNN and LSTM are fused to form a comprehensive feature representation. MLP receives the fused feature vectors and finally outputs the modulus information of the cornea through multi-layer nonlinear transformation. The network structure is optimized through a loss function to achieve accurate corneal modulus prediction. The present invention has the following beneficial effects: The present invention proposes a device for real-time contactless detection of corneal mechanics, which realizes accurate, safe and real-time detection of corneal biomechanical properties by innovatively introducing a microliter excitation nozzle and a corneal wave velocity-modulus model. The device adopts a high-precision excitation device, including an air pump, a pressure regulating valve, a high-frequency solenoid valve, a throttle valve and a microliter nozzle needle. By accurately controlling the pressure, frequency and time of the airflow, elastic waves can be formed inside and on the surface of the cornea, and combined with the light source, fiber coupler, balanced detector, high-pass filter, sample arm mirror group and reference arm mirror group in the detection device, the propagation wave velocity of the elastic wave is detected by optical coherence technology, and the mechanical property image of the cornea is reconstructed, and the resolution reaches the micron level, which significantly improves the detection accuracy and efficiency. The three-dimensional mechanical arm group in the auxiliary device has six degrees of freedom, which can accurately control the excitation site to ensure the accuracy and personalization of the detection process. Furthermore, the corneal wave velocity-modulus model of the present invention can accurately calculate the modulus information of the cornea according to the detected wave velocity information by comprehensively considering various biophysical characteristics such as corneal thickness, corneal curvature, internal pressure and hydration degree. This model combines a three-dimensional reconstruction algorithm to visualize modulus information in space and generate a three-dimensional corneal elastic mechanical image, thus providing a quantitative and intuitive basis for the evaluation of corneal mechanical properties. The detection device uses this model to not only improve the accuracy and reliability of the test results, but also achieve a refined evaluation of corneal mechanical properties in different regions and depth directions, which helps to more comprehensively understand the mechanical state of the cornea and provides strong support for the early diagnosis of corneal-related diseases, the formulation of treatment plans, and the evaluation of surgical effects.

[0023] Compared with the prior art, the present invention not only avoids the discomfort of contact detection, but also further improves the accuracy and reliability of the detection results through an algorithm that considers factors such as corneal thickness, intraocular pressure, and corneal curvature, providing an efficient and reliable solution for the diagnosis and treatment of corneal-related diseases.

[0024] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic structural diagram of an embodiment of the present invention.

[0026] Figure 2 This is a data processing flow chart of the corneal wave velocity-modulus reconstruction model according to an embodiment of the present invention.

[0027] Figure 3 4 is a wave velocity-modulus network structure diagram of an embodiment of the present invention.

[0028] Figure numerals: PC end 1, light source 2, fiber coupler 3, sample arm 4, trigger signal 5, balanced detector 6, high-pass filter 7, data acquisition card 8, air compressor 9, pressure regulating valve 10, high-frequency solenoid valve 11, microliter nozzle needle 12, pressure controller 13, mirror group 14, reference arm 15, reference mirror 16, reflecting mirror 17. DETAILED DESCRIPTION

[0029] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0030] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for fixing as well as for coupling or communication.

[0031] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0032] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0033] See also Figure 1The embodiment of the present invention provides a device for real-time contactless detection of corneal mechanics, including an excitation device, a detection device and an auxiliary device. The excitation device includes an air pump (such as an air compressor 9), a pressure regulating valve 10, a high-frequency electromagnetic valve 11 and a microliter nozzle needle 12, which are connected in sequence through an air pipe to accurately control the pressure, frequency and time of the airflow. The airflow ejected by the microliter nozzle needle 12 acts on the cornea to form elastic waves inside and on the surface of the cornea. The detection device is used to detect the propagation wave velocity of the elastic wave and reconstruct the mechanical property image of the cornea; the detection device includes a light source 2, a fiber coupler 3, a balanced detector 6, a high-pass filter 7, a sample arm and a reference arm 15; the light generated by the light source 2 is divided into two paths through the fiber coupler 3, respectively reflected by the sample arm mirror group and the reference arm mirror group, and then received by the balanced detector 6 and differentially processed, and then filtered out high-frequency noise through the high-pass filter 7, and finally the data acquisition card 8 collects the signal and reconstructs the corneal image. The auxiliary device is used to control the position and angle of the microliter nozzle needle of the excitation device; the auxiliary device includes a three-dimensional mechanical arm group with six degrees of freedom, which is used to accurately adjust the excitation site according to the position of the cornea. The excitation device, the detection device and the auxiliary device work together to achieve real-time, non-contact, and high-precision detection of the biomechanical properties of the cornea.

[0034] In a preferred embodiment, the microliter nozzle needle 12 is formed by pulling a glass tube into a needle with a pointed tip using a needle pulling instrument, the tip size is controlled at 50-100 microns, and is observed and opened using an optical microscope to ensure that the airflow acts accurately on the cornea.

[0035] In some embodiments, the starting frequency of the high-frequency solenoid valve can reach 1200 Hz, and continuous excitation is achieved by controlling the duration of the high level to ensure stable generation of elastic waves.

[0036] In some embodiments, the pressure regulating valve 10 is connected to the throttle valve through an air pipe, and the throttle valve is connected to the microliter nozzle needle 12 through an air pipe. The pressure regulating valve and the throttle valve work together to adjust the airflow pressure at the end of the microliter nozzle needle to achieve personalized and precise control of the output pressure.

[0037] In some embodiments, the detection device uses frequency domain format to convert phase information in the corneal motion electrical signal into corneal motion information, thereby calculating the Young's modulus of the cornea.

[0038] In some embodiments, the robotic arm group in the auxiliary device includes six servos, which enable the microliter nozzle needle to have six degrees of freedom and can accurately control the excitation site according to the required position and angle.

[0039] In some embodiments, the balanced detector processes the light reflected by the sample arm mirror group and the reference arm mirror group through differential technology to improve the sensitivity and anti-interference ability of the photoelectric detector.

[0040] In some embodiments, the high-pass filter is used to filter out high-frequency noise in the optical signal to improve the signal-to-noise ratio and transmission quality of the signal and ensure the accuracy of the detection result.

[0041] In a preferred embodiment, the detection device reconstructs a corneal mechanical wave propagation image under synchronous excitation by using multiple frames of static corneal images; calculates the modulus information of the cornea according to the wave velocity information in the corneal mechanical wave propagation image and the biophysical characteristics of the cornea by using a corneal wave velocity-modulus reconstruction model; based on the output of the corneal wave velocity-modulus reconstruction model, the modulus information is spatially visualized by a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastic mechanical image.

[0042] See also Figure 2 In a further preferred embodiment, the detection device constructs a corneal wave velocity-modulus reconstruction model and is configured to achieve corneal wave velocity-modulus reconstruction through the following processing: De-noising: average the collected electrical signals of corneal movement, average the signal information at multiple times to remove high-frequency noise and obtain the real information of the detection point in the previous time period; Artifact removal: Perform Fourier transform on the denoised signal to obtain a complex-valued signal, and then use high-pass filtering to remove low-frequency noise below a specific cutoff frequency. This low-frequency noise is caused by physiological factors during the detection process and is significantly different from the excitation frequency. Speckle tracking: Based on the laser speckle phenomenon, the movement or deformation of the cornea is tracked and measured by analyzing the changes in the speckle pattern, thereby obtaining information related to the movement of the cornea; Phase information extraction: Perform autocorrelation analysis on the complex-valued signal to obtain the phase information of the signal; Displacement information calculation: Based on the thickness information and boundary conditions of the cornea, combined with the propagation form of the elastic wave generated by the excitation, the displacement information of the cornea is calculated using the phase information; Wave velocity information acquisition: Fourier transform the displacement information to obtain the wave number information, and then solve it to obtain the phase velocity; Modulus image reconstruction: The obtained wave velocity information is combined with factors such as corneal thickness, corneal curvature, internal pressure and hydration degree. The specific modulus of the cornea can be calculated with the help of a neural network and assigned to the original phase velocity image to generate an elastic image of the cornea.

[0043] See also Figure 3In a further preferred embodiment, the neural network includes a convolutional neural network CNN, a long short-term memory network LSTM and a multi-layer perceptron MLP, CNN is used to extract the features of the input data, and convert the feature map into a one-dimensional feature vector through a flattening operation, LSTM is used to process time series data, further extract time series features, and convert them into a one-dimensional vector through a flattening operation; the output feature vectors of CNN and LSTM are fused to form a comprehensive feature representation; MLP receives the fused feature vector, and finally outputs the modulus information of the cornea through multi-layer nonlinear transformation; the network structure is optimized through the loss function to achieve accurate corneal modulus prediction.

[0044] The corneal mechanics measuring instrument of the present invention constructs a high-precision and innovative detection mechanism by introducing key components such as a light source, a fiber coupler, a balance detector, a high-pass filter, a sample arm mirror group, and a reference arm mirror group, which can improve the detection resolution to the micron level, significantly improving the detection accuracy and efficiency of the measuring instrument. Different from the traditional contact press detection, the present invention uses an optical detection module to achieve non-contact precision measurement, avoiding the patient's discomfort. The three-dimensional mechanical arm group in the auxiliary device can accurately control the position and angle of the excitation microliter nozzle needle to ensure the efficiency and accuracy of the detection process. The excitation device includes components such as an air pump, a pressure regulating valve, a high-frequency solenoid valve, a base, a throttle valve, and a microliter nozzle needle. Through the precise control of the air path, personalized adjustment of the excitation pressure, frequency, time, and angle can be achieved, thereby ensuring the safety and accuracy of the detection.

[0045] The microliter nozzle needle plays an important role in the present invention. The airflow ejected by the microliter nozzle acts on the cornea, forming elastic waves inside and on the surface of the cornea, and finally evaluating the mechanical properties of the cornea by detecting the propagation velocity of the elastic waves. Since the wave velocity at the nozzle excitation point cannot be directly detected, the micro size of the microliter nozzle needle is used to obtain high precision of the detection result. At the same time, by using the designed corneal wave velocity-modulus model, the present invention can accurately calculate the modulus information of the cornea according to the detected elastic wave propagation velocity, combined with various biophysical characteristics of the cornea, and generate an elastic mechanical image of the cornea through a three-dimensional reconstruction algorithm. The present invention innovatively combines the microliter excitation nozzle and the corneal wave velocity-modulus model to achieve accurate, safe and real-time detection of the mechanical properties of the cornea. Compared with the prior art, the present invention has higher detection accuracy and better real-time performance, can provide a safe, comfortable and accurate excitation method, and ensure the reliability and accuracy of the detection results. In addition, by controlling the frequency, time, angle and pressure of the excitation, the present invention can achieve personalized corneal biomechanical measurement.

[0046] Compared with the existing solutions, the corneal mechanics measuring instrument of the present invention fully considers the influence of factors such as corneal thickness, intraocular pressure, and corneal curvature on the measurement results. Combined with the data accumulated from the finite element simulation, phantom experiments, and in vitro experiments conducted in the early stage, the present invention proposes an algorithm that comprehensively considers factors such as corneal thickness, intraocular pressure, and corneal curvature, further improving the accuracy and reliability of the test results. Overall, the present invention not only improves the accuracy and comfort of the test, but also significantly improves the convenience and practicality of the test through the optimization of the mechanical structure and the introduction of personalized design, providing an efficient and reliable solution for corneal mechanics testing.

[0047] The following further describes specific embodiments of the present invention, algorithm examples and experimental verification.

[0048] See also Figure 1 , a device for real-time contactless detection of corneal mechanics mainly includes an excitation device, a detection device and an auxiliary device. The excitation device includes an air pump, a pressure regulating valve, a high-frequency solenoid valve and a microliter nozzle needle. The entire excitation device is connected to the air pipe to realize the gas passage. The detection device includes a light source, a light coupler, a balance detector, a high-pass filter, and a sample arm mirror group and a reference arm mirror group. The auxiliary device is a three-dimensional mechanical arm group. The three-dimensional mechanical arm group is installed on the microliter nozzle needle to control the excitation to reach the desired position. The present invention innovates the microliter excitation nozzle and the corneal wave velocity-modulus model to realize accurate, safe and real-time corneal mechanics detection function. Compared with the prior art, the present invention has higher detection accuracy and better real-time performance, and can realize safe, comfortable and accurate excitation and reliable and precise detection. Furthermore, by controlling the frequency, time, angle and pressure of the excitation, personalized accurate measurement of corneal biomechanics can be achieved.

[0049] In a specific embodiment, the excitation device includes an air pump, a pressure regulating valve, a high-frequency electromagnetic valve, a base, a throttle valve and a microliter nozzle needle. The air pump is connected to the pressure regulating valve through an air pipe, the pressure regulating valve and the high-frequency electromagnetic valve are connected to the base through an air pipe, the air outlet of the base is connected to the throttle valve through an air pipe, and the throttle valve is connected to the microliter nozzle needle through an air pipe. The detection device includes a light source, an optical coupler, a balanced detector, a high-pass filter, and a sample arm mirror group and a reference arm mirror group. The light source generates light, and the light is divided into two paths through the optical fiber coupler, passing through the sample arm group and the reference arm group respectively. The light reflected by the sample arm group and the reference arm group passes through the balanced detector, and the sensitivity and anti-interference ability of the photoelectric detector are improved through the differential technology. Afterwards, the light passes through a high-frequency filter to filter out the high-frequency noise in the optical signal, thereby improving the signal-to-noise ratio and transmission quality of the signal. Finally, the light reaches the data acquisition card, and is compared with the light emitted by the light source, and a static corneal image is reconstructed through an algorithm. With the help of multiple frames of static corneal images, the mechanical wave propagation image of the cornea under the synchronous excitation state is obtained. Through the algorithm, the corneal Young's modulus image is obtained. In addition, through the three-dimensional reconstruction algorithm, a three-dimensional corneal elastic mechanical image is obtained. The auxiliary device is a three-dimensional mechanical arm group. The three-dimensional mechanical arm group is installed on the micro-liter nozzle needle to control the excitation to reach the desired position. With the help of the lens positioning algorithm of the detection device, the corneal position is obtained, based on which the auxiliary device is controlled to locate the appropriate excitation position.

[0050] More specifically, the auxiliary module includes a mechanical arm group, which contains six servos inside, ensuring that the microliter nozzle needle has six degrees of freedom and can accurately adjust the excitation site according to the required position and angle. The high-frequency solenoid valve starting frequency can reach 1200Hz. By controlling the duration of the high level, the effect of continuous excitation can be achieved. The pressure regulating valve and the throttle valve play the role of adjusting the pressure at the end of the microliter nozzle needle. Through the coordinated adjustment of these two modules, the output end pressure can be personalized and accurately controlled. The detection device adopts the frequency domain form, and converts the phase information in the corneal motion electrical signal into the corneal motion information, thereby obtaining the Young's modulus of the cornea.

[0051] More specifically, the detection device reconstructs a corneal mechanical wave propagation image under a synchronous excitation state through multiple frames of static corneal images; calculates the modulus information of the cornea according to the wave velocity information in the corneal mechanical wave propagation image and the biophysical characteristics of the cornea through a corneal wave velocity-modulus reconstruction model; based on the output of the corneal wave velocity-modulus reconstruction model, the modulus information is spatially visualized through a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastic mechanical image.

[0052] The specific data processing flow of the corneal wave velocity-modulus reconstruction model is as follows: Figure 2As shown. With the aid of the data acquisition card, the electrical signal information of corneal movement is obtained, which is the original image. After that, the following steps need to be performed.

[0053] Denoising: The original image will have a lot of high-frequency noise. Therefore, the obtained electrical signal needs to be denoised. The specific operation is: average the signal information of 10 moments as the real information of the detection point in the previous time period. That is:

[0054] Artifact removal: In addition to the original high-frequency noise brought by the system, the processed image also contains low-frequency noise caused by factors such as heartbeat, blood vessels, blinking, and unconscious head movement during the detection process. After testing, the frequency domain of the noise is <100Hz. Different from the excitation frequency of 1200Hz. High-pass filtering is used to remove low-frequency noise. The collected denoised signal is Fourier transformed to obtain a complex value signal F(z). That is:

[0055] Then use high-pass filtering to remove low-frequency signals below 100Hz. The specific formula is:

[0056] in is the cut-off frequency, R is the set resistance value, and C is the set capacitance value.

[0057] Speckle tracking: The speckle tracking principle is based on the laser speckle phenomenon, which tracks and measures the movement or deformation of an object by analyzing the changes in the speckle pattern. When the laser is irradiated onto the surface of an object, due to the microscopic rough structure of the surface of the object, the reflected light interferes with each other to form a speckle pattern. When the object moves or deforms, the speckle pattern changes accordingly. By capturing and analyzing these changes, the object's motion state or internal stress, displacement and other information can be inferred.

[0058] Phase information: The phase information of the signal can be obtained by performing autocorrelation analysis on the complex-valued signal.

[0059] Displacement information: Based on the thickness information of the cornea and the boundary conditions of the cornea, the upper layer is air and the lower layer is aqueous humor. The elastic wave generated by the excitation is mainly in the form of Lamb waves and propagates in the asymmetric form of A0-mode. Based on this, the displacement information of the cornea can be obtained with the help of the following formula.

[0060]

[0061] Wave velocity information: Fourier transform the displacement information to obtain wave number information, which is the basic parameter for wave velocity calculation. Then the phase velocity, i.e. wave velocity, is solved according to the following formula.

[0062]

[0063] Modulus image: With the help of the obtained two-dimensional wave velocity image, taking into account the factors affecting the wave velocity, intraocular pressure IOP, hydration degree , corneal thickness CCT, corneal curvature , the specific modulus of the cornea is obtained by using a neural network, and finally assigned to the original phase velocity image to obtain the elasticity image of the cornea. Preferably, the designed network structure is as follows Figure 3 shown. Figure 3 The wave velocity-modulus network shown includes three main components: convolutional neural network (CNN), long short-term memory network (LSTM) and multi-layer perceptron (MLP). First, CNN is used to extract the features of the input data and convert the feature map into a one-dimensional feature vector through a flattening operation. Next, LSTM processes the time series data, further extracts the time series features, and converts them into a one-dimensional vector through a flattening operation. Then, the output feature vectors of CNN and LSTM are fused to form a comprehensive feature representation. Finally, MLP receives the fused feature vector and finally outputs the modulus information of the cornea through multiple layers of nonlinear transformation. The entire network structure is optimized by the loss function to achieve accurate corneal modulus prediction.

[0064] The present invention not only improves the accuracy and comfort of detection, but also reduces the difficulty of operation and improves the convenience of use through the introduction of mechanical structure, and provides an efficient and reliable solution for the detection of corneal mechanics. The corneal mechanics measuring instrument of the present invention constructs an accurate and novel detection mechanism by introducing key components such as light source, fiber coupler, balance detector, high-pass filter, sample arm mirror group and reference arm mirror group, which can improve the resolution of detection to micrometer level and significantly improve the detection accuracy and detection efficiency of the measuring instrument. At the same time, different from the contact-type press detection, with the help of optical detection module, non-contact accurate measurement can be achieved. The mechanical arm group in the auxiliary device, combined with the positioning algorithm, realizes the precise positioning of the excitation microliter nozzle needle, ensuring the accurate and efficient operation of the detection process. The excitation device includes an air pump, a pressure regulating valve, a high-frequency solenoid valve, a base, a throttle valve, and a microliter nozzle needle. Through the precise control of the air path, the precise control of the excitation pressure and excitation time can be achieved, thereby ensuring the accuracy, personalization and safety of the detection. Overall, the present invention not only improves the accuracy and comfort of detection, but also improves the accuracy of detection and meets personalized needs through the introduction of mechanical structure, improves the convenience of use, and provides an efficient and reliable solution for the detection of corneal mechanics.

[0065] Compared with the existing solutions, the corneal mechanics measuring instrument of the present invention takes into account the influence of corneal thickness, intraocular pressure, corneal curvature, etc. on the measurement results. Combining the data accumulated from the finite element simulation, phantom experiments, in vitro experiments, etc. conducted in the early stage, an algorithm considering factors such as corneal thickness, intraocular pressure, corneal curvature, etc. is proposed to make the results more accurate and reliable.

[0066] The multifunctional corneal detection device of the present invention realizes efficient and accurate corneal mechanical measurement through optimized mechanical design and integrated diagnostic function, which greatly facilitates clinical operation.

[0067] During the test, the person to be tested puts his head in the designated position and clicks "Start Positioning". The testing device uses the positioning algorithm to find the corneal position, then moves the device, aligns the lens with the cornea, and prepares for shooting. After that, a prompt will be given to complete the positioning. Then set the jet mode according to the test requirements, or directly select the default mode. After the selection is completed, you can click "Start Testing". The excitation device generates a jet, and the detection device collects electrical signals for detection. Finally, according to the internal algorithm, the corneal image is reconstructed, the corneal mechanical property parameters are corrected, and the corneal elastic topography of the tester is obtained, which is convenient for the doctor to make a judgment.

[0068] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0069] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0070] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0071] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0072] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0073] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0074] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0075] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0076] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0077] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0078] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0079] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0080] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A device for real-time non-contact detection of corneal mechanics, characterized in that: include: The excitation device includes an air pump, a pressure regulating valve, a high-frequency electromagnetic valve and a microliter nozzle needle, which are sequentially connected through an air pipe and are used to accurately control the pressure, frequency and time of the airflow. The airflow ejected by the microliter nozzle needle acts on the cornea to form elastic waves inside and on the surface of the cornea; A detection device is used to detect the propagation velocity of the elastic wave and reconstruct the mechanical property image of the cornea; the detection device comprises a light source, an optical fiber coupler, a balanced detector, a high-pass filter, a sample arm mirror group and a reference arm mirror group; the light generated by the light source is divided into two paths by the optical fiber coupler, and after being reflected by the sample arm mirror group and the reference arm mirror group respectively, it is received by the balanced detector and differentially processed, and then the high-frequency noise is filtered out by the high-pass filter, and finally the data acquisition card collects the signal and reconstructs the corneal image; An auxiliary device is used to control the position and angle of the microliter nozzle needle of the excitation device; the auxiliary device includes a three-dimensional mechanical arm group with six degrees of freedom, which is used to accurately adjust the excitation site according to the position of the cornea.

2. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The microliter nozzle needle is pulled into a needle shape with a tip by a needle pulling instrument through a glass tube, and the tip size is 50-100 microns.

3. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The starting frequency of the high-frequency solenoid valve reaches 1200 Hz, and continuous excitation is achieved by controlling the duration of the high level to ensure stable generation of elastic waves.

4. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The pressure regulating valve is connected to the throttle valve through an air pipe, and the throttle valve is connected to the microliter nozzle needle through the air pipe.

5. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The detection device adopts the frequency domain form, and converts the phase information in the corneal motion electrical signal into the corneal motion information, and then calculates the Young's modulus of the cornea.

6. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The mechanical arm group in the auxiliary device includes six servos, which enable the microliter nozzle needle to have six degrees of freedom and can accurately adjust the excitation site according to the required position and angle.

7. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The balanced detector processes the light reflected by the sample arm mirror group and the reference arm mirror group through a differential technology to improve the sensitivity and anti-interference ability of the photoelectric detector.

8. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that: The detection device reconstructs a corneal mechanical wave propagation image under synchronous excitation state through multiple frames of static corneal images; calculates the modulus information of the cornea according to the wave velocity information in the corneal mechanical wave propagation image and the biophysical characteristics of the cornea through a corneal wave velocity-modulus reconstruction model; and based on the output of the corneal wave velocity-modulus reconstruction model, spatially visualizes the modulus information through a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastic mechanical image.

9. The device for real-time non-contact detection of corneal mechanics according to claim 8, characterized in that: The detection device constructs a corneal wave velocity-modulus reconstruction model and is configured to achieve corneal wave velocity-modulus reconstruction through the following processing: De-noising: average the collected electrical signals of corneal movement, average the signal information at multiple times to remove high-frequency noise and obtain the real information of the detection point in the previous time period; Artifact removal: Perform Fourier transform on the denoised signal to obtain a complex-valued signal, and then use high-pass filtering to remove low-frequency noise below a specific cutoff frequency. This low-frequency noise is caused by physiological factors during the detection process and is significantly different from the excitation frequency. Speckle tracking: Based on the laser speckle phenomenon, the movement or deformation of the cornea is tracked and measured by analyzing the changes in the speckle pattern, thereby obtaining information related to the movement of the cornea; Phase information extraction: Perform autocorrelation analysis on the complex-valued signal to obtain the phase information of the signal; Displacement information calculation: Based on the thickness information and boundary conditions of the cornea, combined with the propagation form of the elastic wave generated by the excitation, the displacement information of the cornea is calculated using the phase information; Wave velocity information acquisition: Fourier transform the displacement information to obtain the wave number information, and then solve it to obtain the phase velocity; Modulus image reconstruction: The wave velocity information obtained is combined with the biophysical characteristics of the cornea, and the specific modulus of the cornea is calculated with the help of a neural network, and assigned to the original phase velocity image to generate an elastic image of the cornea; the biophysical characteristics of the cornea include corneal thickness, corneal curvature, internal pressure and degree of hydration.

10. The device for real-time non-contact detection of corneal mechanics according to claim 9, characterized in that: The neural network includes a convolutional neural network (CNN), a long short-term memory (LSTM) network and a multi-layer perceptron (MLP). The CNN is used to extract features of input data and convert feature maps into one-dimensional feature vectors through a flattening operation. The LSTM is used to process time series data, further extract time series features, and convert them into one-dimensional vectors through a flattening operation. The output feature vectors of the CNN and LSTM are fused to form a comprehensive feature representation. The MLP receives the fused feature vectors and finally outputs the modulus information of the cornea through multi-layer nonlinear transformation. The network structure is optimized through a loss function to achieve accurate corneal modulus prediction.

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