A device for real-time non-contact detection of corneal mechanics

Abstract

A device for real-time non-contact detection of corneal mechanics, including an excitation device, a detection device, and an auxiliary device. The three work together to achieve real-time, non-contact, and high-precision detection of corneal biomechanical properties. The device for real-time non-contact detection of corneal mechanics uses a high-precision excitation device to form elastic waves inside and outside the cornea by controlling airflow parameters, and uses optical coherence technology to detect the wave speed and reconstruct the corneal mechanics property image with a resolution reaching the micron level. The three-dimensional robotic arm group in the auxiliary device ensures the accuracy and personalization of the detection. The corneal wave speed-modulus model comprehensively considers various biophysical properties of the cornea, accurately calculates the corneal modulus information, and generates an intuitive three-dimensional image through a three-dimensional reconstruction algorithm, providing a quantitative basis for the evaluation of corneal mechanics properties. The device improves the accuracy and reliability of the detection results, and helps in the early diagnosis and treatment of corneal-related diseases.

Description

Technical Field

[0001] The present invention relates to ophthalmic medical devices, and particularly to a device for real-time non-contact detection of corneal mechanics. Background Art

[0002] The cornea is an important part of the eye, playing a role in maintaining the structure of the eyeball and protecting the eye in a complete form. More importantly, the cornea is an important refractive medium of the eye, contributing approximately 70% to the eye's refraction. Corneal diseases can seriously affect vision. Therefore, relevant research on the cornea is crucial and is an important part of understanding and treating corneal-related diseases.

[0003] According to current medical research, a normal cornea consists of five layers of structures. They are the epithelial cell layer (50 ), Bowman's layer (15 ), stroma layer (450 ), Descemet's membrane (5 ), and endothelial cell layer (5 ). Each layer is internally 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 also promotes the development of this field. Keratoconus is an eye disease characterized by the central or paracentral thinning and bulging forward of the cornea in a conical shape. The thinning and bulging (conical shape) are related to the loss of the normal lamellar structure of the stromal tissue, resulting in a significant reduction in mechanical stability. For keratoconus, corneal cross-linking (CXL) is a treatment method that has been proven to prevent the progression of keratoconus by directly increasing the biomechanical stability and hardness of the cornea. The mechanical properties of the cornea before and after cross-linking cannot be evaluated in vivo, making it impossible for doctors to directly assess the surgical effect of collagen cross-linking. Moreover, CXL needs to be performed in the early stage of keratoconus. The early diagnosis of keratoconus depends on the judgment of corneal biomechanics. Myopia is characterized by an extended axial length of the eye, which prevents the eye from focusing light onto the retina, resulting in blurred images. The currently more mature method for treating myopia is to perform femtosecond surgery on patients. Femtosecond laser surgery involves lifting the corneal flap and using a laser to cut off a part. Through the preoperative mechanical evaluation of the cornea, doctors can understand the corneal condition and facilitate the implementation of the surgery. Myopic refractive surgery may reduce the tensile strength of the cornea, leading to long-term instability and the risk of rare but serious complications - iatrogenic corneal ectasia. Through the postoperative mechanical evaluation of the cornea, corneal ectasia can be detected in a timely manner, nipping the disease in the bud. Glaucoma is mainly caused by damage to the optic nerve due to increased intraocular pressure. Currently, for the measurement of intraocular pressure, the standard proposed by Goldman is used: assuming 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 an applanation tonometer developed therefrom is used to detect intraocular pressure.

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

[0006] OCT uses the principle of a Michelson interferometer and obtains the depth information of the detected sample by means of the interference information of coherent light. Generating an excitation using an excitation device and then observing it using OCT is the optical coherence elastography (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 relatively mature instruments on the market are non-contact intraocular pressure analyzers (ORA and Corvis ST). ORA uses an air jet to induce the cornea to move inwards, pass through a small concave surface, and then return to the normal curvature. Then, an optoelectronic infrared (IR) detection device is used to record the corneal deformation. The measurement of ORA is based on the pressure of two applanation events. At the same time, currently, hospitals will evaluate the mechanical properties of the cornea by detecting intraocular pressure. However, such an evaluation is inaccurate. Because intraocular pressure and corneal mechanics are not equal. There is a complex coupling relationship between them.

[0008] The instrument for evaluating corneal biomechanics on the market currently is Corvis ST. Corvis ST introduces the use of a high-speed (4300 frames per second) Scheimpflug camera to capture air-induced (maximum pressure: 25 kpa) corneal deformation. According to the collected multi-frame images, multiple parameters are obtained. Based on a large amount of medical data, regression analysis is carried out, and a new evaluation index SSI is proposed. The SSI index is calculated through a linear equation based on the parameters in the multi-frame images. The specific regression data uses the corneal stiffness of 50-year-old healthy samples as an index, defined as SSI = 1. Based on this, according to the size relationship between SSI and 1, the corneal stiffness of the patient is judged. The result has a large inaccuracy. Secondly, the SSI index obtained by means of the Corvis ST device can only evaluate the overall value of the cornea, cannot evaluate different regions of the cornea, nor can it evaluate the depth direction of the cornea. Secondly, Corvis ST needs to blow air to cause millimeter-level deformation of the cornea, 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 based on jet optical coherence elastography technology. Its detection accuracy is still at the millimeter level, and a two-dimensional elastic image of the cornea has not been obtained. At the same time, current corneal mechanics evaluation methods all rely on the corneal Lamb wave model, substitute the corneal wave speed, and obtain the elastic model of the cornea according to the derived formula. This model is derived assuming that the cornea is a linear elastic model. In fact, the corneal material is an anisotropic, viscoelastic, inhomogeneous material. Using a linear elastic model for estimation, the result is inaccurate. Moreover, this model does not consider the influence of the internal pressure of the cornea.

[0010] Judging from the experimental and simulation results, factors such as the curvature, thickness, elastic modulus, and internal pressure hydration degree of the cornea will affect the corneal wave speed. Therefore, directly substituting the experimentally measured corneal wave speed data into the corneal Lamb wave model is inaccurate.

[0011] It should be noted that the information disclosed in the above background art is only for understanding the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0012] The main object of the present invention is to overcome the defects existing in the above background art, and to provide a device for real-time non-contact detection of corneal mechanics.

[0013] To achieve the above object, the present invention adopts the following technical solutions:

[0014] A device for real-time non-contact detection of corneal mechanics, comprising:

[0015] An excitation device, including an air pump, a pressure regulating valve, a high-frequency solenoid valve and a microliter spray needle, which are connected in sequence through an air pipe, and are used to accurately control the pressure, frequency and time of the air flow. The air flow ejected by the microliter spray needle acts on the cornea to form elastic waves inside and on the surface of the cornea;

[0016] A detection device, 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, 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 through 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 subjected to differential processing, and then the high-frequency noise is filtered by the high-pass filter. Finally, the data acquisition card collects the signal and reconstructs the corneal image;

[0017] An auxiliary device, used to control the position and angle of the microliter spray needle of the excitation device; the auxiliary device includes a three-dimensional robotic arm group, which has six degrees of freedom and is used to accurately adjust the excitation site according to the corneal position.

[0018] Further, the microliter spray needle is formed by pulling a glass tube into a needle shape with a tip through a needle puller, and the tip size is controlled within 50-100 microns, and through optical microscope observation and opening operation, it is ensured that the air flow acts on the cornea accurately.

[0019] Further, 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, ensuring the stable generation of elastic waves.

[0020] Further, the pressure regulating valve is connected to a throttle valve through an air pipe, the throttle valve is connected to the microliter spray needle through an air pipe, and the pressure regulating valve and the throttle valve work together to adjust the air flow pressure at the end of the microliter spray needle, realizing personalized and accurate regulation of the output pressure.

[0021] Further, the detection device is in the form of frequency domain. By converting the phase information in the corneal motion electrical signal into the motion information of the cornea, the Young's modulus of the cornea is further calculated.

[0022] Further, the robotic arm group in the auxiliary device includes six servos, enabling the microliter nozzle needle to have six degrees of freedom and being able to accurately control the excitation site according to the required position and angle.

[0023] Further, the balance 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 photodetector.

[0024] Further, the detection device reconstructs the corneal mechanical wave propagation image in the synchronous excitation state through multiple frames of static corneal images; through the corneal wave velocity-modulus reconstruction model, according to the wave velocity information in the corneal mechanical wave propagation image, combined with the biophysical characteristic factors of the cornea, the modulus information of the cornea is calculated; based on the output of the corneal wave velocity-modulus reconstruction model, the modulus information is visualized spatially through a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastomechanics image.

[0025] Further, the detection device realizes the corneal wave velocity-modulus reconstruction by constructing a corneal wave velocity-modulus reconstruction model, which is configured to perform the following processing steps:

[0026] Denoising processing: The electrical signal of the corneal motion collected is averaged, and the signal information at multiple moments is averaged to remove high-frequency noise and obtain the real information of the detection point in the previous time period.

[0027] Artifact removal processing: The denoised signal is subjected to Fourier transform to obtain a complex-valued signal, and then the low-frequency noise below a specific cut-off frequency is removed through high-pass filtering. This low-frequency noise is caused by physiological factors and other factors during the detection process and is significantly different from the excitation frequency.

[0028] Speckle tracking: Based on the laser speckle phenomenon, the motion or deformation of the cornea is tracked and measured by analyzing the change of the speckle pattern, so as to obtain the motion-related information of the cornea.

[0029] Phase information extraction: The complex-valued signal is subjected to autocorrelation analysis to obtain the phase information of the signal.

[0030] Displacement information calculation: According to 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.

[0031] Wave velocity information acquisition: The displacement information is subjected to Fourier transform to obtain the wave number information, and then the phase velocity is solved.

[0032] Modulus image reconstruction: Combine the obtained wave velocity information with the biophysical characteristic factors of the cornea, and use a neural network to calculate the specific modulus of the cornea, and assign it to the original phase velocity image to generate the elastic image of the cornea; the biophysical characteristic factors of the cornea include corneal thickness, corneal curvature, internal pressure, and hydration level.

[0033] Further, the neural network includes a convolutional neural network CNN, a long short-term memory network LSTM, and a multi-layer perceptron MLP. The 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. The LSTM is used to process time series data, further extract temporal features, and convert them into a one-dimensional vector through a flattening operation; the output feature vectors of the CNN and the LSTM are fused to form a comprehensive feature representation; the MLP receives the fused feature vector and finally outputs the modulus information of the cornea through multiple non-linear transformations; the network structure is optimized through a loss function to achieve accurate prediction of the corneal modulus.

[0034] The present invention has the following beneficial effects:

[0035] The present invention provides a device for real-time non-contact detection of corneal mechanics. By innovatively introducing a microliter excitation nozzle and a corneal wave velocity-modulus model, accurate, safe, and real-time detection of corneal biomechanical characteristics is achieved. The device uses 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 precisely controlling the pressure, frequency, and time of the air flow, elastic waves can be formed inside and on the surface of the cornea. Combined with the light source, fiber optic 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 waves is detected using optical coherence technology, and the mechanical characteristic image of the cornea is reconstructed with a resolution reaching the micron level, significantly improving the detection accuracy and efficiency. The three-dimensional robotic arm group in the auxiliary device has six degrees of freedom and can precisely control the excitation site to ensure the accuracy and personalization of the detection process. Further, 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 characteristic factors such as corneal thickness, corneal curvature, internal pressure, and hydration level. This model combines a three-dimensional reconstruction algorithm to visualize the modulus information in space and generate a three-dimensional corneal elastic mechanics image, thus providing a quantitative and intuitive basis for the evaluation of corneal mechanical characteristics. Using this model, the detection device not only improves the accuracy and reliability of the detection results but also realizes the refined evaluation of corneal mechanical characteristics in different regions and depth directions, helping to more comprehensively understand the mechanical state of the cornea and providing strong support for the early diagnosis of corneal-related diseases, the formulation of treatment plans, and the evaluation of surgical effects.

[0036] 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 takes into account 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.

[0037] Other beneficial effects in the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic structural diagram of an embodiment of the present invention.

[0039] Figure 2 It is a data processing flow chart of the corneal wave velocity - modulus reconstruction model in an embodiment of the present invention.

[0040] Figure 3 It is a wave velocity - modulus network structure diagram of an embodiment of the present invention.

[0041] Reference numerals: PC terminal 1, light source 2, fiber optic 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, lens group 14, reference arm 15, reference mirror 16, reflector 17. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following makes a detailed description of the embodiments of the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope of the present invention and its applications.

[0043] It should be noted that when an element is referred to as "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 "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. Additionally, the connection can be for a fixing function or a coupling or communication function.

[0044] It should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0045] In addition, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0046] Referring to Figure 1 , an embodiment of the present invention provides a device for real-time non-contact detection of corneal mechanics, including an excitation device, a detection device, and an auxiliary device. The excitation device includes an air pump (such as air compressor 9), a pressure regulating valve 10, a high-frequency solenoid valve 11, and a microliter spray needle 12, which are connected in sequence through air pipes and are used to precisely control the pressure, frequency, and time of the air flow. The air flow ejected by the microliter spray 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 speed of the elastic waves and reconstruct the mechanical property image of the cornea; the detection device includes a light source 2, an optical 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 by the optical fiber coupler 3, reflected by the sample arm mirror group and the reference arm mirror group respectively, received by the balanced detector 6 and subjected to differential processing, and then the high-frequency noise is filtered by the high-pass filter 7. Finally, the signal is collected by a data acquisition card 8 and the corneal image is reconstructed. The auxiliary device is used to control the position and angle of the microliter spray needle of the excitation device; the auxiliary device includes a three-dimensional robotic arm group with six degrees of freedom, which is used to precisely regulate the excitation site according to the corneal position. Among them, 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.

[0047] In a preferred embodiment, the microliter spray needle 12 is formed by pulling a glass tube into a needle shape with a tip using a needle puller, and the tip size is controlled within 50-100 microns. Through observation with an optical microscope and opening operation, it is ensured that the air flow acts precisely on the cornea.

[0048] 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 the stable generation of elastic waves.

[0049] In some embodiments, the pressure regulating valve 10 is connected to a throttle valve through an air pipe, and the throttle valve is connected to the microliter spray needle 12 through an air pipe. The pressure regulating valve and the throttle valve work together to adjust the air flow pressure at the end of the microliter spray needle to achieve personalized and precise regulation of the output pressure.

[0050] In some embodiments, the detection device adopts a frequency-domain form. By converting the phase information in the corneal motion electrical signal into the motion information of the cornea, the Young's modulus of the cornea is then calculated.

[0051] In some embodiments, the robotic arm group in the auxiliary device includes six servo motors, enabling the microliter nozzle needle to have six degrees of freedom and capable of precisely regulating the excitation site according to the required position and angle.

[0052] In some embodiments, the balance 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 photodetector.

[0053] 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, ensuring the accuracy of the detection result.

[0054] In a preferred embodiment, the detection device reconstructs the corneal mechanical wave propagation image in the synchronous excitation state through multiple frames of static corneal images; through the corneal wave velocity-modulus reconstruction model, according to the wave velocity information in the corneal mechanical wave propagation image, combined with the biophysical characteristic factors of the cornea, the modulus information of the cornea is calculated; based on the output of the corneal wave velocity-modulus reconstruction model, the modulus information is visualized spatially through a three-dimensional reconstruction algorithm to generate a three-dimensional corneal elastomechanics image.

[0055] See Figure 2 , in a further preferred embodiment, the detection device realizes corneal wave velocity-modulus reconstruction through the following processing process by constructing a corneal wave velocity-modulus reconstruction model:

[0056] Denoising processing: The electrical signal of corneal motion collected is averaged, and the signal information at multiple moments is averaged to remove high-frequency noise and obtain the true information of the detection point in the previous time period.

[0057] Artifact removal processing: The denoised signal is subjected to Fourier transform to obtain a complex-valued signal, and then low-frequency noise below a specific cut-off frequency is removed through high-pass filtering. This low-frequency noise is caused by physiological factors and the like during the detection process and is significantly different from the excitation frequency.

[0058] Speckle tracking: Based on the laser speckle phenomenon, the motion or deformation of the cornea is tracked and measured by analyzing the change of the speckle pattern, thereby obtaining the motion-related information of the cornea.

[0059] Phase information extraction: Autocorrelation analysis is performed on the complex-valued signal to obtain the phase information of the signal.

[0060] 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;

[0061] Wave velocity information acquisition: The displacement information is Fourier-transformed to obtain the wave number information, and then the phase velocity is solved;

[0062] Modulus image reconstruction: By combining the obtained wave velocity information with factors such as corneal thickness, corneal curvature, internal pressure, and hydration degree, the specific modulus of the cornea can be calculated using a neural network and assigned to the original phase velocity image to generate the elastic image of the cornea.

[0063] See Figure 3 , in 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. The 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. The LSTM is used to process time series data, further extract temporal features, and convert them into a one-dimensional vector 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 vector and finally outputs the modulus information of the cornea through multi-layer non-linear transformation; the network structure is optimized through a loss function to achieve accurate prediction of the corneal modulus.

[0064] By introducing key components such as 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 corneal mechanical measuring instrument of the present invention constructs a high-precision and innovative detection mechanism, 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 pressing detection, the present invention realizes non-contact accurate measurement by means of an optical detection module, avoiding the discomfort of patients. The three-dimensional robotic arm group in the auxiliary device can accurately control the position and angle of the excitation microliter injection needle, ensuring 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 injection needle. Through precise control of the gas path, personalized adjustment of the excitation pressure, frequency, time, and angle can be achieved, thus ensuring the safety and accuracy of the detection.

[0065] The microliter spray head needle plays an important role in the present invention. The airflow ejected by it acts on the cornea, forming elastic waves inside and on the surface of the cornea. Finally, the mechanical properties of the cornea are evaluated by detecting the propagation wave speed of the elastic waves. Since the wave speed at the spray head excitation point cannot be directly detected, the small size of the microliter spray head needle is utilized to obtain high-precision detection results. At the same time, by using the designed corneal wave speed - modulus model, the present invention can accurately calculate the modulus information of the cornea based on the detected propagation wave speed of the elastic waves, combined with various biophysical characteristic factors of the cornea, and generate an elastic mechanics image of the cornea through a three-dimensional reconstruction algorithm. The present invention innovatively combines a microliter excitation spray head and a corneal wave speed - modulus model, realizing accurate, safe, and real-time detection of the mechanical properties of the cornea. Compared with the prior art, the detection accuracy of the present invention is higher, the real-time performance is better, it can provide a safe, comfortable, and accurate excitation method, and at the same time ensure the reliability and precision 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.

[0066] Compared with the existing solutions, the corneal mechanical property 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. Combining the data accumulated from the previous finite element simulations, phantom experiments, and ex vivo experiments, 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 detection results. Overall, the present invention not only improves the detection accuracy and comfort, but also significantly improves the detection convenience and practicality through the optimization of the mechanical structure and the introduction of personalized design, providing an efficient and reliable solution for corneal mechanical property detection.

[0067] The following further describes the specific embodiments, algorithm examples, and experimental verifications of the present invention.

[0068] Refer to Figure 1 A device for real-time non-contact 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 spray head needle. The entire excitation device realizes a gas path through tracheal connection. The detection device includes a light source, a light coupler, a balanced detector, a high-pass filter, and a sample arm mirror group and a reference arm mirror group. The auxiliary device is a three-dimensional robotic arm group. The three-dimensional robotic arm group is installed on the microliter spray head needle to control the excitation to reach the desired position. The present invention innovatively combines a microliter excitation spray head and a corneal wave speed - modulus model, realizing the functions of accurate, safe, and real-time corneal mechanics detection. Compared with the prior art, the detection accuracy of the present invention is higher, the real-time performance is better, it can achieve safe, comfortable, and accurate excitation and reliable and precise detection. Further, by controlling the frequency, time, angle, and pressure of the excitation, accurate personalized corneal biomechanical measurement is realized.

[0069] In a specific embodiment, the excitation device includes an air pump, a pressure regulating valve, a high-frequency solenoid valve, a base, a throttle valve, and a microliter spray head needle. The air pump is connected to the pressure regulating valve through an air pipe. The pressure regulating valve and the high-frequency solenoid 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 spray head needle through an air pipe. The detection device includes a light source, a fiber optic 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 fiber optic coupler and respectively passes through the sample arm group and the reference arm group. The light reflected back by the sample arm group and the reference arm group passes through the balanced detector. Through differential technology, the sensitivity and anti-interference ability of the photodetector are improved. Then, the light passes through a high-frequency filter to filter out high-frequency noise in the optical signal, improving the signal-to-noise ratio and transmission quality of the signal. Finally, the light reaches the data acquisition card. By comparing it with the light emitted by the light source and through an algorithm, a static corneal image is reconstructed. With the help of multiple frames of static corneal images, a mechanical wave propagation image of the cornea under synchronous excitation is obtained. Through an algorithm, a corneal Young's modulus image is obtained. In addition, through a three-dimensional reconstruction algorithm, a three-dimensional corneal elastomechanics image is obtained. The auxiliary device is a three-dimensional robotic arm group. The three-dimensional robotic arm group is installed on the microliter spray head 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, and based on this, the auxiliary device is controlled to position the appropriate excitation position.

[0070] More specifically, the auxiliary module includes a robotic arm group, which contains six servo motors inside, ensuring that the microliter spray head needle has six degrees of freedom and can accurately regulate the excitation site according to the required position and angle. The starting frequency of the high-frequency solenoid valve can reach 1200 Hz. By controlling the duration of the high-level signal, the effect of continuous excitation can be achieved. The pressure regulating valve and the throttle valve play a role in regulating the pressure at the end of the microliter spray head needle. Through the coordinated regulation of these two modules, the personalization of the output pressure can be realized, and accurate regulation can be achieved. The detection device adopts a frequency domain form. By converting the phase information in the corneal motion electrical signal into the motion information of the cornea, the Young's modulus of the cornea is obtained.

[0071] More specifically, the detection device reconstructs the mechanical wave propagation image of the cornea in the synchronous excitation state through multiple frames of static corneal images; through a corneal wave velocity-modulus reconstruction model, based on the wave velocity information in the corneal mechanical wave propagation image and combined with the biophysical characteristic factors of the cornea, the modulus information of the cornea is calculated; based on the output of the corneal wave velocity-modulus reconstruction model, through a three-dimensional reconstruction algorithm, the modulus information is visualized in space to generate a three-dimensional corneal elastomechanics image.

[0072] The specific data processing flow of the corneal wave velocity-modulus reconstruction model is as Figure 2As shown. The electrical signal information of corneal movement is obtained by means of the data acquisition card, which is the original image. After that, the following steps are required.

[0073] Denoising: The original image will have a large amount of high-frequency noise. Therefore, it is necessary to denoise the obtained electrical signal. The specific operation is as follows: The signal information at 10 moments is averaged to obtain the true information of the detection point in the previous time period. That is:

[0074]

[0075] Artifact removal: In addition to the original high-frequency noise brought by the system, the processed image also has low-frequency noise caused by factors such as heartbeat, blood vessels, blinking, and unconscious head movement during the detection process. After detection, the frequency domain of this noise is <100Hz. Different from the excitation frequency of 1200Hz. High-pass filtering is adopted to remove the low-frequency noise. The denoised signal obtained by acquisition is Fourier-transformed to obtain the complex-valued signal F(z). That is:

[0076]

[0077] Then, with the help of high-pass filtering, the low-frequency signal below 100Hz is removed. The specific formula is:

[0078]

[0079] Where is the cut-off frequency, R is the set resistance value, and C is the set capacitance value.

[0080] Speckle tracking: The principle of speckle tracking is based on the laser speckle phenomenon, and the movement or deformation of an object is tracked and measured by analyzing the change of the speckle pattern. When a laser irradiates the surface of an object, due to the microscopic rough structure of the object surface, the reflected light will interfere with each other to form a speckle pattern. When the object moves or deforms, the speckle pattern will change accordingly. By capturing and analyzing these changes, the movement state or internal stress, displacement, etc. information of the object can be inferred.

[0081] Phase information: The phase information of the signal can be obtained by performing autocorrelation analysis on the complex-valued signal. That is

[0082]

[0083] Displacement information: According to 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 waves generated by excitation mainly propagate in the form of Lamb waves and in an asymmetric form of A0-mode. Based on this, with the help of the following formula, the displacement information of the cornea can be obtained.

[0084]

[0085] Wave velocity information: Fourier transform the displacement information to obtain the wave number information, which is the basic parameter for wave velocity calculation. Then, solve the phase velocity, i.e., the wave velocity, according to the following formula.

[0086]

[0087] Modulus image: With the obtained two-dimensional wave velocity image, considering the factors affecting the wave velocity, intraocular pressure value IOP, degree of hydration , corneal thickness CCT, corneal curvature , obtain the specific modulus of the cornea with the help of a neural network, and finally assign it to the original phase velocity image to obtain the elastic image of the cornea. Preferably, the designed network structure is as Figure 3 shown. Figure 3 The wave velocity-modulus network shown includes three main components: a convolutional neural network (CNN), a long short-term memory network (LSTM), and a multi-layer perceptron (MLP). First, the 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. Then, the LSTM processes the time series data, further extracts the temporal features, and converts them into a one-dimensional vector through a flattening operation. Next, the output feature vectors of the CNN and the LSTM are fused to form a comprehensive feature representation. Finally, the MLP receives the fused feature vector, and through multiple non-linear transformations, finally outputs the modulus information of the cornea. The entire network structure is optimized through a loss function to achieve accurate prediction of the corneal modulus.

[0088] The present invention not only improves the detection accuracy and comfort, but also reduces the operation difficulty and improves the convenience of use by introducing a mechanical structure, providing an efficient and reliable solution for corneal mechanics detection. The corneal mechanics measuring instrument of the present invention constructs an accurate and novel detection mechanism by introducing key components such as a light source, an optical fiber coupler, a balanced detector, a high-pass filter, and 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. At the same time, different from the contact pressing detection, with the help of the optical detection module, non-contact accurate measurement can be realized. The robotic arm group in the auxiliary device, combined with the positioning algorithm, realizes the accurate positioning of the excitation microliter injection 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 injection needle, etc. Through the precise control of the air circuit, the excitation pressure and excitation time can be precisely controlled, thus ensuring the accuracy, personalization, and safety of the detection. Overall, the present invention not only improves the detection accuracy and comfort, but also improves the detection accuracy and meets the personalized needs by introducing a mechanical structure, improving the convenience of use, and providing an efficient and reliable solution for corneal mechanics detection.

[0089] Compared with the existing solutions, the corneal mechanical measuring instrument of the present invention takes into account the influences of corneal thickness, intraocular pressure, corneal curvature, etc. on the measurement results. Combining the data accumulated from the previous finite element simulations, phantom experiments, ex vivo experiments, etc., an algorithm considering factors such as corneal thickness, intraocular pressure, corneal curvature, etc. is proposed to make the results more accurate and reliable.

[0090] The multifunctional corneal detection device of the present invention realizes efficient and accurate corneal mechanical measurement through optimized mechanical design and integrated diagnostic functions, providing great convenience for clinical operations.

[0091] During the detection, the person to be detected places their head at the designated position and clicks "Start Positioning". The detection device uses the positioning algorithm to find the position of the cornea. Subsequently, the device moves to align the lens with the cornea to prepare for shooting. Then, a prompt will be given to complete the positioning. Then, set the jet mode according to the detection requirements, or directly select the default mode. After selection, click "Start Detection". The excitation device generates a jet of air, and the detection device collects electrical signals for detection. Finally, according to the internal algorithm, the corneal image is reconstructed, the corneal mechanical characteristic parameters are corrected, and the corneal elastogram of the detected person is obtained, facilitating the doctor's judgment.

[0092] The embodiment of the present invention also provides a storage medium for storing a computer program, which when executed, at least executes the method described above.

[0093] The embodiment of the present invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein, the processor is used to execute the computer program to at least execute the method described above.

[0094] The embodiment of the present invention also provides a processor, which executes a computer program to at least execute the method described above.

[0095] 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 ferromagnetic random access memory (FRAM), a 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 memories.

[0096] In 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 merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0097] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0099] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the aforementioned storage medium includes: various media such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0100] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present invention, in essence or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing 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 the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0101] The methods disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0102] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0103] 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.

[0104] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those skilled in the technical field to which the present invention belongs, without departing from the concept of the present invention, several equivalent substitutions or obvious variations can be made, and as long as the performance or use is the same, they should all be regarded as falling within the protection scope of the present invention.

Claims

1. A device for real-time non-contact detection of corneal mechanics, characterized in that, Comprising: An excitation device, including an air pump, a pressure regulating valve, a high-frequency solenoid valve, and a microliter spray needle, which are connected in sequence through an air pipe, and are used to precisely control the pressure, frequency, and time of the air flow. The air flow ejected by the microliter spray needle acts on the cornea to form elastic waves inside and on the surface of the cornea; A detection device, which is used to detect the propagation wave speed of the elastic wave and reconstruct the mechanical property image of the cornea; the detection device includes 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 through 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 subjected to differential processing, and then the high-frequency noise is filtered by the high-pass filter. Finally, the data acquisition card collects the signal and reconstructs the cornea image; An auxiliary device, which is used to control the position and angle of the microliter spray needle of the excitation device; the auxiliary device includes a three-dimensional robotic arm group, which has six degrees of freedom and is used to precisely adjust the excitation site according to the cornea position; The detection device reconstructs the cornea mechanical wave propagation image in the synchronous excitation state through multiple frames of static cornea images; through the cornea wave speed-modulus reconstruction model, according to the wave speed information in the cornea mechanical wave propagation image, combined with the biophysical characteristic factors of the cornea, the modulus information of the cornea is calculated; based on the output of the cornea wave speed-modulus reconstruction model, the modulus information is visualized spatially through a three-dimensional reconstruction algorithm to generate a three-dimensional cornea elastic mechanics image; The detection device realizes the cornea wave speed-modulus reconstruction through the following processing process by constructing a cornea wave speed-modulus reconstruction model: Denoising processing: The electrical signals of the cornea movement collected are averaged, and the signal information at multiple moments is averaged to remove high-frequency noise and obtain the real information of the detection point in the previous time period; Artifact removal processing: The denoised signal is subjected to Fourier transform to obtain a complex-valued signal, and then the low-frequency noise below a specific cut-off frequency is removed through high-pass filtering. 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 change of the speckle pattern, so as to obtain the movement-related information of the cornea; Phase information extraction: The complex-valued signal is subjected to autocorrelation analysis to obtain the phase information of the signal; Displacement information calculation: According to 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 speed information acquisition: The displacement information is subjected to Fourier transform to obtain the wave number information, and then the phase velocity is solved; Modulus image reconstruction: The obtained wave speed information is combined with the biophysical characteristic factors of the cornea, and the specific modulus of the cornea is calculated by means of a neural network and assigned to the original phase velocity image to generate the elastic image of the cornea; the biophysical characteristic factors of the cornea include cornea thickness, cornea curvature, internal pressure, and hydration degree.

2. The device for real-time non-contact detection of corneal mechanics according to claim 1, wherein, The microliter spray needle is pulled into a needle shape with a tip by a needle puller, and the tip size is 50-100 microns.

3. The device for real-time non-contact detection of corneal mechanics according to claim 1, wherein 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 the 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 an 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 a frequency domain form. By converting the phase information in the corneal movement electrical signal into the movement information of the cornea, the Young's modulus of the cornea is calculated.

6. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that, The robotic arm group in the auxiliary device includes six servo motors, enabling the microliter nozzle needle to have six degrees of freedom and being able to accurately regulate 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 balance 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 photodetector.

8. The device for real-time non-contact detection of corneal mechanics according to claim 1, characterized in that, The neural network includes a convolutional neural network CNN, a long short-term memory network LSTM, and a multi-layer perceptron MLP. The 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. The LSTM is used to process time series data, further extract temporal features, and convert them into a one-dimensional vector through a flattening operation; the output feature vectors of the CNN and the LSTM are fused to form a comprehensive feature representation; the MLP receives the fused feature vector and finally outputs the modulus information of the cornea through multiple non-linear transformations; the network structure is optimized through a loss function to achieve accurate prediction of the corneal modulus.

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