System for assessing myopia risk and intervention effect based on ciliary muscle biomechanical characteristics

Abstract

The application provides a myopia risk and intervention effect evaluation system based on ciliary muscle biomechanical characteristics, and relates to the technical field of ophthalmic examination. The method comprises the following steps: acquiring data of the eye of a target object, wherein the data at least includes ciliary muscle morphology data, ciliary muscle mechanical parameters and accommodation function parameters; constructing an individualized ciliary muscle biomechanical model of the target object based on the ciliary muscle morphology data and the ciliary muscle mechanical parameters; evaluating the individual accommodation function reserve state of the target object based on the individualized ciliary muscle biomechanical model and the accommodation function parameters; and predicting the myopia occurrence risk based on the individual accommodation function reserve state. By constructing the individualized ciliary muscle biomechanical model, the application evaluates the accommodation function reserve, predicts the myopia occurrence risk, simulates the influence of optical intervention on the ciliary muscle mechanical state, and realizes early identification of the myopia occurrence risk and early prediction of the intervention effect.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of ophthalmic examination technology, and in particular to a myopia risk and intervention effect assessment system based on the biomechanical characteristics of the ciliary muscle. Background Technology

[0002] Myopia is a major public health challenge facing the world. Current optical interventions for myopia control mainly include defocus lenses (such as microlens array lenses and peripheral defocus lenses) and orthokeratology lenses (OK lenses). Essentially, these various optical interventions work by altering accommodative demands, thereby regulating the mechanical responses of the ciliary muscle, such as its contractile force, contraction speed, and fatigue level, to slow the progression of myopia.

[0003] At the current clinical and research level, the evaluation of myopia intervention effectiveness still relies on refractive endpoint indicators, axial growth rate, and retinal defocus signal as the core basis. A unified and standardized optical intervention-myopia control coupled evaluation system has not yet been formed. The evaluation dimensions, technical means, and evaluation standards have obvious limitations, making it difficult to accurately characterize the intervention mechanism and individual differences in efficacy. Summary of the Invention

[0004] This invention provides a myopia risk and intervention effect assessment system based on the biomechanical characteristics of the ciliary muscle. It aims to solve the technical problems of existing technologies, such as the lack of early risk identification tools, long assessment cycles, and inability to select individualized intervention programs. By constructing an individualized ciliary muscle biomechanical model, it assesses accommodative function reserve, predicts the risk of myopia, and simulates the impact of optical intervention on the mechanical state of the ciliary muscle, thereby achieving early identification of myopia risk and early prediction of intervention effects.

[0005] The first aspect of this invention provides a method for assessing myopia risk and intervention effectiveness based on the biomechanical characteristics of the ciliary muscle, the method comprising: Acquire data about the eye of the target object, including at least ciliary muscle morphological data, ciliary muscle mechanical parameters, and accommodation function parameters; Based on the ciliary muscle morphological data and ciliary muscle mechanical parameters, an individualized ciliary muscle biomechanical model of the target object is constructed. Based on the individualized ciliary muscle biomechanical model and regulatory function parameters, the individual regulatory function reserve status of the target subject was evaluated. Based on the individual's accommodative reserve status, the risk of developing myopia is predicted.

[0006] A second aspect of this invention provides a myopia risk and intervention effect assessment system based on the biomechanical characteristics of the ciliary muscle, the system comprising: The data acquisition module is used to acquire data of the target object's eye, and the data includes at least ciliary muscle morphological data, ciliary muscle mechanical parameters, and accommodation function parameters; The model building module is used to construct an individualized ciliary muscle biomechanical model of the target object based on the ciliary muscle morphological data and ciliary muscle mechanical parameters. The assessment module is used to assess the individual regulatory function reserve status of the target object based on the individualized ciliary muscle biomechanical model and regulatory function parameters. The prediction module is used to predict the risk of myopia based on the individual's accommodation function reserve status.

[0007] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes a method for assessing myopia risk and intervention effects based on the biomechanical characteristics of the ciliary muscle as described in the first aspect of the present invention.

[0008] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for assessing myopia risk and intervention effects based on the biomechanical characteristics of the ciliary muscle as described in the first aspect of the present invention.

[0009] The fifth aspect of this invention provides a computer program product, including a computer program / instructions, which is implemented by a processor as the steps in the method for assessing myopia risk and intervention effect based on the biomechanical characteristics of ciliary muscle as described in the first aspect of this invention.

[0010] Compared with the prior art, the present invention has the following beneficial effects: (1) Covers the entire population: It can be applied to both non-myopic people (to predict the risk of myopia) and myopic people (to assess the effectiveness of intervention), with a wide range of applications.

[0011] (2) Shifting the assessment window forward: For those without myopia, high-risk individuals with insufficient accommodative reserve can be identified early; for those with myopia, the intervention effect can be predicted within a few weeks before or after wearing the device.

[0012] (3) Mechanism-driven and highly interpretable: For the first time, a quantitative assessment link of "regulatory demand → ciliary muscle contraction force → fatigue accumulation" was established, revealing the mechanical mechanism of myopia occurrence and optical intervention.

[0013] (4) High degree of individualization: The model is constructed based on the individual's measured ciliary muscle morphology, mechanical parameters and regulatory function to achieve accurate assessment of "one person, one model".

[0014] (5) Covers multiple intervention methods: It can uniformly evaluate various optical intervention methods such as defocus glasses, orthokeratology lenses, and multifocal soft contact lenses.

[0015] (6) Guidance scheme optimization: It can simulate the mechanical effects of different wearing schemes and output individualized optimal scheme recommendations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the light stepping steps in the myopia risk and intervention effect assessment method based on the biomechanical characteristics of ciliary muscle provided in the embodiments of the present invention. Figure 2 This is a schematic diagram of the ciliary muscle morphology and mechanical parameter input in the myopia risk and intervention effect assessment method based on the biomechanical characteristics of the ciliary muscle provided in the embodiments of the present invention, wherein (a) is an anterior segment OCT image showing the position of the ciliary muscle, and (b) is a schematic diagram of the ciliary muscle mechanical parameters. Figure 3 This is a schematic diagram of the Hill active contraction model in the myopia risk and intervention effect assessment method based on the biomechanical characteristics of the ciliary muscle provided in this embodiment of the invention, showing the force-velocity relationship curve; Figure 4 This is a flowchart of the myopia risk assessment method based on the biomechanical characteristics of ciliary muscle in the embodiments of the present invention. Figure 5 This is a flowchart of the simulation of the impact of optical intervention in the myopia risk and intervention effect assessment method based on the biomechanical characteristics of ciliary muscle provided in the embodiments of the present invention, showing the transmission link of "accommodative demand → ciliary muscle contraction force → fatigue accumulation"; Figure 6 This is a schematic diagram of the prediction result output interface in the myopia risk and intervention effect assessment method based on ciliary muscle biomechanical characteristics provided in the embodiments of the present invention, wherein (a) is the myopia risk assessment interface and (b) is the intervention effect assessment and program recommendation interface. Figure 7 This is a schematic diagram of the overall process of the myopia risk and intervention effect assessment method based on the biomechanical characteristics of the ciliary muscle provided in the embodiments of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Studies have shown that the occurrence and progression of myopia are closely related to the biomechanical state of the ciliary muscle, the executive organ of the eye's accommodation function. Currently, the evaluation of the effectiveness of myopia optical intervention still focuses on refractive and axial length endpoint indicators, supplemented by basic visual function examinations. These indicators play an important role in clinical screening and efficacy verification, but there are significant shortcomings in mechanism explanation, accurate assessment, individual optimization, and early prediction.

[0020] The inventors discovered through exploration and analysis that current evaluation methods mainly suffer from the following technical defects: (1) Lack of early risk identification tools: For children who have not yet developed myopia, there is a lack of tools for predicting the risk of myopia based on accommodative reserve. The ciliary muscle is the executive organ of accommodative function, and its mechanical state is closely related to accommodative load tolerance, but current technology does not include it in risk assessment.

[0021] (2) Long evaluation period: For those who are already nearsighted, it takes 6-12 months of follow-up to judge the intervention effect, making it impossible to adjust the plan in the early stage. If the intervention is ineffective, the best control window will be missed.

[0022] (3) Lack of mechanistic indicators: The impact of interventions on accommodative function cannot be quantified, making it difficult to explain individual differences in efficacy. Defocus glasses indirectly affect ciliary muscle contraction by altering accommodative demand, but existing technologies have not established quantitative assessment methods.

[0023] (4) Ignoring the biomechanical state of the ciliary muscle: Studies have shown that the occurrence and progression of myopia are closely related to the biomechanical state of the ciliary muscle, the executive organ of the eye's accommodation function. The contractile mechanical properties of the ciliary muscle (contraction force, contraction speed, and degree of fatigue) are closely related to the accommodative load. Long-term excessive accommodative load can lead to ciliary muscle fatigue and decreased accommodative function, thereby affecting the occurrence and progression of myopia.

[0024] (5) Lack of tools for selecting individualized intervention programs: Different optical intervention methods (different defocusing amounts, different lens designs) have different suitability for different individuals, and existing technologies cannot predict which program is optimal for a specific patient before wearing the device.

[0025] To meet the needs of precise and systematic development in myopia prevention and control, this invention proposes to establish a multi-dimensional, full-chain evaluation system that integrates in vivo biomechanical testing of the ciliary muscle, monitoring of scleral mechanical response, and verification of refractive and axial length endpoints. Biomechanical parameters such as ciliary muscle contractility, accommodative mechanical stability, and fatigue recovery rate are used as early predictive indicators and mechanism evaluation indicators for intervention effects. This invention proposes a method and system that can quantify the mechanical state of the ciliary muscle, predict the risk of myopia, evaluate the effects of optical interventions, and guide the selection of individualized treatment plans.

[0026] Specifically, such as Figure 1 As shown, the method for assessing myopia risk and intervention effectiveness based on the biomechanical characteristics of the ciliary muscle provided in this embodiment of the invention includes the following steps: S101, acquire data of the target object's eye, the data including at least ciliary muscle morphology data, ciliary muscle mechanical parameters and accommodation function parameters.

[0027] In this embodiment of the invention, the ciliary muscle morphological data includes at least the ciliary muscle thickness, cross-sectional area, and direction of the circular fibers. This data can be obtained through anterior segment optical coherence tomography or ultrasound biomicroscopy.

[0028] In this embodiment of the invention, the ciliary muscle mechanical parameters include: principal kinetic parameters and passive kinetic parameters, wherein the passive kinetic parameters include at least the passive elastic modulus, and the principal kinetic parameters include at least the maximum isometric contractile force, contraction velocity constant, and fatigue index. These parameters can be obtained through optical coherence elastography, ultrasonic elastography, accommodation response measurement, or in vitro measurement.

[0029] In this embodiment of the invention, the adjustable function parameters include at least the following four indicators: (1) Amplitude of accommodation: The maximum accommodative force that the human eye can produce, that is, the difference in refractive power between when the eye can see the far point clearly (accommodation is completely relaxed) and when it can see the near point clearly (accommodation is at its maximum contraction); (2) Accommodation lag: During near fixation, the actual amount of accommodative response is less than the theoretically required amount of accommodative stimulus; (3) Positive relative accommodation: Under the condition of fixed convergence (when both eyes are looking at near objects), gradually increase the negative lens stimulation accommodation until the patient can no longer see the target at the maximum accommodation amount; (4) Negative relative accommodation: Under the condition of fixed convergence, relax accommodation by increasing positive lenses until the maximum relaxation amount of accommodation is reached when the target is blurred.

[0030] In this embodiment of the invention, the accommodation function parameters of the target object can be obtained by using a comprehensive optometry instrument or a vision function testing device.

[0031] S102, Based on the ciliary muscle morphological data and ciliary muscle mechanical parameters, construct an individualized ciliary muscle biomechanical model for the target object.

[0032] In this embodiment of the invention, the model types include, but are not limited to: active contraction mechanics model, finite element model, boundary element model, or reduced-order mechanics model.

[0033] Specifically, such as Figure 2 As shown, it illustrates the source of input data and parameterization method required for modeling in this invention. Among them, (a) the location of the ciliary muscle is indicated by the localization method of anterior segment OCT image, and morphological indicators such as ciliary muscle thickness and cross-sectional area are marked; (b) it shows that morphological input and mechanical input are used together for individualized parameter calibration and form a ciliary muscle biomechanical model.

[0034] Specifically, in this embodiment of the invention, the Hill active contraction model is used to describe the active contraction behavior of the ciliary muscle, and the control equation is as follows: ; in, F ciliary The ciliary muscle contractile force (unit: N) is calculated from the model. F max and v The values ​​are the individual's maximum isometric contractile force (unit: N) and contraction velocity (unit: mm / s), both obtained based on the morphological and mechanical parameters of the ciliary muscle. a , b These are constants related to muscle fiber properties.

[0035] Specifically, Figure 3 The ciliary muscle force-velocity relationship in Hill's active contraction model is shown. The horizontal axis represents contraction velocity, and the vertical axis represents ciliary muscle contractile force; the model utilizes the maximum isometric contractile force. F max and muscle fiber characteristic constants a , b This describes the active contraction capacity of the ciliary muscle at different contraction speeds, which is used for subsequent simulation of load and fatigue accumulation.

[0036] Specifically, the Hill active contraction model can be used in situations where... F max , a , b Under the premise of equal parameters, simulate the contractile force of the ciliary muscle at different contraction velocities. F ciliary .

[0037] Specifically, the model parameters are calibrated based on the following data: The maximum isometric contractile force of the ciliary muscle is directly proportional to its physiological cross-sectional area, therefore: ; in, A ciliary Physiological cross-sectional area of ​​the ciliary muscle (unit: mm) 2 ); k The proportionality coefficient is determined based on in vitro measurement data from literature or reference values ​​for normal populations.

[0038] Constants related to muscle fiber properties are obtained by fitting regulatory response measurement data. Specifically, actual regulatory response quantities under different levels of regulatory stimulation can be collected, and the least squares method can be used to fit the force-velocity relationship based on the Hill model. a、b Values. When individual data is insufficient, normal reference values ​​for the same age group can be used.

[0039] S103, Based on the individualized ciliary muscle biomechanical model and regulatory function parameters, assess the individual regulatory function reserve status of the target subject.

[0040] In this embodiment of the invention, the individual's regulatory function reserve status can be assessed through adjustment amplitude assessment, adjustment lag assessment, and fatigue risk index.

[0041] In this embodiment of the invention, step S103 includes: S1031, compare the adjustment range with the normal reference value for the same age to assess the adjustment range; S1032, compare the adjustment lag with the normal reference value for the same age to assess the adjustment lag; S1033, calculate the fatigue risk index, the calculation formula is: ; in, Adjust the load to the baseline. The maximum isometric contractile force is normal for its age. The maximum isometric contraction force of the target object is obtained based on the model parameters calibrated in step S102.

[0042] Specifically, the amplitude of accommodation can be compared with the normal reference value for the same age group; a lower amplitude of accommodation suggests insufficient ciliary muscle contraction ability. Similarly, the hysteresis can be compared with the normal reference value for the same age group; a higher hysteresis suggests low hysteretic efficiency and easy fatigue.

[0043] In this embodiment of the invention, the fatigue risk index can be calculated based on the maximum isometric contraction force and accommodative load. The baseline accommodative load refers to the inherent accommodative load value of the human eye under normal daily eye use conditions, before intervention and without wearing protective lenses. It is determined based on the baseline accommodative amplitude and baseline accommodative requirement, where the baseline accommodative requirement is the amount of accommodation required for daily eye use, and the baseline accommodative amplitude refers to the maximum accommodative capacity of the target subject's eye before intervention.

[0044] S104, based on the individual's accommodative reserve status, predict the risk of myopia.

[0045] Specifically, Figure 4 The calculation process for assessing the risk of myopia is illustrated. In this embodiment of the invention, risk factors such as accommodative amplitude, accommodative hysteresis, ciliary muscle cross-sectional area, and fatigue risk index are quantified and input into a risk prediction model to obtain the probability of myopia development. Based on preset thresholds, high-risk, medium-risk, or low-risk levels and corresponding follow-up recommendations are output.

[0046] In this embodiment of the invention, the following risk factors can be integrated to assess the risk of developing myopia: The adjustment range is too low (OR = 2.5). The adjustment lag is too high (OR = 3.0) The fatigue risk index is high (OR = 4.0). The cross-sectional area of ​​the ciliary muscle is relatively small (OR = 2.0). Here, OR refers to the ratio of the abnormal population value to the normal population value.

[0047] In one specific embodiment, step S104 includes: integrating accommodative amplitude, accommodative hysteresis, fatigue risk index, and ciliary muscle cross-sectional area as risk factors, and using a logistic regression model to calculate the probability of myopia occurrence, as follows:

[0048] Among them, X i This is the quantified value of the i-th risk factor, which includes, but is not limited to, indicators such as: adjustment amplitude, adjustment lag, maximum isometric contractile force, and fatigue risk index. β i The i-th regression coefficient is obtained by training with a large sample of historical follow-up dataset.

[0049] In this embodiment of the invention, after obtaining the probability of myopia development, a myopia risk level can be further output. Specifically, the myopia risk level can be divided according to the following criteria: High risk: Probability of developing myopia > 60% Medium risk: 20%-60% probability of developing myopia. Low risk: Probability of developing myopia <20% In one specific embodiment, when the target object is a nearsighted object, the acquired eye data of the target object further includes: optical intervention parameters.

[0050] Specifically, the optical intervention parameters include at least: Intervention type: one or more of the following: defocus glasses, orthokeratology lenses, and multifocal soft contact lenses; Defocus amount (D): Positive or negative defocus value; Lens design parameters: peripheral defocus distribution, optical zone diameter; Wearing plan: wearing time and wearing frequency.

[0051] The acquisition method of the above data is not limited, and can be one or more of the following: optical coherence tomography, ultrasound biomicroscopy, optical coherence elastography, ultrasound elastography, visual function examination, or literature reference values.

[0052] In this embodiment of the invention, when the target object is a nearsighted individual, the method further includes the following steps: S105, based on optical intervention parameters and adjustment function parameters, calculates the adjustment load after optical intervention.

[0053] Specifically, for defocused glasses, the formula for calculating accommodation requirements is: ; in, d The viewing distance (unit: m) is typically 0.33 m. L lens This represents the lens defocus amount (unit: diopter), with a positive value indicating positive defocus.

[0054] In this embodiment of the invention, the regulating load is defined as: ; in, A amp As for the individual accommodation amplitude, in clinical practice, this parameter is usually measured and obtained using standardized subjective and objective visual function examination methods such as the near-movement method, the far-movement method, or the negative lens method through a comprehensive optometry instrument or visual function examination equipment.

[0055] For orthokeratology lenses, first calculate the equivalent defocus: ; in, K pre and K post The figures show the corneal curvature before and after wearing contact lenses (unit: diopters).

[0056] The formula for calculating adjustment demand is: ; The formula for calculating the load adjustment is the same as above.

[0057] S106, based on load regulation, simulates the mechanical response of the ciliary muscle after optical intervention using a ciliary muscle biomechanical model.

[0058] In this embodiment of the invention, the mechanical response of the ciliary muscle after optical intervention can be simulated by calculating the steady-state contractile force and the ciliary muscle fatigue accumulation index.

[0059] S107, based on the results of mechanical response simulation, outputs one or more of the following intervention effect evaluation indicators: adjustment load reduction index, fatigue risk level, individual fit score, and predicted axial length control effect.

[0060] Specifically, step S106 includes the following sub-steps: S1061, based on the Hill active contraction model, determines the maximum isometric contraction force of an individual and calculates the steady-state contraction force corresponding to the regulating load; S1062, calculates the cumulative fatigue index of the ciliary muscle based on the ciliary muscle contraction force and contraction time.

[0061] Specifically, based on the maximum isometric contraction force calibrated in step S102 F max By combining muscle activation-contraction dynamics, the contraction velocity of the ciliary muscle while maintaining a fixed focal length was calculated. v Steady-state contractile force (= 0) F steady The accommodative load on the eyes. L load The contraction signal of the ciliary muscle is considered as follows: the greater the regulatory load, the greater the contractile force emitted by the muscle. Therefore, the steady-state contractile force equation is as follows:

[0062] in, λ The characteristic constant is determined by adjusting the reaction data fitting or by referring to literature values.

[0063] Specifically, the cumulative fatigue index of the ciliary muscle can be calculated based on contractile force and contraction time. I fatigue The ciliary muscle fatigue accumulation index refers to the energy expenditure accumulated by the ciliary muscle in maintaining its contracted state during sustained accommodation and fixation. It is used to quantify the degree of physiological muscle fatigue caused by prolonged high-intensity accommodative load. The specific calculation formula is as follows:

[0064] in, Fciliary ( t (This refers to the ciliary muscle) t The contractile force at any moment; F max This represents the maximum isometric contraction force. v ( t )for t The rate of contraction at any given moment; v max This represents the maximum contraction speed; T This is the upper limit of the contraction time.

[0065] In this embodiment of the invention, the intervention effect is evaluated based on the adjustment load reduction index, fatigue risk level, individual fitness score, and predicted axial length control effect.

[0066] Specifically, step S107 includes the following sub-steps: S1071, Calculate the regulation load reduction index based on the regulation load before optical intervention and the regulation load before optical intervention; Specifically, the formula for calculating the load reduction index is as follows: ; in, To adjust the load, the index is reduced. This represents the accommodative load following optical intervention. This formula is used to quantify the degree to which optical intervention reduces the accommodative pressure on the ciliary muscle.

[0067] S1072, Determine the fatigue risk level based on the ciliary muscle fatigue accumulation index; Specifically, it can be based on the calculated fatigue accumulation index I. fatigue Classify into levels: (1) Low risk: I fatigue <0.3; (2) Medium risk: 0.3 fatigue <0.6; (3) High risk: I fatigue >0.6.

[0068] S1073, Determine the individual fitness score based on the steady-state contractile force, the individual maximum isometric contractile force and the ciliary muscle fatigue accumulation index; Specifically, the formula for calculating individual fit score is as follows: ; Here, α and β are preset weighting coefficients. This formula uses a weighted subtraction system to comprehensively evaluate the suitability of a specific intervention for an individual.

[0069] S1074, based on the adjustment load reduction index and the effective threshold of the adjustment load reduction index, determines the predicted axial length control effect.​

[0070] Specifically, the calculation formula for the effect of measuring axial length control is as follows:

[0071] in, γ This is the proportionality coefficient; R threshold To adjust the effective threshold for the load reduction index, it was determined through statistical analysis of large-sample historical clinical follow-up data (e.g., ROC curve analysis or multivariate regression model); (symbol) + This is a positive part operator; when the value within the parentheses is greater than 0, it takes its original value; when it is less than or equal to 0, it takes the value 0. This formula is used to quantify the effect of the degree of load reduction on the axial length control effect.

[0072] In one specific implementation, the ciliary muscle mechanical response can be simulated based on parameters of multiple candidate intervention programs to output an optimal program recommendation. Specifically, the method further includes the following steps: S108, acquire multiple sets of candidate optical intervention parameters; S109, based on the candidate optical intervention parameters of each group, output the intervention effect evaluation index respectively; S110, based on the intervention effect evaluation index corresponding to each group of candidate optical intervention parameters, the optimal candidate optical intervention parameters are obtained.

[0073] Figure 5 This paper demonstrates the simulation process of optical interventions such as defocus glasses, orthokeratology lenses, or multifocal soft contact lenses. The system calculates the accommodative load based on optical intervention parameters and accommodative function parameters, then simulates the steady-state contractile force and fatigue accumulation index using a ciliary muscle biomechanical model. Finally, it outputs the accommodative load reduction index, fatigue risk level, individual fit score, and predicted axial length control effect.

[0074] Specifically, in this embodiment of the invention, candidate optical intervention parameters are input, including defocus amount (such as the design parameters of orthokeratology lenses and defocused lenses), lens design scheme, wearing method, etc.

[0075] Based on the user's viewing distance and the defocus amount of the lens, the actual amount of accommodation required by the eye is calculated. One of the core principles of defocus lenses is to reduce the accommodation demand when viewing near objects through optical design.

[0076] Based on the calculated accommodative demand, the actual accommodative load of the eye under different intervention schemes was further obtained. A model was used to simulate the mechanical performance of the ciliary muscle under this accommodative load, and the steady-state contractile force F was calculated. steady .

[0077] The fatigue cumulative index was calculated based on the mechanical response of the ciliary muscle._fatigue .

[0078] After obtaining core data such as the fatigue index, multiple evaluation indicators are output: adjustment load reduction index, fatigue risk level, individual suitability score, and predicted axial length control effect. Based on multiple evaluation indicators, candidate intervention programs are compared, and finally, an individualized optimal program recommendation is output.

[0079] For example, Figure 6 The interface for the output results of this invention is shown. (a) displays the probability of myopia onset, risk level, and key risk indicators for non-myopic subjects; (b) displays the intervention effect evaluation results, comparison results of different candidate programs, and individualized recommended programs for myopic subjects. This interface is a functional layout illustration and does not limit the specific style of the actual software interface.

[0080] Figure 7 The diagram illustrates the overall main process of the method of the present invention. Specifically, firstly, ciliary muscle morphological data, ciliary muscle mechanical parameters, and accommodative function parameters are acquired. Then, an individualized ciliary muscle biomechanical model is constructed to assess the accommodative function reserve status and predict the risk of myopia. For individuals who are already myopic, optical intervention parameters can be further input to calculate the accommodative load after intervention, simulate the ciliary muscle mechanical response, output intervention effect evaluation indicators, and optimize candidate solutions.

[0081] In this invention, by acquiring ciliary muscle morphological data, mechanical parameters, and accommodative function parameters of the target subject, an individualized ciliary muscle Hill active contraction mechanical model is constructed. Based on this model, the individual's accommodative reserve status is assessed. This model can not only calculate the fatigue risk index and predict the probability of myopia development for non-myopic individuals, but also quantify the accommodative load of interventions such as defocus glasses or orthokeratology lenses for myopic individuals, simulating the steady-state contractile force and fatigue accumulation of the ciliary muscle after optical intervention, while simultaneously outputting evaluation indicators. This invention overcomes the shortcomings of traditional assessment methods, such as lack of mechanical mechanism support and long assessment cycles, realizing myopia prediction and evaluation of intervention effects. It also reveals the mechanical mechanism of myopia development, providing an effective biomechanical tool for myopia prevention and control.

[0082] The following specific embodiments further illustrate the method for predicting the occurrence and progression of myopia based on the biomechanical material properties of the sclera / cornea provided by the present invention: Example 1: Assessment of Accommodative Reserve and Prediction of Myopia Risk in Non-Myopic Children 1. Subjects A 9-year-old child who is not nearsighted, but spends a long time using their eyes at close range (more than 3 hours a day), and one of their parents is nearsighted.

[0083] 2. Data Acquisition (1) Ciliary muscle morphology data: Ciliary muscle images were obtained by optical coherence tomography of the anterior segment. The results showed that the ciliary muscle thickness was 1.65 mm (normal reference value 1.80 ± 0.12 mm) and the cross-sectional area was 10.2 mm² (normal reference value 11.5 ± 1.0 mm²).

[0084] (2) Ciliary muscle mechanical parameters: The passive elastic modulus of the ciliary muscle was measured to be 8.2 kPa using ultrasound elastography; the maximum isometric contractile force Fmax was calibrated to be 2.8 N (normal reference value 3.5 ± 0.4 N) by adjusting the response; the fatigue recovery constant was low.

[0085] (3) Adjustment function parameters: adjustment range 12.5 D (normal reference value 14.0 ± 1.0 D), adjustment hysteresis 1.25 D (normal reference value 0.75 ± 0.25 D).

[0086] 3. Assessment of regulatory capacity reserves; the assessment results are shown in Table 1: Table 1. Results of the assessment of regulatory function reserve

[0087] 4. Myopia Risk Prediction Calculating the risk probability of developing myopia based on a logistic regression model: ; The predicted probability of developing myopia within 2 years is >99%.

[0088] 5. Conclusion This subject had insufficient ciliary muscle functional reserve, with low accommodative amplitude, high accommodative lag, and low maximum isometric contraction force, indicating a high risk of fatigue. The predicted risk of developing myopia was high. Recommendations: Control near-vision time, rest by looking into the distance every 30 minutes, and have accommodative function and axial length checked every 6 months.

[0089] Example 2: Evaluation of the impact of defocused glasses on ciliary muscle mechanics 1. Obtaining Subject Data Subjects: 12-year-old myopic children with an equivalent spherical power of -3.00 D.

[0090] (1) Ciliary muscle morphology data: Ciliary muscle thickness 1.85 mm, cross-sectional area 12.3 mm².

[0091] (2) Ciliary muscle mechanical parameters: passive elastic modulus 8.5 kPa, maximum isometric contraction force Fmax=3.8N.

[0092] (3) Adjustment function parameters: adjustment range 14.5 D, adjustment lag 0.75 D.

[0093] (4) Optical intervention parameters: defocus glasses, defocus amount +3.00 D.

[0094] 2. Calculation of regulating load Assuming a viewing distance of 33 cm, for the unworn state: ; Regarding the state after wearing: ; 3. Simulation of ciliary muscle mechanical response; simulation results are shown in Table 2: Table 2 Simulation results of ciliary muscle mechanical response

[0095] 4. Evaluation Conclusion These defocus glasses significantly reduce the ciliary muscle contraction load (by 99%), lowering the risk of fatigue from medium to low. This treatment is predicted to have good myopia control effects and is recommended.

[0096] Based on the same inventive concept, embodiments of the present invention also provide a myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics, the system comprising: The data acquisition module is used to acquire data of the target object's eye, and the data includes at least ciliary muscle morphological data, ciliary muscle mechanical parameters, and accommodation function parameters; The model building module is used to construct an individualized ciliary muscle biomechanical model of the target object based on the ciliary muscle morphological data and ciliary muscle mechanical parameters. The assessment module is used to assess the individual regulatory function reserve status of the target object based on the individualized ciliary muscle biomechanical model and regulatory function parameters. The prediction module is used to predict the risk of myopia based on the individual's accommodation function reserve status.

[0097] Optionally, if the target object is a nearsighted individual, the acquired eye data of the target object further includes: optical intervention parameters; the system further includes: The calculation module is used to calculate the regulatory load after optical intervention based on optical intervention parameters and regulation function parameters; The simulation module is used to simulate the mechanical response of the ciliary muscle after optical intervention based on the adjusted load using a ciliary muscle biomechanical model. The output module is used to output one or more of the following intervention effect evaluation indicators based on the mechanical response simulation results: adjustment load reduction index, fatigue risk level, individual fit score, and predicted axial length control effect.

[0098] Optionally, the model building module is used for: The Hill active contraction model is used to describe the active contraction behavior of the ciliary muscle, and the governing equations are as follows:

[0099] in, F ciliary It is the contractile force of the ciliary muscle; F max The maximum isometric contractile force for an individual was determined based on the morphology and mechanical parameters of the ciliary muscle. v The contraction speed; a , b These are constants related to muscle fiber properties.

[0100] Optionally, the evaluation module is used for: The adjustment range was compared with the normal reference range for the same age group to assess the adjustment range; The adjustment lag is assessed by comparing it with the normal reference value for the same age group. The fatigue risk index is calculated using the following formula: ; in, Adjust the load to the baseline. The maximum isometric contractile force is normal for its age. The maximum isometric contraction force of the target object.

[0101] Optionally, the prediction module is used for: Integrating accommodative amplitude, accommodative hysteresis, fatigue risk index, and ciliary muscle cross-sectional area as risk factors, a logistic regression model is used to calculate the probability of developing myopia. The formula is as follows:

[0102] Among them, X i Let i be the quantified value of the i-th risk factor. β i The i-th regression coefficient.

[0103] Optionally, the simulation module is used for: Based on the Hill active contraction model, the maximum isometric contraction force of an individual was determined and the steady-state contraction force corresponding to the regulating load was calculated. The cumulative fatigue index of the ciliary muscle is calculated based on the ciliary muscle contractile force and contraction time. The output module is used for: Based on the conditioning load before optical intervention and the conditioning load before optical intervention, calculate the conditioning load reduction index; The fatigue risk level is determined based on the ciliary muscle fatigue accumulation index. Individual fitness scores are determined based on the steady-state contractile force, the individual's maximum isometric contractile force, and the ciliary muscle fatigue accumulation index. The predictive effect of axial length control is determined based on the regulation load reduction index and the effective threshold of the regulation load reduction index.

[0104] Optionally, the system further includes: The candidate parameter evaluation module is used to: acquire multiple sets of candidate optical intervention parameters; output intervention effect evaluation indicators based on each set of candidate optical intervention parameters; and obtain the optimal candidate optical intervention parameters based on the intervention effect evaluation indicators corresponding to each set of candidate optical intervention parameters.

[0105] The embodiment of the myopia risk and intervention effect assessment device based on the biomechanical characteristics of the ciliary muscle provided by this invention can be applied to any device with data processing capabilities, such as a computer. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution.

[0106] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps in the method for assessing myopia risk and intervention effect based on ciliary muscle biomechanical characteristics as described in any of the above embodiments.

[0107] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for assessing myopia risk and intervention effects based on the biomechanical characteristics of ciliary muscle described in any of the above embodiments.

[0108] Based on the same inventive concept, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in the method for assessing myopia risk and intervention effects based on the biomechanical characteristics of ciliary muscle described in any of the above embodiments.

[0109] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0110] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (apparatus), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable terminal device to cause a series of operational steps to be performed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0115] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0116] The foregoing has provided a detailed description of a myopia risk and intervention effect assessment system based on the biomechanical characteristics of the ciliary muscle. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A myopia risk and intervention effect assessment system based on the biomechanical properties of the ciliary muscle, characterized in that, The system includes: The data acquisition module is used to acquire data of the target object's eye, and the data includes at least ciliary muscle morphological data, ciliary muscle mechanical parameters, and accommodation function parameters; The model building module is used to construct an individualized ciliary muscle biomechanical model of the target object based on the ciliary muscle morphological data and ciliary muscle mechanical parameters. The assessment module is used to assess the individual regulatory function reserve status of the target object based on the individualized ciliary muscle biomechanical model and regulatory function parameters. The prediction module is used to predict the risk of myopia based on the individual's accommodative function reserve state. In the case where the target object is a nearsighted individual, the acquired eye data of the target object further includes: optical intervention parameters; the system further includes: The calculation module is used to calculate the regulatory load after optical intervention based on optical intervention parameters and regulation function parameters; The simulation module is used to simulate the mechanical response of the ciliary muscle after optical intervention based on the adjusted load using a ciliary muscle biomechanical model. The output module is used to output one or more of the following intervention effect evaluation indicators based on the mechanical response simulation results: adjustment load reduction index, fatigue risk level, individual fit score, and predicted axial length control effect.

2. The myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics according to claim 1, characterized in that, The model building module is used for: The Hill active contraction model is used to describe the active contraction behavior of the ciliary muscle, and the governing equations are as follows: in, F ciliary It is the contractile force of the ciliary muscle; F max The maximum isometric contractile force for an individual was determined based on the morphology and mechanical parameters of the ciliary muscle. v The contraction speed; a , b These are constants related to muscle fiber properties.

3. The myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics according to claim 2, characterized in that, The evaluation module is used for: The adjustment range was compared with the normal reference range for the same age group to assess the adjustment range; The adjustment lag is assessed by comparing it with the normal reference value for the same age group. The fatigue risk index is calculated using the following formula: ； in, Adjust the load to the baseline. The maximum isometric contractile force is normal for its age. The maximum isometric contraction force of the target object.

4. The myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics according to claim 3, characterized in that, The prediction module is used for: Integrating accommodative amplitude, accommodative hysteresis, fatigue risk index, and ciliary muscle cross-sectional area as risk factors, a logistic regression model is used to calculate the probability of developing myopia. The formula is as follows: Among them, X i Let i be the quantified value of the i-th risk factor. β i The i-th regression coefficient.

5. The myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics according to claim 1, characterized in that, The simulation module is used for: Based on the Hill active contraction model, the maximum isometric contraction force of an individual was determined and the steady-state contraction force corresponding to the regulating load was calculated. The cumulative fatigue index of the ciliary muscle is calculated based on the ciliary muscle contractile force and contraction time. The output module is used for: Based on the conditioning load before optical intervention and the conditioning load before optical intervention, calculate the conditioning load reduction index; The fatigue risk level is determined based on the ciliary muscle fatigue accumulation index. Individual fitness scores are determined based on the steady-state contractile force, the individual's maximum isometric contractile force, and the ciliary muscle fatigue accumulation index. The predictive effect of axial length control is determined based on the regulation load reduction index and the effective threshold of the regulation load reduction index.

6. The myopia risk and intervention effect assessment system based on ciliary muscle biomechanical characteristics according to claim 1, characterized in that, The system also includes: The candidate parameter evaluation module is used to: acquire multiple sets of candidate optical intervention parameters; output intervention effect evaluation indicators based on each set of candidate optical intervention parameters; and obtain the optimal candidate optical intervention parameters based on the intervention effect evaluation indicators corresponding to each set of candidate optical intervention parameters.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the myopia risk and intervention effect assessment system based on the biomechanical characteristics of ciliary muscle as described in any one of claims 1-6.

8. A readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the myopia risk and intervention effect assessment system based on the biomechanical properties of the ciliary muscle as described in any one of claims 1-6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the myopia risk and intervention effect assessment system based on the biomechanical properties of the ciliary muscle as described in any one of claims 1-6.

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