Myopic control progression modeling, tracking, and / or treatment

Abstract

Myopic control progression modeling, tracking, and / or treatment is disclosed. The myopic control progression tracking uses a synthesized myopic progression modeling employing an initial efficacy boost in myopic progression of the patient's eye. Methods and apparatuses for determining myopic control treatment efficacy in a myopic control treated patient using synthesized myopic progression modeling employing using an "untreated" predicted myopic control progression data for the respective patient group(s) but then altered with (1) application of observed initial boost in efficacy; (2) an expected consistent reduction per year in axial elongation subsequent; and (3) an accumulated multi-year (e.g. three year) reduction of axial elongation. In this manner, "treated" predicted myopic control progression models can be generated that can be used to determine whether the actual efficacy of the myopia control intervention treatment in the patient's eye is consistent with an accurate predicted efficacy of the myopia control intervention treatment.

Description

MYOPIC CONTROL PROGRESSION MODELING, TRACKING, AND / OR TREATMENTPRIORITY APPLICATION

[0001] The present application claims priority to U.S. Provisional Patent Application Serial No. 63 / 645,513, entitled MYOPIC CONTROL PROGRESSION MODELING, TRACKING, AND / OR TREATMENT, filed on May 10, 2024, the contents of which are hereby incorporated by reference in their entirety.FIELD OF THE DISCLOSURE

[0002] The present application relates to myopic control intervention treatment in a patient to arrest or reduce the progression of myopia in a patient. More specifically, the present application is directed to determining myopic control treatment efficacy in a myopic control-treated patient using synthesized myopic progression modeling employing an observed, predictable initial efficacy boost in myopic progression.BACKGROUND

[0003] Emmetropia describes the state of clear vision where an object at infinity is in relatively sharp focus without the need for optical correction and with the crystalline lens relaxed. In normal or emmetropic adult eyes, light from both distant and close objects passing through the central or paraxial region of the aperture or pupil is focused by the crystalline lens inside the eye close to the retinal plane where the inverted image is sensed. However, patients with myopia (i.e., nearsightedness) and hyperopia (i.e., farsightedness) have a reduced visual acuity for which corrective lenses in the form of spectacles or rigid or soft contact lenses are prescribed. These conditions are generally described as the imbalance between the length of the eye and the focus of the optical elements of the eye. Myopic eyes focus light in front of the retinal plane, and hyperopic eyes focus light behind the retinal plane. Myopia typically develops because the axial length of the eye grows to be longer than the focal length of the optical components of the eye, that is, the eye grows too long. Hyperopia typically develops because the axial length of the eye is too short compared with the focal length of the optical components of the eye. Patients with theseconditions can have their vision corrected with spherical contact lenses having the appropriate lens spherical power.

[0004] Myopia has a high prevalence rate in many regions of the world. Of greatest concern with this condition is its possible progression to high myopia, for example, greater than five (5) or six (6) diopters (D), which dramatically affects one’s ability to function without optical aids. High myopia is also associated with an increased risk of retinal disease, cataracts, glaucoma, and myopic macular degeneration (MMD; also known as myopic retinopathy) and may become a leading cause of permanent blindness worldwide. MMD has been related to refractive error (RE) to a degree rendering no clear distinction between pathological and physiological myopia and such that there is no “safe” level of myopia.

[0005] Corrective lenses are used to alter the gross focus of the eye to render a clearer image at the retinal plane by shifting the focus from in front of the plane to correct myopia or from behind the plane to correct hyperopia, respectively. However, the corrective approach to the conditions does not address the cause of the condition, but rather is merely prosthetic or intended to address symptoms. Most eyes do not have simple myopia or hyperopia but also have myopic astigmatism or hyperopic astigmatism. Astigmatic errors of focus cause the image of a point source of light to form as two mutually perpendicular lines at different focal distances. In the following discussion, the terms myopia and hyperopia are used to include simple myopia and myopic astigmatism and hyperopia and hyperopic astigmatism, respectively.

[0006] Known approaches that attempt to eliminate or reduce axial elongation of the eye, particularly in children, include the application of ophthalmic lenses that intentionally introduce myopic defocus in the field of vision. The myopic defocus introduces a ‘stop’ stimulus to the eye that results in limitation of eye growth. This is initially observed as a thickening of the choroid. Eye growth changes in response to retinal image defocus have been demonstrated in animal studies to be largely mediated through local retinal mechanisms, because eye length changes still occur when the optic nerve is damaged, and because imposing defocus on local retinal regions has been shown to result in altered eye growth localized to that specific retinal region.

[0007] Ophthalmic lenses with concentric annular designs have been shown to slow myopia progression. These include the Acuvue® Bifocal lens by Johnson & JohnsonVision Care, Inc. and the MiSight® contact lenses by CooperVision, Inc. These lenses have certain annular zones which contain optics that correct for myopia, while others introduce myopic defocus. Light from distant objects along the optical axis that pass through a given annulus essentially comes to a point focused on the optical axis, on the retina, and in front of the retina for the myopic correction annuli and myopic defocus annuli, respectively.

[0008] U.S. Patent No. 10,901,237, which is incorporated herein by reference in its entirety, describes various other lens designs for myopia control, with particular application in soft contact lenses. These lenses also have a concentric annular design where certain annular sections include optics that focus on the retina. For patients who require correction for myopia, these annular sections may include optics that redirect the focal point onto the retina. For patients who do not require myopia correction, these annular sections may provide no optical correction. Myopic defocus annular sections that are not centrally located contain optics that cause light passing therethrough to focus in front of the retina, but rather than a point focus on the optical axis, the light forms a noncoaxial ring focus. In some disclosed embodiments, a central portion of the lens contains an add power that induces myopic defocus but along the optical axis.SUMMARY OF THE DISCLOSURE

[0009] Aspects disclosed herein include myopic control progression modeling, and / or treatment. Myopic progression in an eye is the result in progression of axial elongation of the eye, and thus tracking of the axial length of a patient’s eye is a preferred metric to study the efficacy of a myopia control intervention treatment in an eye. It is desired to provide myopic control intervention treatment in myopic patients to try to reduce the rate of myopic progression before it continues to occur. A comparison of actual, tracked (from measurement) axial elongation progression in a patient’s eye can be made to an untreated predicted axial elongation progression data indicating the efficacy of myopic control intervention treatment as compared to an untreated eye. However, this does not indicate if the efficacy of the myopic control intervention treatment is as expected to, in turn, make future myopic control treatment decisions (e.g., continue with the same, try a different, or provide an additional myopic control treatment). It has been observed and discovered based on observed inhuman population data that there is an initial boost (i.e., increase) inthe efficacy of myopic control intervention treatment in a human eye under a myopic control intervention treatment in an initial time period (e.g. first year) of treatment as compared to an untreated eye. This initial boost of efficacy results from an observed consistent average reduction in axial elongation in a human eye (e.g., 0.2 millimeters (mm)) treated under a myopic control intervention in a first year of treatment as compared to an untreated eye. It has also been observed and discovered based on observation of human population data that after the initial boost in efficacy of a myopia control intervention treatment in a patient’s eye (e.g. an initial reduction in axial elongation rate), the treated eye thereafter has an approximately consistent average reduction per year (e.g., 15% per year) in further axial elongation although this rate is generally slows than that for compatible untreated eyes. It has also been observed and discovered based on observation of human population data that with the amount of initial boost of axial elongation from the myopia control treatment intervention, such is approximately equal to the sum of reduction in axial elongation in the second and third years with the continuation of the same treatment. That is, efficacy during the first year of treatment is double that which occurs in the first three years of treatment.

[0010] In this regard, in exemplary aspects disclosed herein, these observations can be used to actually generated “treated” predicted myopic control progression models of patients’ eyes treated with a myopic control intervention treatment. The “treated” predicted myopic control progression models for a given patient group(s) are synthesized using an “untreated” predicted myopic control progression data for the respective patient group(s) but then altered with (1) the application of observed initial boost in efficacy of the myopic control intervention treatment in a sample human population using the treatment for an initial time interval (e.g., 1 year), preferably in a multi-site, controlled, randomized masked clinical trial of said intervention (e.g. reduction of 0.2 mm in the initial time interval of myopic control intervention treatment) in the treated model; (2) an expected consistent reduction per year (e.g., 15% per year) in axial elongation subsequent to the initial time interval; and (3) an accumulated multi-year (e.g.. 3 year) time interval reduction of axial elongation that is double that in the initial time interval (e.g., 1styear). In this manner, a “treated” predicted myopic control progression model with synthesized “treated” predicted myopic control progression data over a time period can be generated for the patient’s eye under myopic control intervention treatment. This generated“treated” predicted myopic control progression model can then be used to determine whether the actual efficacy of the myopia control intervention treatment in a specific patient’s eye is consistent with an accurately predicted efficacy of the myopia control intervention treatment in the patient’s eye.

[0011] Actual, tracked myopia control progression data of a patient’s eye can be recorded and tracked over one or more time intervals and be compared to the “treated” predicted myopic control progression model for the patient’s group for the same time interval(s). In this manner, it can be observed if the actual, tracked myopic control progression in the treated eye deviates over time from the “treated” predicted “myopic control progression for the patient’s eye. An observed unexpected deviation in myopia control progression for the patient’s eye under the myopia control intervention treatment can be used to make or adjust myopic control treatment decisions for the patient.

[0012] In other exemplary aspects, the synthesizing “treated” predicted axial elongation progression models can also be used to generate synthesized “treated” refractive error models. There is a known correlation between axial elongation progression and its resulting effect on refractive error. Refractive progression in diopters (D) has been observed to be approximately double the axial elongation (mm) in progressing myopes. In this manner, “treated” predicted axial elongation progression models can be generated that can be used to determine whether the actual efficacy of the myopia control intervention treatment in terms of axial elongation changes in the patient’s eye over time is consistent with an accurately predicted efficacy of the myopia control intervention treatment in terms of predicted axial elongation changes over time. Actual, tracked refractive error data of a patient’s eye can be recorded and tracked over one or more time intervals and be compared to the “treated” predicted refractive error model for the patient’s group for the same time interval(s). In this manner, it can be observed if actual, tracked refractive error in the treated eye deviates over time from the “treated” predicted refractive error for the patient’s eye. An observed unexpected deviation in refractive error for a patient’s eye under the myopia control intervention treatment can be used to make or adjust myopic control treatment decisions for the patient.

[0013] In another exemplary aspect, a method of modeling myopic progression in a first patient’s eye under a first myopic control intervention treatment is provided. The method comprise providing an untreated predicted myopic control progression model fora time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age. The method also comprises generating a treated predicted myopic control progression model comprising a treated predicted myopic control progression data over the time period by: applying an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression, and generating a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the untreated predicted myopic control progression model, and applying a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in the treated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.

[0014] In another exemplary aspect, a computer system comprising processing circuitry is provided. The processing circuitry is configured retrieve an untreated predicted myopic control progression model for a time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age. The processing circuitry is further configured to generate a treated predicted myopic control progression model comprising a treated predictedmyopic control progression data over the time period by being configured to: apply an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression, generate a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the untreated predicted myopic control progression model, and apply a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in the treated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.

[0015] In another exemplary aspect, a method of treating myopic progression in a first patient’s eye under a first myopic control intervention treatment is provided. The method comprises tracking an actual myopic progression of the eye under the first myopic control intervention treatment. The method also comprises comparing the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

[0016] In another exemplary aspect a computer system comprising processing circuitry is provided. The processing circuitry is configured to track an actual myopic progression of the eye under the first myopic control intervention treatment. The processing circuitry is also configured to compare the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

[0017] Additional features and advantages will be set forth in the detailed description that follows and, in part, will be readily apparent to those skilled in the art from thedescription or recognized by practicing the aspects as described in the written description and claims hereof, as well as the appended drawings.

[0018] It is to be understood that both the foregoing general description and the following detailed description are merely exemplary and are intended to provide an overview or framework to understand the nature and character of the claims.

[0019] The accompanying drawings are included to provide a further understanding and are incorporated in and constitute a part of this specification. The drawings illustrate one or more aspects and, together with the description, serve to explain the principles and operation of the various aspects.BRIEF DESCRIPTION OF THE FIGURES

[0020] The foregoing and other features and advantages of the disclosure will be apparent from the following, more particular descriptions of the aspects of the disclosure, as illustrated in the accompanying drawings.

[0021] Figure 1A is a myopic control progression graph illustrating a predicted axial elongation progression (millimeters (mm) / year) of an exemplary first patient’s eye (e.g., of a 7-year-old East Asian child) under a myopic control intervention treatment (“treated predicted axial elongation progression”) over a time period, determined from a synthesized treated myopia control progression model based an observed initial boost in efficacy of the myopic control intervention treatment, an expected consistent reduction per year in axial elongation; and an accumulated multi-year reduction of axial elongation that is double that in the initial time interval, as compared to (1) an actual, tracked axial elongation in the first patient’s eye over the time period, (2) an untreated predicted axial elongation progression of a patient group that includes the first patient, as a method of determining efficacy of the myopic control intervention treatment in the first patient’s eye;

[0022] Figure IB is a refractive error graph illustrating a treated predicted refractive error model (diopters (D) / year) in the first patient’s eye in Figure 1A under the myopic control intervention treatment (“treated predicted refractive error”) using the treated predicted axial elongation progression in Figure 1A, as compared to (1) an actual, tracked refractive error in the first patient’s eye over the time period; and (2) an untreated predicted refractive error of the first patient’s eye using the untreated predicted axialelongation progression in Figure 1A, as a method of determining efficacy of the myopic control intervention treatment in the first patient’s eye;

[0023] Figure 2A is a myopic control progression graph illustrating another treated predicted axial elongation (mm / year) progression of a second patient’s eye (e.g., of a 10- year-old non-Asian child) under a myopic control intervention treatment over a time period determined from a synthesized myopia progression model based an observed initial boost in efficacy of the myopic control intervention treatment, an expected consistent reduction per year in axial elongation; and an accumulated multi-year reduction of axial elongation that is double that in the initial time interval, as compared to (1) an actual, tracked axial elongation progression in the second patient’s eye over the time period, and (2) an untreated predicted axial elongation progression of an eye of a patient group that includes the second patient, as a method of determining efficacy of the myopic control intervention treatment in the second patient’s eye.

[0024] Figure 2B is a refractive error graph illustrating another treated predicted refractive error model (D / year) in the second patient’s eye in Figure 1A under the myopic control intervention treatment using the treated predicted axial elongation progression in Figure 1A, as compared to (1) an actual, tracked refractive error in the second patient’s eye over the time period; and (2) an untreated predicted refractive error of the second patient’s eye using the untreated predicted axial elongation progression in Figure 1A, as a method of determining efficacy of the myopic control intervention treatment in the second patient’s eye.

[0025] Figure 3 is a flowchart illustrating an exemplary process of treating myopic progression in a patient’s eye using a generated treated predicted axial elongation progression of the patient’s eye under a myopic control intervention treatment;

[0026] Figure 4A is a graph illustrating an observed average treated and untreated three (3) year average axial elongation in the eyes of a human population of patients versus one (1) average axial elongation;

[0027] Figure 4B is another graph illustrating an observed average treated and untreated three (3) year average axial elongation in the eyes of a human population of patients versus one (1) average axial elongation;

[0028] Figure 5 is a graph illustrating a reliably observed approximate fifteen percent (15%) average reduction per year in axial elongation progression in the eyes of a humanpopulation of patients that are untreated with a myopic control intervention treatment and having known rates of axial elongation;

[0029] Figure 6A is a graph illustrating an observed approximate fifteen percent (15%) average reduction per year in axial elongation progression in both untreated and myopic control intervention-treated eyes of a human population of patients;

[0030] Figure 6B is a graph illustrating an observed approximate fifteen percent (15%) average reduction per year in axial elongation progression in both untreated and myopic control intervention-treated eyes of a human population of patients; and

[0031] Figure 7 is a block diagram of an exemplary computer system that can be configured to execute computer program instructions to generate time-based treated predicted myopic control progression models with treated predicted myopic control progression data over a period of time based on observed initial boost in efficacy of the myopic control intervention treatment, an expected consistent reduction per year in axial elongation; and an accumulated multi-year reduction of axial elongation that is double that in the initial time interval, and that can be used to analyze the efficacy of a myopic control intervention treatment in patients, based on a comparison of actual, tracked myopic progression in a patient’s eye over a time interval of a time period, to the respective treated predicted myopic control progression data in the treated predicted myopic control progression model.DETAILED DESCRIPTION

[0032] Aspects disclosed herein include myopic control progression modeling, and / or treatment. Myopic progression in an eye is the result in progression of axial elongation of the eye, and thus tracking of the axial length of a patient’s eye is a preferred metric to study the efficacy of a myopia control intervention treatment in an eye. It is desired to provide myopic control intervention treatment in myopic patients to try to reduce the rate of myopic progression before it continues to occur. A comparison of actual, tracked (from measurement) axial elongation progression in a patient’s eye can be made to an untreated predicted axial elongation progression data indicating the efficacy of myopic control intervention treatment as compared to an untreated eye. However, this does not indicate if the efficacy of the myopic control intervention treatment is as expected to, in turn, make future myopic control treatment decisions (e.g., continue with the same, try a different, orprovide an additional myopic control treatment). It has been observed and discovered based on observed inhuman population data that there is an initial boost (i.e., increase) in the efficacy of myopic control intervention treatment in a human eye under a myopic control intervention treatment in an initial time period (e.g. first year) of treatment as compared to an untreated eye. This initial boost of efficacy results from an observed consistent average reduction in axial elongation in a human eye (e.g., 0.2 millimeters (mm)) treated under a myopic control intervention in a first year of treatment as compared to an untreated eye. It has also been observed and discovered based on observation of human population data that after the initial boost in efficacy of a myopia control intervention treatment in a patient’s eye (e.g. an initial reduction in axial elongation rate), the treated eye thereafter has an approximately consistent average reduction per year (e.g., 15% per year) in further axial elongation although this rate is generally slows than that for compatible untreated eyes. It has also been observed and discovered based on observation of human population data that with the amount of initial boost of axial elongation from the myopia control treatment intervention, such is approximately equal to the sum of reduction in axial elongation in the second and third years with the continuation of the same treatment. That is, efficacy during the first year of treatment is double that which occurs in the first three years of treatment.

[0033] In this regard, in exemplary aspects disclosed herein, these observations can be used to actually generated “treated” predicted myopic control progression models of patients’ eyes treated with a myopic control intervention treatment. The “treated” predicted myopic control progression models for a given patient group(s) are synthesized using an “untreated” predicted myopic control progression data for the respective patient group(s) but then altered with (1) the application of observed initial boost in efficacy of the myopic control intervention treatment in a sample human population using the treatment for an initial time interval (e.g., 1 year), preferably in a multi-site, controlled, randomized masked clinical trial of said intervention (e.g. reduction of 0.2 mm in the initial time interval of myopic control intervention treatment) in the treated model; (2) an expected consistent reduction per year (e.g., 15% per year) in axial elongation subsequent to the initial time interval; and (3) an accumulated multi-year (e.g.. 3 year) time interval reduction of axial elongation that is double that in the initial time interval (e.g., 1styear). In this manner, a “treated” predicted myopic control progression model with synthesized“treated” predicted myopic control progression data over a time period can be generated for the patient’s eye under myopic control intervention treatment. This generated “treated” predicted myopic control progression model can then be used to determine whether the actual efficacy of the myopia control intervention treatment in a specific patient’s eye is consistent with an accurately predicted efficacy of the myopia control intervention treatment in the patient’s eye.

[0034] Actual, tracked myopia control progression data of a patient’s eye can be recorded and tracked over one or more time intervals and be compared to the “treated” predicted myopic control progression model for the patient’s group for the same time interval(s). In this manner, it can be observed if the actual, tracked myopic progression in the treated eye deviates over time from the “treated” predicted “myopic control progression for the patient’s eye. An observed unexpected deviation in myopia control progression for the patient’s eye under the myopia control intervention treatment can be used to make or adjust myopic control treatment decisions for the patient.

[0035] In this regard, Figure 1 A is a myopic control progression graph 100 illustrating a generated treated predicted myopia control progression model 102 of an exemplary first patient’s eye (e.g., of a 7-year-old East Asian child) under a myopic control intervention treatment. The treated predicted myopia control progression model 102 can be used to track the efficacy of the myopic control intervention treatment of the patient’ s treated eye . The treated predicted myopia control progression model 102 in this example is a curve that contains a series of plotted predicted myopia control progression data that extends over a time period (t) on the X-axis, which in this example is eleven (11) years from the patient’s year of age seven (7) to year of age eighteen (18). The treated predicted myopia control progression model 102 shows the predicted myopia control for the treated patient’s eye on the Y -axis as a function of the time on the X-axis as the age of the patient. In this example, the treated predicted myopia control progression model 102 is specifically a treated predicted axial elongation progression model 104 that is a curve of a series of predicted axial elongation progression data of changes in axial elongation of the patient’s treated eye expected with the myopic control intervention treatment as a function of the patient’s age on the X-axis. Myopic progression in an eye is the result of progression of axial elongation of the eye, and thus, the tracking of axial length of a patient’s eye is a preferred metric to study the efficacy of a myopia control interventiontreatment in an eye. 1. Brennan et al. Prog Retin Eye Res 2021:83;100923. The Y-axis of the myopic control progression graph 100 indicates a change in axial elongation (i.e. axial elongation progression) in millimeters (mm) in increasing manner (i.e., 0.1, 0.2 ... , 0., 0.7) as a function of the amplitude of the myopic control progression graph 100 at each give year of the age of the treated patient. Thus, in this example, the treated predicted axial elongation progression model 104 can be used to predict change in axial elongation (Y-axis) at each year of age of the treated patient (X-axis).

[0036] As also shown in the myopic control progression graph 100 in Figure 1A, an actual, tracked axial elongation progression curve 106 of axial elongation progression of the patient’s treated eye at each half year of the patient’s age in this example from ages 7 to 12 is plotted against the treated predicted axial elongation progression model 104. In the example in Figure 1A, the actual, tracked axial elongation progression curve 106 is based on a series of axial elongation progressions that were measured on each time differences before and after the show measurement at a given age to determine the change in axial elongation in the patient’s eye. This can be used to analyze if the efficacy of the myopic control intervention treatment is better or worse than expected (i.e. predicted). Each axial elongation progression value in the actual, tracked axial elongation progression curve 106 in this example is an average of the axial elongation measurement of the patient’s eye and equal distant times before and after a given year of age (e.g., for 7.5 years of age, at 7.25 years of age and 7.75 years of age) to determine the progression (i.e. change) in axial elongation. If at a given age of the treated patient, the actual axial elongation progression of the patient’s eye from the actual, tracked axial elongation progression curve 106 is greater than the predicted axial elongation progression from the treated predicted axial elongation progression model 104, it is known that the efficacy of the myopic control intervention treatment is less than expected in the patient’s eye. This information can be used for myopic control treatment decisions for the patient, as discussed in more detail below. In other words, if an actual, tracked axial elongation progression is larger than the predicted axial elongation progression from the treated predicted myopia control progression model 102 for a given age (Y -axis), this means that the actual, tracked axial elongation progression is greater (i.e. longer in length) than the predicted axial elongation progression for the myopic control intervention treatment.

[0037] For example, as shown in Figure 1A, at age 8.5 of the patient, the actual tracked axial elongation progression of the patient’s eye is approximately 0.52 mm. This measurement may have resulted from an average of the axial elongation measurement of the patient’s eye and equal distant time before and after 8 years of age (e.g., at 7.75 years of age and 8.25 years of age) to determine the change in axial elongation. However, the predicted axial elongation of the patient’s eye at age 8.5 from the treated predicted axial elongation progression model 104 is approximately 0.43 mm, meaning the efficacy of the myopic control intervention treatment is less than expected at age 8.5 of the patient.

[0038] If, however, at a given age of the treated patient, the actual, tracked axial elongation progression of the patient’s eye from the actual, tracked axial elongation progression curve 106 is less than (i.e. lower) the predicted axial elongation progression from the treated predicted axial elongation progression model 104, it is known that the efficacy of the myopic control intervention treatment is better than expected in the patient’s eye. In other words, if an actual, tracked axial elongation progression is less than the predicted axial elongation progression from the treated predicted myopia control progression model 102 for a given age (Y -axis), this means that the actual, tracked axial elongation progression is smaller (i.e. shorter in length) than the predicted axial elongation progression for the myopic control intervention treatment. For example, as shown in Figure 1A, at age 8 of the patient, the actual, tracked axial elongation progression of the patient’s eye is approximately 0.47 mm. However, the predicted axial elongation of the patient’s eye at age 8 from the treated predicted axial elongation progression model 104 is approximately 0.44 mm, meaning the efficacy of the myopic control intervention treatment is greater than expected at age 8 of the patient. This information can also be used for myopic control treatment decisions for the patient.

[0039] The predicted emmetropic axial elongation progression for the patient is also shown in the predicted emmetropic axial elongation progression curve 108 in the myopic control progression graph 100 in Figure 1A for convenience. The predicted emmetropic axial elongation progression curve 108 is based on axial elongation data from a human population of emmetropes with known rates of axial elongation. The predicted emmetropic axial elongation progression curve 108 allows one to also analyze the patient’s actual, tracked axial elongation progression as compared to an expected emmetropic axial elongation progression at a given age of the patient.

[0040] As discussed in more detail below, the treated predicted axial elongation progression model 104 shown in Figure 1A is generated based on an untreated predicted axial elongation progression model 110 (as a type of untreated predicted myopia control progression model), which is also shown in Figure 1A. The untreated predicted axial elongation progression model 110 is a curve that is based on axial elongation progression data from a human population of myopes having known rates of axial elongation progression (e.g., in a patient group of Asian children). As discussed in more detail below, it has been observed and discovered based on observed human population data having similar demographic characteristics for a given patient group (e.g., having same or similar race, geographic location, sex, parent myopia, education, etc.), and having known rates of myopic progression at a given age, that there is an initial boost (i.e., increase) in the efficacy of myopic control intervention treatment in a human eye in the patient group in a first year of treatment as compared to an untreated eye. This initial boost of efficacy results from an observed consistent average consistent reduction in axial elongation in a human eye (e.g., 0.2 millimeters (mm)) treated under a myopic control intervention treatment versus in an initial time period (e.g., first year) of treatment as compared to an untreated eye.

[0041] It has also been observed and discovered based on observation of human population data for a similar demographic profile (i.e., patient group, e.g., having same or similar race, geographic location, sex, parent myopia, education, etc.) in a given patient group that after the initial boost in efficacy of a myopia control intervention treatment in a patient’s eye (e.g. an initial reduction in axial elongation rate), the treated eye thereafter has an approximately consistent average reduction per year (e.g., 15% per year) in further axial elongation although this rate is generally slows than that for compatible untreated eyes.

[0042] It has also been observed and discovered based on observation of human population data that with the amount of initial boost of axial elongation from the myopia control treatment intervention (e.g. 0.2 mm), such is approximately equal to the sum of reduction in axial elongation in the second and third years with the continuation of the same treatment. That is, efficacy during the first year of treatment is a factor (e.g., double) that which occurs in the first three years of treatment in an example and observation. These observation can be used to determine the efficacy of myopic control progressiondata in a treated predicted myopic control progression model in a second time period after the initial time period in which an initial boost of myopic progression is applied from the untreated predicted myopic control progression model.

[0043] In this regard, as an example, Table 1 below shows exemplary axial elongation progression in millimeters (mm) of the untreated predicted axial elongation progression model 110, the treated predicted axial elongation progression model 104, and the predicted emmetropic axial elongation progression curve 108 in Figure 1A, all as a function of the patient’s age. The models in Figure 1A are based on a 7-year-old East Asian child, such that the untreated predicted axial elongation progression model 110 and predicted emmetropic axial elongation progression curve 108 are based on data from a human population of a patient group related to Asians having known rates of axial elongation progression.Table 1:

[0044] In this regard, using the above stated observations, the treated predicted axial elongation progression model 104 can be accurately synthesized from the untreated predicted axial elongation progression model 110. The treated predicted axial elongation progression model 104 can synthesized from the untreated predicted axial elongationprogression model 110 by first adding the assumed initial boost in the efficacy of the myopic control intervention treatment in a patient’s eye (e.g. a reduction in 0.2 mm axial elongation progression) at a given initial time interval (year 1 at age 7 in Table 1) after the treatment begins. For example, in the myopic control progression graph 100 in Figure 1A, the patient was treated with a myopic control intervention treatment prior to age 7. Thus, the axial elongation progression data at age 7 in the treated predicted axial elongation progression model 104 is set to be 0.2 mm less than the axial elongation progression data at age 7 in the untreated predicted axial elongation progression model 110. Note that first, there is a 0.2 mm initial boost reduction in axial elongation progression shown in Table 1 at age 7 (i.e., 0.481615186 mm) in the treated predicted axial elongation progression model 104 versus the axial elongation progression at age 7 (i.e., 0.681615186 mm) in the untreated predicted axial elongation progression model 110 in this example.

[0045] Then, the treated predicted axial elongation progression model 104 is further synthesized at a second time interval (year 2 at age 8) with axial elongation progression data based on a second time interval (year 2 at age 8) after the initial time interval (year 1 at age 7). This is done based on an accumulated multi-year (e.g.. 3 year, third time interval) time interval reduction of axial elongation in the treated predicted elongation progression model 104 being a factor (e.g. double multiplier) the reduction in axial elongation in the initial time interval in a treated eye (year 1 at age 7, e.g. 0.2 mm). In other words, in this example, as shown in Table 1 above, the initial axial elongation reduction at the initial time period (year 1 at age 7) between the untreated predicted axial elongation progression model 110 and the treated predicted axial elongation progression model 104 is 0.2 mm in this example. Thus, double that difference is 0.4 mm in this example. Thus, based on observations, it is assumed that the accumulated reduction in axial elongation in the untreated predicted myopic control progression model 110 of 1.75360398 mm = 0.681615186 mm (year 1 at age 7) + 0.579428061 mm (year 2 at age 8) + 0.492560737 mm (year 3 at age 9), will be 0.4 greater than accumulated reduction in axial elongation in the treated predicted myopic control progression model 104 over years 1-3 at ages 7-9. Using this observation, this means that the total accumulated reduction in axial elongation in the treated predicted myopic control progression model 104 in years 1-3 at ages 7-9 should be 0.4 less than 1.75360398 mm, or approximately 1.35360398mm. Knowing that the year 1 at age 7 axial elongation reduction in the treated predicted axial elongation progression model 104 is 0.2 mm, and knowing that the year 2 to year 3 reduction in axial elongation in the treated predicted myopic control progression model 104 is assumed to be a consistent, proportional reduction of 15%, this means that the axial elongation reduction at the second time interval at year 2 at age 8 will be 0.47132806 mm, and the axial elongation reduction at the third time interval at year 3 at age will be 15% less than 0.47132806, or 0.40067574. This is based on determining the accumulated axial reduction treated predicted myopic control progression model 104 in years 1-3 at ages 7- 9 being 1.35360398 mm (i.e., 0.481615186 mm + 0.471328061 mm + 0.400675737 mm), which is 0.4 mm less than accumulated reduction in axial elongation in the untreated predicted myopic control progression model 110 of 1.75360398 mm discussed above. Thus, in order for this to be true and the axial reduction at year 3 at age 9 being 15% less than the axial reduction at year 2 at age 8 in the treated predicted myopic control progression model 104, this means that the year 2 at age 8 axial reduction is approximately 0.47132806 mm.

[0046] Then, the treated predicted axial elongation progression model 104 is further synthesized starting at the third time interval (year 3 at age 9) by applying the second consistent proportional reduction (e.g. 15%) in myopic progression to the treated predicted myopic control progression data in the treated predicted myopic control progression model 104, starting at the third time interval (e.g. year 3 at age 9) after the second time interval (e.g., year 2 at age 8). The second consistent proportional reduction is determined from a treated human population under the first myopic control intervention treatment. The remaining axial elongation reductions in the treated predicted myopic control progression model 104 after year 3 at age 9 are 15% reductions from the previous year’s axial elongation reduction.

[0047] In this manner, the “treated” predicted axial elongation progression model 104 with “treated” predicted myopic control progression data for the time period ‘t’ in Figure 1A is generated for the patient’s eye under myopic control intervention treatment. This “treated” predicted axial elongation progression model 104 can be used to determine whether the actual efficacy of the myopia control intervention treatment in the patient’s eye is consistent with an accurately predicted efficacy of the myopia control intervention treatment in the patient’s eye.

[0048] Table 2 below shows an exemplary actual, tracked axial elongation progression in mm of the patient’s treated eye, which is a 7-year-old East Asian child who is part of the actual, tracked axial elongation progression curve 106 in Figure 1A.Table 2:

[0049] Figure IB is another refractive error graph 112 illustrating the treated tracked refractive error curve 114 in diopters (D) / year in the first patient’s eye in myopic control progression graph 100 in Figure 1A under the myopic control intervention treatment as compared to a treated predicted refractive error model 116 and an untreated predicted refractive error model 118. This data plotted in Figure IB is also shown in Table 3 below. The treated predicted refractive error model 116 is plotted within a 50% prediction interval shown between the upper 50% and lower 50% data, also in Table 3 below, that represents the prediction that 50% of treated patients will fall within that interval given all of the assumptions and data variation in the treated predicted refractive error model 116 development for a specific or targeted population. The treated predicted refractive error model 116 is also plotted within a 90% prediction interval shown between the upper 90% and lower 90% data, also in Table 3 below, that represents the prediction that 90%of treated patients will fall within that interval given all of the assumptions and data variation in the treated predicted refractive error model 116 development for a specific or targeted population.Table 3:

[0050] In the example in Figure IB, the treated tracked refractive error curve 114 is based on a series of refractive errors that were measured at a given age. The treated tracked refractive error curve 114 is based on actual measurements of refractive error in the patient in the Y-axis corresponding to different ages along the time period of years in the X-axis. The untreated predicted refractive error model 118 is synthesized based on the untreated predicted axial elongation progression model 110 in Figure 1A. Refractive progression in diopters (D) has been observed to be approximately double the axial elongation (mm) in progressing myopes. The treated predicted refractive error model 116 is synthesized based on the treated predicted axial elongation progression model 104 in Figure 1A, also based on the observation that refractive progression in diopters (D) has been observed to be approximately double the axial elongation (mm) in progressing myopes. In this manner, the models in Figure IB can be used to compare the actual,tracked measured refractive error in a patient to the treated predicted refractive error model 116 to determine the efficacy of the myopic control intervention treatment. This can be used to analyze if the efficacy of the myopic control intervention treatment is better or worse than expected (i.e. predicted).

[0051] If, at a given age of the treated patient, the actual, tracked refractive error of the patient’s eye from the treated tracked refractive error curve 114 is greater than the predicted refractive error from the treated predicted refractive error model 116, it is known that the efficacy of the myopic control intervention treatment is less than expected in the patient’s eye. This information can be used for myopic control treatment decisions for the patient, as discussed in more detail below. If, however, at a given age of the treated patient, the actual, tracked refractive error of the patient’s eye from the treated tracked refractive error curve 114 is less than (i.e. lower) the treated predicted refractive error from the treated predicted refractive error model 116, it is known that the efficacy of the myopic control intervention treatment is better than expected in the patient’s eye.

[0052] Figure 2A is another myopic control progression graph 200 illustrating a generated treated predicted myopia control progression model 202 of an exemplary first patient’s eye (e.g., of a 10-year-old non-Asian child) under a myopic control intervention treatment. The treated predicted myopia control progression model 202 in this example is a curve that contains a series of plotted predicted myopia control progression data that extends over a time period (t) on the X-axis, which in this example is eight (8) years from the patient’s year of age ten (10) to year of age eighteen (18). The treated predicted myopia control progression model 202 shows the predicted myopia control for the treated patient’s eye on the Y -axis as a function of the time on the X-axis as the age of the patient. In this example, the treated predicted myopia control progression model 202 is specifically a treated predicted axial elongation progression model 204 that is a curve of a series of predicted axial elongation progression data of changes in axial elongation of the patient’s treated eye expected with the myopic control intervention treatment as a function of the patient’s age on the X-axis.

[0053] The predicted emmetropic axial elongation progression for the patient is also shown in the predicted emmetropic axial elongation progression curve 208 in the myopic control progression graph 200 in Figure 2A for convenience. The predicted emmetropic axial elongation progression curve 208 is based on axial elongation data from a humanpopulation of emmetropes with known rates of axial elongation. The predicted emmetropic axial elongation progression curve 208 allows one to also analyze the patient’s actual, tracked axial elongation progression as compared to an expected emmetropic axial elongation progression at a given age of the patient.

[0054] The treated predicted axial elongation progression model 204, shown in Figure 2A, is generated based on an untreated predicted axial elongation progression model 210 (as a type of untreated predicted myopia control progression model), which is also shown in the Figure 2A. The untreated predicted axial elongation progression model 210 is a curve that is based on axial elongation progression data from a human population of myopes having known rates of axial elongation progression (e.g., of a patient group of non-Asian children). Like the treated predicted axial elongation progression model 104 in Figure 1A, the treated predicted axial elongation progression model 204 in Figure 2A can be accurately synthesized from the untreated predicted axial elongation progression model 210. The treated predicted axial elongation progression model 204 can be synthesized from the untreated predicted axial elongation progression model 210 by first adding the assumed initial boost in the efficacy of the myopic control intervention treatment in a patient’s eye (e.g. a reduction in 0.2 mm axial elongation progression) at a given initial time interval (e.g. 1 year) after the treatment begins. For example, in the myopic control progression graph 200 in Figure 2A, the patient was treated with a myopic control intervention treatment prior to age 10. Thus, the axial elongation progression data at age 10 in the treated predicted axial elongation progression model 204 is set to be 0.2 mm less than the axial elongation progression data at age 10 in the untreated predicted axial elongation progression model 210.

[0055] Then, the treated predicted axial elongation progression model 204 is further synthesized like discussed above for the treated predicted axial elongation progression model 104 in Figure 1A and Table 1.

[0056] Like described above in Figure 1A, the “treated” predicted axial elongation progression model 204 can be used to be plotted against actual, tracked and tracked myopic axial elongation progression in the patient to determine if the efficacy of the myopic control intervention treatment is better or worse than predicted. Further treatment decisions can be made based on this comparison.

[0057] Table 4 below shows exemplary axial elongation progression in millimeters (mm) of the untreated predicted axial elongation progression model 210, the treated predicted axial elongation progression model 204, and the predicted emmetropic axial elongation progression curve 208 in Figure 2A, all as a function of the patient’s age. The models in Figure 2A are based on a 10-year-old non-Asian child, such that the untreated predicted axial elongation progression model 210 and predicted emmetropic axial elongation progression curve 208 are based on data from a human population of a patient group related to non-Asian children having known rates of axial elongation progression. Note that there is a 0.2 mm reduction in axial elongation progression shown in Table 4 at age 10 (i.e. 0.28468 mm) in the treated predicted axial elongation progression model 104 versus the axial elongation progression at age 10 (i.e. 0.08468 mm) in the treated predicted axial elongation progression model 206. The remaining treated predicted axial elongation progression data in the treated predicted axial elongation progression model 204 is a consistent proportional reduction of 15% per year) in axial elongation from the treated predicted axial elongation progression data the preceding year. For example, as shown in Table 4, the treated predicted axial elongation progression data in the treated predicted axial elongation progression model 204 at year 12 (0.113836mm) is generated to be 15% less than the treated predicted axial elongation progression data at year 11 (0. 0.133901 mm), and so on.

[0058] Figure 2B is a refractive error graph 212 illustrating the treated tracked refractive error curve 214 in diopters (D) / year in the first patient’s eye in myopic control progression graph 200 in Figure 2A under the myopic control intervention treatment as compared to a treated predicted refractive error model 216 and an untreated predicted refractive error model 218. This data plotted in Figure 2B is also shown in Table 5 below. The treated predicted refractive error model 216 is plotted within a 50% prediction interval shown between the upper 50% and lower 50% data, also in Table 5 below, that represents the prediction that 50% of treated patients will fall within that interval given all of the assumptions and data variation in the treated predicted refractive error model 216 development for a specific or targeted population. The treated predicted refractive error model 216 is also plotted within a 90% prediction interval shown between the upper 90% and lower 90% data, also in Table 5 below, that represents the prediction that 90% of treated patients will fall within that interval given all of the assumptions and data variation in the treated predicted refractive error model 216 development for a specific or targeted population.Table 5:

[0059] In the example in Figure 2B, the treated tracked refractive error curve 214 is based on a series of refractive errors that were measured at a given age. The treated tracked refractive error curve 214 is based on actual measurements of refractive error inthe patient in the Y-axis corresponding to different ages along the time period of years in the X-axis. The untreated predicted refractive error model 218 is synthesized based on the untreated predicted axial elongation progression model 210 in Figure 2A. Refractive progression in diopters (D) has been observed to be approximately double the axial elongation (mm) in progressing myopes. The treated predicted refractive error model 216 is synthesized based on the treated predicted axial elongation progression model 204 in Figure 2A, also based on the observation that refractive progression in diopters (D) has been observed to be approximately double the axial elongation (mm) in progressing myopes. In this manner, the models in Figure 2B can be used to compare the actual, tracked measured refractive error in a patient to the treated predicted refractive error model 216 to determine the efficacy of the myopic control intervention treatment. This can be used to analyze if the efficacy of the myopic control intervention treatment is better or worse than expected (i.e. predicted).

[0060] If, at a given age of the treated patient, the actual, tracked refractive error of the patient’s eye from the treated tracked refractive error curve 214 is greater than the predicted refractive error from the treated predicted refractive error model 216, it is known that the efficacy of the myopic control intervention treatment is less than expected in the patient’s eye. This information can be used for myopic control treatment decisions for the patient, as discussed in more detail below. If, however, at a given age of the treated patient, the actual, tracked refractive error of the patient’s eye from the treated tracked refractive error curve 214 is less than (i.e. lower) the treated predicted refractive error from the treated predicted refractive error model 216, it is known that the efficacy of the myopic control intervention treatment is better than expected in the patient’s eye.

[0061] Note that the untreated and treated predicted myopic control progression models can be developed for more than one patient group, such as in Figures 1A-1B for an Asian myope child and Figures 2A-2B for a non-Asian myope child. In this manner, a treated predicted myopic control progression can be developed for different patient groups based on untreated predicted myopic control progression models developed using observed data of myopic control progression for untreated eyes for human populations for such groups.

[0062] Figure 3 is a flowchart illustrating an exemplary process 300 of modeling and / or treating myopic progression in a patient’s eye using a generated treated predictedaxial elongation progression of the patient’s eye under a myopic control intervention treatment. The process 300 is described in reference to the models in Figures 1A-2B as examples.

[0063] The process 300 includes providing an untreated predicted myopic control progression model 110, 210 for a time period as a function of time ‘t’ (block 302 in Figure 3). The process 300 also includes generating a treated predicted myopic control progression model 102, 104, 202, 204 comprising a treated predicted myopic control progression data over the time period (block 304 in Figure 3) like described above. The process 300 can also optionally include tracking an actual myopic progression of the eye under the first myopic control intervention treatment over a second time interval after the initial time interval of the time period (block 306 in Figure 3). The process 300 also includes comparing the actual myopic progression of the eye under the first myopic control intervention treatment over the second time interval to the treated predicted myopic control progression data over the second time interval in the treated predicted myopic control progression model 102, 104, 202, 204 (block 308 in Figure 3). The process 300 also includes determining a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye over the second time interval to the treated predicted myopic control progression data over the second time interval in the treated predicted myopic control progression model 102, 104, 202, 204 (block 310 in Figure 3).

[0064] Based on the comparison of the patient eye’s actual, tracked myopic control progression (e.g., using actual, tracked axial elongation progression curve 106 in Figure 1A or actual, tracked axial elongation progression curve 106 in Figure 2A) under the first myopic control intervention treatment to the treated predicted myopic control progression model (e.g., using models 102, 104 in Figure 1A, models 202, 204 in Figure 2A, model 116 in Figure IB, model 216 in Figure 2B), treatment decisions can be made based on such comparison. For example, a next myopic control intervention treatment for the patient’s eye based on this comparison can be provided. If the actual, tracked myopic control progression of the eye is greater than the predicted myopic control progression data in the treated predicted myopic control progression model, this means that the actual efficacy of the existing myopic control intervention treatment was worse than predicted. In this case, a next, new myopic control intervention treatment can be prescribed for thepatient’s eye in addition to or in lieu of their existing myopic control intervention treatment. The process described in Figure 3 and above can be repeated at a subsequent time for the patient after the additional or other myopic control intervention treatment has been effectuated. If, however, the actual, tracked myopic control progression of the eye is less than the treated predicted myopic control progression model, this means that the actual efficacy of the existing myopic control intervention treatment was better than predicted. The existing myopic control intervention treatment may be continued for the patient.

[0065] Figure 4A is a graph 400 illustrating an observed average treated and untreated three (3) year average axial elongation in the eyes of a human population of patients versus one (1) average axial elongation. This is to show that the axial elongation progresses consistently proportional per year as part of the observation that the eye has an approximately consistent average reduction per year (e.g., 15% per year) in further axial elongation whether treated with a myopic control intervention treatment or not. This observation can be used to synthesize a treated predicted myopic control progression model from an untreated myopic control progression model after recognizing an initial boost in myopic control intervention treatment efficacy. As shown in Figure 4A, a treated curve 402 of the 3 -year axial elongation in mm on the Y-axis plotted against the 1-year axial elongation for myopic control-treated eyes is shown. An untreated curve 404 of the 3 -year axial elongation in mm on the Y-axis plotted against the 1-year axial elongation for eyes not treated with a myopic control intervention treatment is also shown in Figure 4A. A fitted line 406 to the date in both curves 402, 404 shows a generally consistent proportional axial elongation progression as a function of time.

[0066] Figure 4B is another graph 408 illustrating an observed average treated three (3) year average axial elongation in eyes of a human population of patients versus one (1) average axial elongation. As shown therein, a 95% confidence interval 410 and 95% prediction interval 412 of data of the treated three (3) year average axial elongation in eyes of a human population of patients plotted against one (1) average axial elongation both exhibit consistent proportional axial elongation progression. A fitted line 414 through the axial elongation data of the95% confidence interval 410 and 95% prediction interval 412 further generally consistent proportional axial elongation progression as a function of time.

[0067] Figure 5 is a graph 500 illustrating a reliably observed approximate fifteen percent (15%) average reduction per year in axial elongation in the eyes of a human population of patients having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age that are untreated with a myopic control intervention treatment and having known rates of axial elongation. Curve 502 is a fitted curve of predicted axial elongation on the Y -axis as a function of the mean age of the patients (in years), showing an approximate fifteen percent (15%) average reduction per year in axial elongation progression.

[0068] Figure 6A is a graph 600 illustrating an observed approximate fifteen percent (15%) average reduction per year in axial elongation progression in untreated and treated curves 602, 604 of respective untreated and myopic control intervention treated eyes of a human population of patients. Chamberlain et al. OptomVisSci 2022;99:204 2. Hiraoka et al. IOVS 2012;53 :3913. The respective untreated and treated curves 602, 604 plot axial elongation (mm) along the Y -axis as a function of follow up time (months) along the X- axis. As shown in the treated curve 604 in Figure 6A, there is an initial boost in the efficacy of the eyes under myopic control intervention treatment as compared to the untreated curve 602.

[0069] Figure 6B is a graph 606 illustrating an observed approximate fifteen percent (15%) average reduction per year in axial elongation progression in untreated and treated curves 608, 610 of respective untreated and myopic control intervention treated eyes of a human population of patients. Chamberlain et al. OptomVisSci 2022;99:204 2. Hiraoka et al. IOVS 2012;53:3913. The untreated and treated curves 608, 610 plot axial elongation (mm) along the Y-axis as a function of time (years) along the X-axis. As shown in the treated curve 610 in Figure 6B, there is an initial boost in the efficacy of the eyes under myopic control intervention treatment as compared to the untreated curve 608.

[0070] Figure 7 is a block diagram of an exemplary computer system 700 that can be configured to execute computer program instructions from a computer-readable medium to generate time-based treated predicted myopic control progression models with predicted myopic control progression data over a period of time, including in accordance with the process 300 in Figure 3. These treated predicted myopic control progression models can then be used by a person or by the computer system 700 to analyze the efficacy of a myopic control intervention treatment in patients based on a comparison of actual,measured myopic control progression in a patient’s eye over an initial time interval of the time period, to the respective treated predicted myopic control progression data in the treated predicted myopic control progression model.

[0071] The computer system 700 includes processing circuitry 702 configured to execute instructions from a computer-readable medium to perform these and / or any of the functions or processing described herein, including the generation of treated predicted myopic control progression models that can then used by a person or by the computer system 700 to analyze the efficacy of a myopic control intervention treatment in patients, based on a comparison of actual, measured myopic control progression in a patient’s eye over a time interval of the time period, to the respective treated predicted myopic control progression data in the treated predicted myopic control progression model. The computer system 700 may be connected (e.g., networked) to other machines in a LAN (Local Area Network), LIN (Local Interconnect Network), automotive network communication protocol (e.g., FlexRay), an intranet, an extranet, or the Internet. While only a single device is illustrated, the computer system 700 may include any collection of devices that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Accordingly, any reference in the disclosure and / or claims to a computer system, computing system, computer device, computing device, control system, control unit, processor device, processing circuitry, etc., includes reference to one or more such devices to individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. For example, a control system may include a single control unit or a plurality of control units connected or otherwise communicatively coupled to each other, such that any performed function may be distributed between the control units as desired. Further, such devices may communicate with each other or other devices by various system architectures, such as directly or via a Controller Area Network (CAN) bus, etc.

[0072] The computer system 700 may comprise at least one computing device or electronic device capable of including firmware, hardware, and / or executing software instructions to implement the functionality described herein. The computer system 700 may include processing circuitry 702 (e.g., processing circuitry including one or more processor devices or control units). The computer system 700 also includes amemory 704 and a system bus 706. The computer system 700 can be configured to store the aforementioned models / curves as data 707, including an untreated predicted myopic control progression model (e.g., models 110, 118, 210, 218), a treated predicted myopic control progression model (e.g., models 102, 104, 116, 202, 204), and / or actual, tracked myopic progression curves (e.g., curves 106, 206) of the eye. The computer system 700 can be configured to store in the memory system 704 the difference between the actual myopic progression of the eye under the first myopic control intervention treatment over the second time interval and the treated predicted myopic control progression data over the second time interval in the treated predicted myopic control progression model.

[0073] The computer system 700 may include at least one computing device having the processing circuitry 702. The system bus 706 provides an interface for system components including, but not limited to, the memory 704 and the processing circuitry 702. The processing circuitry 702 may include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 704 executed by the processing circuitry 702. The processing circuitry 702 may, for example, include a general-purpose processor, an application specific processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processing circuitry 702 may further include computer executable code that controls the operation of the programmable device.

[0074] The system bus 706 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of bus architectures. The memory 704 may be one or more devices for storing data and / or computer code for completing or facilitating methods described herein. The memory 704 may include database components, object code components, script components, or other types of information structure for supporting the various activities herein. Any distributed or local memory device may be utilized with the systems and methods of this description. The memory 704 may be communicably connected to the processing circuitry 702 (e.g., via a circuitor any other wired, wireless, or network connection) and may include computer code for executing one or more processes described herein. The memory 704 may include nonvolatile memory 708 (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory 710 (e.g., random-access memory (RAM)), or any other medium which can be used to carry or store desired program code in the form of machineexecutable instructions or data structures and which can be accessed by a computer or other machine with processing circuitry 702. A basic input / output system (BIOS) 712 may be stored in the non-volatile memory 708 and can include the basic routines that help to transfer information between elements within the computer system 700.

[0075] The computer system 700 may further include or be coupled to a non- transitory computer-readable storage medium such as the storage device 714, which may comprise, for example, an internal or external hard disk drive (HDD) (e.g., enhanced integrated drive electronics (EIDE) or serial advanced technology attachment (SATA)), HDD (e.g., EIDE or SATA) for storage, flash memory, or the like. The storage device 714 and other drives associated with computer-readable media and computer- usable media may provide non-volatile storage of data, data structures, computerexecutable instructions, and the like.

[0076] Computer-code which is hard or soft coded may be provided in the form of one or more modules. The module(s) can be implemented as software and / or hard-coded in circuitry to implement the functionality described herein in whole or in part. The modules may include an operating system 716 and / or one or more program modules 718. All or a portion of the examples disclosed herein may be implemented as a computer program 720 stored on a transitory or non-transitory computer-usable or computer- readable storage medium (e.g., single medium or multiple media), such as the storage device 714, which includes complex programming instructions (e.g., complex computer- readable program code) to cause the processing circuitry 702 to carry out actions described herein. Thus, the computer-readable program code of the computer program 720 can comprise software instructions for implementing the functionality of the examples described herein when executed by the processing circuitry 702. In some examples, the storage device 714 may be a computer program product (e.g., readablestorage medium) storing the computer program 720 thereon, where at least a portion of a computer program 720 may be loadable (e.g., into a processor) for implementing the functionality of the examples described herein when executed by the processing circuitry 702. The processing circuitry 702 may serve as a controller or control system for the computer system 700 that is to implement the functionality described herein.

[0077] The computer system 700 may include an input device interface 722 configured to receive input and selections to be communicated to the computer system 700 when executing instructions, such as from a keyboard, mouse, touch-sensitive surface, etc. Such input devices may be connected to the processing circuitry 702 through the input device interface 722 coupled to the system bus 706 but can be connected through other interfaces, such as a parallel port, an Institute of Electrical and Electronic Engineers (IEEE) 1394 serial port, a Universal Serial Bus (USB) port, an IR interface, and the like. The computer system 700 may include an output device interface 724 configured to forward output, such as to a display, a video display unit (e.g., a liquid crystal display (LCD), or a cathode ray tube (CRT)). The computer system 700 may include a communications interface 726 suitable for communicating with a network as appropriate or desired.

[0078] It is to be understood that the disclosure is not to be limited to the specific aspects disclosed and that modifications and other aspects are intended to be included within the scope of the appended claims and their equivalents. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. The aspects set forth below represent the necessary information to enable those skilled in the art to practice the disclosure and illustrate the best mode of practicing the disclosure. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. Many modifications and other embodiments of the disclosure set forth herein will come to mind to one skilled in the art to which the disclosure pertains, having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Although shown and described in what is believed to be the most practical and specific aspects disclosed, modifications andother aspects are intended to be included within the scope of the appended claims It is apparent that departures from specific designs and methods described and shown will suggest themselves io those skilled in the art and may be used without departing from the spirit and scope of the disclosure.

[0079] Implementation examples are described in the following numbered clauses:1. A method of modeling myopic progression in a first patient’s eye under a first myopic control intervention treatment, comprising: providing an untreated predicted myopic control progression model for a time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age; generating a treated predicted myopic control progression model comprising a treated predicted myopic control progression data over the time period by: applying an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression; generating a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progressionbetween the initial time interval and a third time interval from the untreated predicted myopic control progression model; and applying a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in the treated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.2. The method of clause 1, wherein the time period comprises a plurality of years, the initial time interval is one (1) year, the second time interval is two (2) years, and the third time interval is three (3) years.3. The method of clauses 1 to 2, wherein the time period is inclusive of the first patient’s age from 10 years of age to 18 years of age.4. The method of clauses 1 to 2, wherein the time period is inclusive of the first patient’s age from 7 years of age to 15 years of age.5. The method of clauses 1 to 4, wherein: the untreated predicted myopic control progression model comprises an untreated predicted axial elongation progression model; the untreated predicted myopic control progression data comprises untreated predicted axial elongation progression data; the treated predicted myopic control progression model comprises a treated predicted axial elongation progression model; and the untreated predicted myopic control progression data comprises treated predicted axial elongation progression data;6. The method of clause 5, wherein the first consistent proportional reduction in myopic progression is a 15% reduction in axial elongation.7. The method of clause 6. wherein the second consistent proportional reduction in myopic progression is a 15% reduction in axial elongation.8. The method of clauses 5 to 7, wherein the initial boost in the reduction of axial elongation in the eye over the initial time interval of the time period is 0.2 millimeters (mm).9. The method of clauses 1 to 8, further comprising: generating a treated predicted refractive error model comprising a predicted refractive error over the time period based on the treated predicted axial elongation progression model.10. The method of clauses 1 to 9, further comprising: providing a second untreated predicted myopic control progression model for a second time period as a function of time, wherein the second untreated predicted myopic control progression model comprising second untreated predicted myopic progression data having the first consistent proportional reduction in myopic progression over the time period; generating a second treated predicted myopic control progression model comprising a second treated predicted myopic control progression data over the time period by: applying a second initial boost in efficacy of myopic control progression to the second untreated predicted myopic progression data for the initial time interval in the second untreated predicted myopic control progression model, the second initial boost in efficacy of myopic progression determined from a second human population under a second myopic control intervention treatment having known rates of myopic progression; generating a fourth efficacy in myopic control progression in the second treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulateddifference in efficacy between the initial time interval and the third time interval in the second treated predicted myopic control progression model being a second known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the second untreated predicted myopic control progression model; and applying a fourth consistent proportional reduction in myopic progression to the second untreated predicted myopic control progression data in the second treated predicted myopic control progression model, starting at the third time interval, the fourth consistent proportional reduction determined from a treated human population under the second myopic control intervention treatment. The method of clauses 1 to 10, further comprising: tracking an actual myopic progression of the eye under the first myopic control intervention treatment; and comparing the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model. The method of clause 11, further comprising: determining a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model. The method of clause 12, further comprising: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; andin response to determining the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as having at least one new myopic control intervention treatment for the eye.14. The method of clause 13, comprising determining the next myopic control treatment for the eye as having a new myopic control intervention treatment for the eye different from the first myopic control intervention treatment.15. The method of clause 14, comprising determining the next myopic control intervention treatment for the eye as having a new myopic control intervention treatment for the eye in addition to the first myopic control intervention treatment.16. The method of clause 12, wherein: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is less than the treated predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as the first myopic control intervention treatment for the eye.17. A computer system comprising processing circuitry configured to: retrieve an untreated predicted myopic control progression model for a time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population havingsimilar demographic characteristics to the first patient and having known rates of myopic progression at a given age; generate a treated predicted myopic control progression model comprising a treated predicted myopic control progression data over the time period by being configured to: apply an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression; generate a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the untreated predicted myopic control progression model; and apply a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in the treated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.18. The computer system of clause 17, wherein the processing circuitry is further configured to: track an actual myopic progression of the eye under the first myopic control intervention treatment; andcompare the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model.19. The computer system of clause 18, wherein the processing circuitry is further configured to: determine a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model.20. The computer system of clauses 17 to 20, further comprising a memory system, wherein the processing circuitry is further configured to: store in the memory system, the untreated predicted myopic control progression model; retrieve the untreated predicted myopic control progression model from the memory system; and store in the memory system the generated treated predicted myopic control progression model.21. A method of treating myopic progression in a first patient’s eye under a first myopic control intervention treatment, comprising: tracking an actual myopic progression of the eye under the first myopic control intervention treatment; and comparing the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model in clause 1.22. The method of clause 21, further comprising: determining a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treatedpredicted myopic control progression data in the treated predicted myopic control progression model.23. The method of clause 22, further comprising: determining whether the actual myopic progression of the eye is greater than the predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is greater than the predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as having at least one new myopic control intervention treatment for the eye.24. The method of clause 23, comprising determining the next myopic control treatment for the eye as having a new myopic control intervention treatment for the eye different from the first myopic control intervention treatment.25. The method of clause 24, comprising determining the next myopic control intervention treatment for the eye as having a new myopic control intervention treatment for the eye in addition to the first myopic control intervention treatment.26. The method of clause 22, wherein: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is less than the treated predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as the first myopic control intervention treatment for the eye.27. A computer system comprising processing circuitry configured to:track an actual myopic progression of the eye under the first myopic control intervention treatment; and compare the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model in clause 1.28. The computer system of clause 27, wherein the processing circuitry is further configured to: determine a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model.29. The computer system of clauses 27 to 28, wherein the processing circuitry is further configured to: store in the memory system, the received actual myopic progression of the eye; and store in the memory system, a difference between the actual myopic progression of the eye under the first myopic control intervention treatment, and the treated predicted myopic control progression data in the treated predicted myopic control progression model.

Claims

What is claimed is:

1. A method of modeling myopic progression in a first patient’s eye under a first myopic control intervention treatment, comprising: providing an untreated predicted myopic control progression model for a time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age; generating a treated predicted myopic control progression model comprising a treated predicted myopic control progression data over the time period by: applying an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression; generating a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the untreated predicted myopic control progression model; and applying a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in thetreated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.

2. The method of claim 1 , wherein the time period comprises a plurality of years, the initial time interval is one (1) year, the second time interval is two (2) years, and the third time interval is three (3) years.

3. The method of claim 1, wherein the time period is inclusive of the first patient’s age from 10 years of age to 18 years of age.

4. The method of claim 1, wherein the time period is inclusive of the first patient’s age from 7 years of age to 15 years of age.

5. The method of claim 1, wherein: the untreated predicted myopic control progression model comprises an untreated predicted axial elongation progression model; the untreated predicted myopic control progression data comprises untreated predicted axial elongation progression data; the treated predicted myopic control progression model comprises a treated predicted axial elongation progression model; and the untreated predicted myopic control progression data comprises treated predicted axial elongation progression data;6. The method of claim 5, wherein the first consistent proportional reduction in myopic progression is a 15% reduction in axial elongation.

7. The method of claim 6, wherein the second consistent proportional reduction in myopic progression is a 15% reduction in axial elongation.

8. The method of claim 5, wherein the initial boost in the reduction of axial elongation in the eye over the initial time interval of the time period is 0.2 millimeters (mm).

9. The method of claim 1, further comprising: generating a treated predicted refractive error model comprising a predicted refractive error over the time period based on the treated predicted axial elongation progression model.

10. The method of claim 1, further comprising: providing a second untreated predicted myopic control progression model for a second time period as a function of time, wherein the second untreated predicted myopic control progression model comprising second untreated predicted myopic progression data having the first consistent proportional reduction in myopic progression over the time period; generating a second treated predicted myopic control progression model comprising a second treated predicted myopic control progression data over the time period by: applying a second initial boost in efficacy of myopic control progression to the second untreated predicted myopic progression data for the initial time interval in the second untreated predicted myopic control progression model, the second initial boost in efficacy of myopic progression determined from a second human population under a second myopic control intervention treatment having known rates of myopic progression; generating a fourth efficacy in myopic control progression in the second treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and the third time interval in the second treated predicted myopic control progression model being a second known factor applied to theaccumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the second untreated predicted myopic control progression model; and applying a fourth consistent proportional reduction in myopic progression to the second untreated predicted myopic control progression data in the second treated predicted myopic control progression model, starting at the third time interval, the fourth consistent proportional reduction determined from a treated human population under the second myopic control intervention treatment.

11. The method of claim 1, further comprising: tracking an actual myopic progression of the eye under the first myopic control intervention treatment; and comparing the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

12. The method of claim 11, further comprising: determining a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

13. The method of claim 12, further comprising: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model:determining the next myopic control intervention treatment for the eye as having at least one new myopic control intervention treatment for the eye.

14. The method of claim 13, comprising determining the next myopic control treatment for the eye as having a new myopic control intervention treatment for the eye different from the first myopic control intervention treatment.

15. The method of claim 14, comprising determining the next myopic control intervention treatment for the eye as having a new myopic control intervention treatment for the eye in addition to the first myopic control intervention treatment.

16. The method of claim 12, wherein: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is less than the treated predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as the first myopic control intervention treatment for the eye.

17. A computer system comprising processing circuitry configured to: retrieve an untreated predicted myopic control progression model for a time period as a function of time, wherein the untreated predicted myopic control progression model comprising untreated predicted myopic progression data having a first consistent proportional reduction in myopic progression over the time period as determined from a human population having similar demographic characteristics to the first patient and having known rates of myopic progression at a given age;generate a treated predicted myopic control progression model comprising a treated predicted myopic control progression data over the time period by being configured to: apply an initial boost in efficacy of myopic control progression to the untreated predicted myopic progression data for an initial time interval in the untreated predicted myopic control progression model, the initial boost in efficacy of myopic progression determined from a human population under the first myopic control intervention treatment having known rates of myopic progression; generate a second efficacy in myopic control progression in the treated predicted myopic control progression model for a second time interval after the initial time interval, based on an accumulated difference in efficacy between the initial time interval and a third time interval after the second time interval in the treated predicted myopic control progression model being a known factor applied to the accumulated difference in efficacy in myopic progression between the initial time interval and a third time interval from the untreated predicted myopic control progression model; and apply a second consistent proportional reduction in myopic progression to the untreated predicted myopic control progression data in the treated predicted myopic control progression model, starting at a third time interval after the second time interval, the second consistent proportional reduction determined from a treated human population under the first myopic control intervention treatment.

18. The computer system of claim 17, wherein the processing circuitry is further configured to: track an actual myopic progression of the eye under the first myopic control intervention treatment; andcompare the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

19. The computer system of claim 18, wherein the processing circuitry is further configured to: determine a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

20. The computer system of claim 17, further comprising a memory system, wherein the processing circuitry is further configured to: store in the memory system, the untreated predicted myopic control progression model; retrieve the untreated predicted myopic control progression model from the memory system; and store in the memory system the generated treated predicted myopic control progression model.

21. A method of treating myopic progression in a first patient’s eye under a first myopic control intervention treatment, comprising: tracking an actual myopic progression of the eye under the first myopic control intervention treatment; and comparing the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model in claim 1.

22. The method of claim 21, further comprising: determining a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treatedpredicted myopic control progression data in the treated predicted myopic control progression model.

23. The method of claim 22, further comprising: determining whether the actual myopic progression of the eye is greater than the predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is greater than the predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as having at least one new myopic control intervention treatment for the eye.

24. The method of claim 23, comprising determining the next myopic control treatment for the eye as having a new myopic control intervention treatment for the eye different from the first myopic control intervention treatment.

25. The method of claim 24, comprising determining the next myopic control intervention treatment for the eye as having a new myopic control intervention treatment for the eye in addition to the first myopic control intervention treatment.

26. The method of claim 22, wherein: determining whether the actual myopic progression of the eye is greater than the treated predicted myopic control progression data in the treated predicted myopic control progression model; and in response to determining the actual myopic progression of the eye is less than the treated predicted myopic control progression data in the treated predicted myopic control progression model: determining the next myopic control intervention treatment for the eye as the first myopic control intervention treatment for the eye.

27. A computer system comprising processing circuitry configured to:track an actual myopic progression of the eye under the first myopic control intervention treatment; and compare the actual myopic progression of the eye under the first myopic control intervention treatment to the treated predicted myopic control progression data in the treated predicted myopic control progression model in claim 1.

28. The computer system of claim 27, wherein the processing circuitry is further configured to: determine a next myopic control intervention treatment for the eye based on the comparison of the actual myopic progression of the eye to the treated predicted myopic control progression data in the treated predicted myopic control progression model.

29. The computer system of claim 27, wherein the processing circuitry is further configured to: store in the memory system, the received actual myopic progression of the eye; and store in the memory system, a difference between the actual myopic progression of the eye under the first myopic control intervention treatment, and the treated predicted myopic control progression data in the treated predicted myopic control progression model.

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