A system and method of analyzing the refractive risk of an eye based on anatomical features

Abstract

A computer-implemented method for determining and analysing the refractive risk of an eye based on anatomical characteristics. The method includes measuring a set of anatomical features of the eye and obtaining demographic information of the patient. An anatomical equivalent refractive score (AER) is generated by inputting the measured anatomical features and demographic data into a model trained on ophthalmic phenotype population data. The method further includes calculating a refractive score for the patient's eye and comparing it with the AER to further assess refractive risk due to anatomical features.

Description

[0001] Title

[0002] A system and method of analyzing the refractive risk of an eye based on anatomical features.

[0003] Field

[0004] The present disclosure relates to a method and system for analyzing the refractive risk of an eye based on anatomical features.

[0005] Background

[0006] Myopia is one of the leading causes of visual impairment globally, primarily resulting from abnormal axial elongation of the eye beginning in childhood and progressing through young adulthood. By 2050, it is projected that nearly 5 billion people worldwide will be affected by this condition, making it a significant global health concern. According to the MRC Trial of Assessment and Management of Older People in the Community, myopic degeneration ranks as the fourth most common cause of vision impairment (defined as vision acuity of 6 / 18 or worse) among individuals over 75 in the UK, surpassing diabetic eye disease (retinopathy), which ranks fifth. While interventions now exist to slow the progression of myopia in children, no proven interventions currently prevent the age-related structural complications of myopia that can lead to vision impairment or loss in adulthood. Early and accurate detection of these complications is crucial for developing treatments that could alter the long-term course of myopia. This would also help identify those most likely to benefit from treatments that slow myopic progression in children and young adults.

[0007] Myopia impacts nearly every anatomical structure within the eye, initiating a cascade of changes that contribute to its progression and the development of associated complications. The anterior chamber depth tends to increase as the eye elongates, the elongation being a hallmark of myopia, while the lens often becomes thinner and flatter to accommodate this elongation. In later life, myopic eyes are more likely to develop lens opacities (cataracts) due to changes in lens structure. The vitreous chamber also deepens significantly, contributing to the overall increase in axial length, which is a defining characteristic of myopia.

[0008] As the eye elongates, the sclera undergoes thinning and a change in shape, which compromises its structural integrity. Choroidal thickness, another critical factor in ocular health, typically decreases in myopic eyes, and changes in the choroidal vascularity can lead to reduced blood flow and oxygen supply to the outer retina. These changes adversely affect the retinal pigment epithelium (RPE), which plays a vital role in retinal health and visual function. The retina itself may become stretched and thinner, increasing the risk for degenerative changes and retinal detachment.

[0009] Additionally, the optic nerve is subject to structural changes, such as optic disc tilting and elongation, which elevate the risk of glaucoma and non-glaucomatous optic neuropathy. These comprehensive anatomical alterations underscore the complexity of myopia and its far-reaching effects on ocular health.

[0010] There are various definitions for myopia, which have either optical or anatomical bases. A recent international white paper [Flitcroft DI, He M, Jonas JB, Jong M, Naidoo K, Ohno-Matsui K, Rahi J, Resnikoff S, Vitale S, Yannuzzi L. IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies. Invest Ophthalmol Vis Sci. 2019 Feb 28;60(3):M20-M30.] included a range of definitions for myopia. The plurality of metrics used to define the optical quality of an eye, and its refractive error are each best suited to different specific applications.

[0011] A standardised, comparable, and reproduceable method has yet to emerge to explain why an eye has a certain refractive error or to fully explain how different myopia interventions change eye growth. European Patent Publication EP4078615A2, titled ‘A SYSTEM AND METHOD OF DETERMINING AND ANALYSING THE OCULAR BIOMETRIC STATUS OF A PATIENT’ provides a method for tracking and comparing multiple growth parameters over time on a single chart, including refraction, axial length, and other biometric factors. The systems and methods disclosed therein attribute the optical refraction based on the optical properties of various ocular surfaces and the axial length.

[0012] No current approaches extend beyond the interpretation of an eye's optical function, which restricts their utility to aspects that directly affect vision, without addressing broader anatomical implications. Current approaches are additionally not capable of determining the non-optical impact of refractive errors on the anatomy and long-term health of the eye and are inadequate at assessing future risks of age-related ocular complications.

[0013] It is an object of the invention to overcome at least one of the above-referenced problems.

[0014] Summary of the Invention

[0015] The Applicant has addressed the limitations of the prior art by providing a method for accurately assessing the refractive risk of an eye based on anatomical characteristics. In one embodiment, by leveraging ophthalmic phenotype population data in the training of specialised models, the method generates an anatomical equivalent refractive score of an eye based on measurements of anatomical feature, adjusted by patient demographic information. This score indicates the expected refractive error of an eye based on anatomical features alone. This score can be compared to the actual (optical) refractive score, enabling a comprehensive refractive risk assessment.

[0016] In various embodiments, the present invention enables the determination of the non- optical anatomical changes in an eye associated with refractive errors. This further enables a comparison of the non-optical anatomical changes and an expected amount of ocular damage at a given age. This expected amount of ocular damage can be informed by population data, and optionally can involve ML or Al models for this comparison. The introduction of methods to measure, evaluate and compare an anatomical equivalent refractive score, a novel way to identify mismatches between anatomical development and refractive outcomes is provided, offering deeper insight into potential risks. Such a method is particularly useful in predictive modelling and personalised treatment, making it a powerful tool for managing and preventing refractive disorders.

[0017] An advantage of the method and system disclosed herein is that it enables the determination of the non-optical impact of a refractive error on the anatomy and ultimately the health of an eye. An important utility of the present disclosure is in assessing the future risk of ocular complications that develop with age. The methods disclosed enable the comparison of an individual eye with a wide range of eyes with both normal refraction and abnormal refraction. It also depends on eyes with normal and abnormal anatomy. It can be applied to any anatomical feature (without reference to optical function / role) to determine how normal it is and provide a metric to determine implications of that anatomical change.

[0018] In a first aspect, the invention provides a computer implemented method for analysing the refractive risk of an eye of a patient based on anatomical characteristics, comprising the steps of:

[0019] (a) measuring a set of anatomical features of the eye;

[0020] (b) obtaining demographic information of the patient; and

[0021] (c) generating an anatomical equivalent refractive score of the eye based on the results of steps (a) and (b).

[0022] In one embodiment, the anatomical equivalent refractive score indicates an expected refractive error of the eye based only on the set of anatomical features of the eye.

[0023] In one embodiment, the computer implemented method further comprises the steps of:

[0024] (d) measuring an optical refractive score of the eye; and (e) providing a relative refractive risk assessment based on the mismatch between the scores generated in step (c) and measured in step (d).

[0025] In one embodiment, the relative refractive risk assessment comprises an anatomical refractive risk score, wherein the anatomical refractive risk score comprises the difference between the anatomical equivalent refraction and the measured optical refractive score of the eye.

[0026] In one embodiment, an anatomical equivalent refraction indicating greater myopic refractive error compared to the measured optical refraction indicates greater than expected anatomical changes.

[0027] In one embodiment, the greater than expected anatomical changes indicate a higher risk of visually damaging consequences and / or associated ocular diseases.

[0028] In one embodiment, an anatomical equivalent refraction indicating lesser myopic error compared to the measured optical refraction indicates less than expected anatomical changes.

[0029] In one embodiment, the lower than expected anatomical changes indicate a lower risk of visually damaging consequences and / or associated ocular diseases.

[0030] In one embodiment, the computer implemented method further comprises the step of determining an anatomical success of a myopia control treatment or other intervention implemented for a patient.

[0031] In one embodiment, the determining an anatomical success of a myopia control treatment or other intervention comprises tracking the anatomical refractive risk score of the patient over a course of the myopia treatment or other intervention. In one embodiment, a reduction over time of the anatomical refractive risk score indicates that treatment is serving to reduce the risk of the eye developing visually damaging consequences or associated ocular diseases.

[0032] In one embodiment, the expected anatomical changes comprise one or more of: age and sex dependent alterations in the choroid, retina, retinal pigment epithelium, bruch's membrane and the optic nerve.

[0033] In one embodiment, the anatomical refractive risk score is equal to the absolute difference in dioptres or the relative difference as a percentage.

[0034] In one embodiment, the anatomical features comprise at least one of axial length, choroidal thickness, scleral thickness, anterior chamber depth, vitreous chamber depth, retinal thickness, retinal pigment epithelium, choroidal vascularity, or optic nerve head measurements such as alpha-zone, beta-zone, or gamma-zone areas.

[0035] In one embodiment, the anatomical features are obtained from measurements of ophthalmic images using one or more of standard morphometric techniques or artificial intelligence analysis.

[0036] In one embodiment, the demographic information of the patient comprises one or more of their age, sex, or race / ethnicity.

[0037] In one embodiment, step (c) comprises inputting the results of steps (a) and (b) into a model, wherein the model has been trained on ophthalmic phenotype population data.

[0038] In one embodiment the model comprises a machine learning model or an Al model.

[0039] In one embodiment, the machine learning model or Al model is configured to: be trained on ophthalmic phenotype population data ; determine the anatomical changes associated with a given refractive error and set of demographic factors; and derive an estimate of the refractive error.

[0040] In one embodiment, the ophthalmic phenotype data comprises a large sample of biometric measurements and / or ophthalmic images.

[0041] In one embodiment, the set of demographic factors comprises age.

[0042] In one embodiment the estimate of the refractive error is determined from the anatomical changes and demographic factors in a specific patient.

[0043] In one embodiment, the ophthalmic phenotype population data for each member of the population comprises: demographic information of the population member; refraction data for the population member's eyes; and one or more of the following: measurements of anatomical features of the population member's eyes; or images of the population member's eyes from which anatomical features can be derived.

[0044] In one embodiment, the refraction data is spherical equivalent refraction (SER), obtained through clinical refraction tests.

[0045] In one embodiment, the anatomical equivalent refractive score of the eye is calculated from a plurality of sub-scores, wherein there is a sub-score for each of the one or more anatomical features of the eye.

[0046] In one embodiment, the computer implemented method further comprises the steps of: (f) identifying the anatomical features most likely to cause future vision impairment based on their impact on one or more of the anatomical equivalent refractive score or the relative refractive risk assessment; and

[0047] (g) providing this information to a user to inform a decision on possible interventions.

[0048] In one embodiment, the anatomical equivalent refractive score of the eye is a weighted sum of the plurality of sub-scores.

[0049] In one embodiment, the weights are unit-sum normalised.

[0050] In one embodiment, the weights are exponential decays based on the difference between the sub-score value and the minimum value of the plurality of sub-scores.

[0051] In one embodiment, the anatomical refractive risk score is equal to the relative difference as a percentage.

[0052] In one embodiment, step (a) comprises: receiving ophthalmic images representing the anatomical features of the eye; and analysing image data in the received ophthalmic images.

[0053] In one embodiment, the image analysis comprises one or more of: deriving a choroidal volume; deriving a thickness map; quantifying a choroidal vascularity; and quantifying a retinal curvature at the retinal pigment epithelium RPE interface. In one embodiment, the results of the image analysis are utilised to calculate a prediction of risks associated with axial elongation , wherein the risks associated with axial elongation optionally comprise retinal detachment and / or myopic maculopathy.

[0054] In another aspect, the invention provides a system for analysing the refractive risk of an eye of a patient based on anatomical characteristics, comprising: an ophthalmic measurement device configured to determine one or more anatomical features of the eye; and a computer device, comprising a processor, wherein the processor is configured to execute instruction to:

[0055] (a) measure a set of anatomical features of the eye;

[0056] (b) obtain demographic information of the patient; and

[0057] (c) generate an anatomical equivalent refractive score of the eye based on the results of steps (a) and (b).

[0058] In one embodiment, the processor is further configured to execute instructions to:

[0059] (d) measuring an optical refractive score of the eye; and

[0060] (e) providing a relative refractive risk assessment based on the mismatch between the scores generated in step (c) and measured in step (d).

[0061] In one embodiment, the processor is further configured to execute instructions to:

[0062] (f) identifying the anatomical features most likely to cause future vision impairment based on their impact on one or more of the anatomical refractive risk score or the relative refractive risk assessment; and

[0063] (g) providing this information to a user to inform a decision on possible interventions.

[0064] In one embodiment, the system for analysing the refractive risk of an eye of a patient based on anatomical characteristics further comprises a display screen, wherein the processor is further configured to execute instructions to display one or more of the outputs of steps (a), (b), (c), (d), (e), (f), or (g) on the display screen. In one embodiment, the system for analysing the refractive risk of an eye of a patient based on anatomical characteristics further comprises a printing device, wherein the processor is further configured to execute instructions to print one or more of the outputs of steps (a), (b), (c), (d), (e), (f), or (g) on a physical medium, including but not limited to paper, plastic, or other printable materials.

[0065] In one embodiment, the ophthalmic measurement device is an optical biometer configured to measure one or more of the following: corneal thickness, anterior chamber depth, lens thickness, vitreous chamber depth, or overall axial length.

[0066] In one embodiment, the ophthalmic measurement device is based on Optical Coherence Tomography (OCT) and is configured to measure one or more of the following: the thickness of retinal layers, the choroid, or derive measures related to blood flow in the retina and the choroid.

[0067] In one embodiment, the ophthalmic measurement device is further configured to capture images representing the anatomical features of the eye.

[0068] In one embodiment, the processor is further configured to execute instructions to receive the captured images and analyse image data therein.

[0069] In one embodiment, the image analysis comprises deriving a choroidal volume and / or a thickness map.

[0070] In one embodiment, the analysis comprises the application of machine-learning or Al-based image analysis.

[0071] In one embodiment, the image analysis comprises quantifying a choroidal vascularity and / or a retinal curvature at the retinal pigment epithelium (RPE) interface. In one embodiment, wherein one or more of the following are utilised to calculate a prediction of an axial elongation risk: the derived choroidal volume; the derived thickness map the quantified choroidal vascularity; and the quantified retinal curvature.

[0072] In one embodiment, the ophthalmic measurement device comprises an autofluorescence imaging device configured to assess the anatomical and functional status of the retinal pigment epithelium layer.

[0073] In one embodiment, the ophthalmic measurement device comprises an adaptive optics imaging device configured to capture details down to the level of individual photoreceptors and retinal pigment epithelium (RPE) cells.

[0074] In one embodiment, the ophthalmic measurement device comprises an ocular fundal imaging camera configured to collect images for analysis using standard morphometric techniques or artificial intelligence (Al) to determine anatomical changes related to posterior segment and retinal stretch and impact on the structure of the optic nerve.

[0075] In another aspect, the invention provides a computer system comprising hardware, software and firm ware for implementing the method of any embodiment disclosed above.

[0076] Other aspects and preferred embodiments of the invention are defined and described in the other claims set out below.

[0077] Brief Description of the Fiqures

[0078] Figure 1 (prior art) shows a Refractive Mechanism Map for a patient comprising radar plots for both right and left eye. Illustrated is an example of an output on a graphical user interface which shows values of refraction, average K values, axial length and internal dioptric power (labelled as Lens / ACD, which are the dominant anatomical contributors to internal dioptric power) for a patient.

[0079] Figure 2 shows the correlation between choroidal thickness, Axial Length (AXL), and Spherical Equivalent Refraction (SER) for a sample study. This illustrates a significant but weak correlation between both axial length and refraction with central choroidal thickness.

[0080] Figure 3 shows a clinical example of a ETDRS patient choroidal thickness and the mismatch with expected value (AER). Illustrated is an example of an output on a graphical user interface of one embodiment which shows how the complex interpretation of choroidal thickness in an individual can be reduced to a single metric as the Anatomical Equivalent Refraction of -13.1 D.

[0081] Figure 4 shows an exemplary output of a system according to one embodiment, which incorporates the individual components of the calculation of AER for axial length, choroidal thickness and fundal analysis that provides an explanation of the contribution of each factor to ensure understandability of the final output value for healthcare professionals and patients.

[0082] Figure 5 shows an exemplary output of a system according to one embodiment, which incorporates the individual calculation of AER for axial length and choroidal thickness, showing the AER and the contributory axial length and choroidal values in terms of actual dimensions in mm and microns.

[0083] Detailed Description of the Invention

[0084] All publications, patents, patent applications and other references mentioned herein are hereby incorporated by reference in their entireties for all purposes as if each individual publication, patent or patent application were specifically and individually indicated to be incorporated by reference and the content thereof recited in full. Definitions and general preferences

[0085] Where used herein and unless specifically indicated otherwise, the following terms are intended to have the following meanings in addition to any broader (or narrower) meanings the terms might enjoy in the art:

[0086] Unless otherwise required by context, the use herein of the singular is to be read to include the plural and vice versa. The term "a" or "an" used in relation to an entity is to be read to refer to one or more of that entity. As such, the terms "a" (or "an"), "one or more," and "at least one" are used interchangeably herein.

[0087] As used herein, the term "comprise," or variations thereof such as "comprises" or "comprising," are to be read to indicate the inclusion of any recited integer (e.g. a feature, element, characteristic, property, method / process step or limitation) or group of integers (e.g. features, element, characteristics, properties, method / process steps or limitations) but not the exclusion of any other integer or group of integers. Thus, as used herein the term "comprising" is inclusive or open-ended and does not exclude additional, unrecited integers or method / process steps.

[0088] As used herein, the term “disease” is used to define any abnormal condition that impairs physiological function and is associated with specific symptoms. The term is used broadly to encompass any disorder, illness, abnormality, pathology, sickness, condition or syndrome in which physiological function is impaired irrespective of the nature of the aetiology (or indeed whether the aetiological basis for the disease is established). It therefore encompasses conditions arising from infection, trauma, injury, surgery, radiological ablation, age, poisoning or nutritional deficiencies.

[0089] As used herein, the term "treatment" or "treating" refers to an intervention (e.g. the administration of an agent to a subject) which cures, ameliorates or lessens the symptoms of a disease or removes (or lessens the impact of) its cause(s) (for example, the reduction in accumulation of pathological levels of lysosomal enzymes).

[0090] In this case, the term is used synonymously with the term “therapy”.

[0091] Additionally, the terms "treatment" or "treating" refers to an intervention (e.g. the administration of an agent to a subject) which prevents or delays the onset or progression of a disease or reduces (or eradicates) its incidence within a treated population. In this case, the term treatment is used synonymously with the term “prophylaxis”.

[0092] In the context of treatment and effective amounts as defined above, the term subject (which is to be read to include "individual", "animal", "patient" or "mammal" where context permits) defines any subject, particularly a mammalian subject, for whom treatment is indicated. Mammalian subjects include, but are not limited to, humans, domestic animals, farm animals, zoo animals, sport animals, pet animals such as dogs, cats, guinea pigs, rabbits, rats, mice, horses, camels, bison, cattle, cows; primates such as apes, monkeys, orangutans, and chimpanzees; canids such as dogs and wolves; felids such as cats, lions, and tigers; equids such as horses, donkeys, and zebras; food animals such as cows, pigs, and sheep; ungulates such as deer and giraffes; and rodents such as mice, rats, hamsters and guinea pigs. In preferred embodiments, the subject is a human. As used herein, the term “equine” refers to mammals of the family Equidae, which includes horses, donkeys, asses, kiang and zebra.

[0093] "Anatomical feature" refers to one or more structural characteristics of the eye, including axial length, choroidal thickness, scleral thickness, anterior chamber depth, vitreous chamber depth, retinal thickness, retinal pigment epithelium, choroidal vascularity, and optic nerve head structure. The method of the invention generally involves taking at least one measurement of at least one, and typically more than one, anatomical feature over a period of analysis. The period of analysis may coincide with a therapeutic intervention, such as corrective lens therapy or other forms of myopia management. Typically, more than two measurements are taken for each anatomical feature, with examples ranging from 3 to 7 measurements. In some embodiments, multiple anatomical features, such as axial length, choroidal thickness, and optic nerve head structure, may be measured in combination for a more comprehensive analysis.

[0094] “Refraction” refers to optical correction in dioptres required in the spectacle plane (typically 12mm from back surface of spectacle lens to the anterior surface of the cornea) that ensures rays of light entering the eye parallel to the optic axis are brought to a focus in front of the retina when ocular accommodation is relaxed. Refraction may also refer to a combination of spherocylindrical lens powers with an associated orientation axis. Refraction can also be represented as power vectors (M, JO, and J45). Refraction can also be specified as a single spherical power, the spherical equivalent refraction.

[0095] “Axial length” refers to distance in millimetres from the anterior surface of the cornea to the anterior surface of the retina if measured with ultrasound or to the level of the retinal pigment epithelium if measured with partial coherence interferometry.

[0096] "Choroidal thickness" refers to the distance between the outer surface of the retinal pigment epithelium and the inner surface of the sclera (measured in micrometres). This measurement may vary at different points along the retina and is often reported as the average thickness at specific locations, such as the subfoveal region or along various meridians.

[0097] "Scleral thickness" refers to the distance from the outer surface to the inner surface of the sclera (measured in micrometres). This thickness can vary depending on the location, typically increasing from the anterior to the posterior regions of the eye. It may be reported as a single value representing an average or measured at specific points along the sclera. "Anterior chamber depth" refers to the distance between the posterior surface of the cornea and the anterior surface of the crystalline lens (measured in millimetres). This measurement can vary depending on refractive status and age, and it is often measured along the visual axis or as an average depth across the chamber.

[0098] "Vitreous chamber depth" refers to the distance from the posterior surface of the crystalline lens to the anterior surface of the retina (measured in millimetres). This value can fluctuate based on axial elongation or refractive status and is typically measured along the visual axis.

[0099] "Retinal thickness" refers to the distance from the inner limiting membrane to the outer boundary of the retinal pigment epithelium (measured in micrometres). This value can vary across different regions of the retina, often reported at specific locations such as the macula or as an average thickness across the retina.

[0100] "Retinal pigment epithelium" refers to the layer of pigmented cells between the retina and the choroid. It plays a key role in supporting photoreceptors and maintaining the blood-retinal barrier. The thickness of this layer (measured in micrometres) may be measured in studies of ocular health and can vary depending on retinal location.

[0101] "Choroidal vascularity" refers to the proportion of the choroidal tissue occupied by blood vessels. It is typically quantified as a ratio or percentage representing the vascular area relative to the total choroidal area. This measurement is often derived from imaging techniques such as optical coherence tomography (OCT) and is reported for specific regions like the subfoveal area.

[0102] "Optic nerve head structure" refers to the anatomical features of the optic nerve head, including the neuroretinal rim, cup, and peripapillary region. The dimensions of these structures (measured in millimetres or micrometres) may be assessed in terms of their size, shape, and depth. These measurements can be influenced by factors like intraocular pressure and are often used in diagnosing and monitoring conditions such as glaucoma.

[0103] “Anatomical Equivalent Refraction (AER)” refers to the expected refraction of an eye based on one or more measured anatomical characteristics of the eye, taking into account demographic factors comprising one or more of age, sex, or ethnicity.

[0104] “Myopic progression” refers to annualised rate of change of the spherical equivalent refraction.

[0105] “Period of analysis” refers to the time period during which the plurality of measurements of the or each ocular (or health) parameter are taken. It is generally between 3 and 18 months, typically it is 6-monthly or 12-monthly but may extend due to delayed appointments. Typically, the time period between measurements is at least 3-6 months. For example, the period of analysis may be over several years in which measurements are taken every 6-12 months. In cases of unusually fast myopic progression, ocular parameter measurements may be taken more frequently, e.g. every 3 months or less. The patient may be undergoing therapy during the period of analysis (for example an ocular therapy).

[0106] "Ophthalmic phenotype population data" refers to a dataset containing age-matched measurements for a given population of subjects, incorporating both refractive measurements (such as spherical equivalent refraction, astigmatic power, astigmatic axis) and anatomical feature measurements (such as axial length, and axial length to corneal radius (ALCR) ratio, choroidal thickness, scleral thickness, anterior chamber depth, vitreous chamber depth, retinal thickness, retinal pigment epithelium thickness, choroidal vascularity, and optic nerve head structure). In some cases, the data may also include images of the eye from which anatomical features can be measured. Typically, the population data is also gender (sex) matched, and ideally matched by additional factors, such as ethnicity or geography. A range of published scientific data has been provided from a large number of population-based epidemiological studies including:

[0107] Chen, Y., Zhang, J., Morgan, I. G., & He, M. (2016). Identifying children at risk of high myopia using population centile curves of refraction. PLoS ONE, 11 (12), e0167642; Tideman, J. W. L., Polling, J. R., Vingerling, J. R., Jaddoe, V. W. V., Williams, C., Guggenheim, J. A., & Klaver, C. C. W. (2018). Axial length growth and the risk of developing myopia in European children. Acta Ophthalmologica, 96(3), 301-309;

[0108] Sanz Diez, P., Yang, L. H., Lu, M. X., Wahl, S., & Ohlendorf, A. (2019). Sanz Diez, P., Yang, L.-H., Lu, M.-X., Wahl, S., & Ohlendorf, A. (2019). Growth curves of myopia-related parameters to clinically monitor the refractive development in Chinese schoolchildren. Graefe’s Archive for Clinical and Experimental Ophthalmology, 257(5), 1045-1053. htps: / / doi.Org / 10.1007 / s00417-019-04290-6.).

[0109] Certain cross sectional health studies as the National Health and Nutrition Examination Survey (NHANES) and Korean National Health and Nutrition Examination Survey (KNHANES) have reported ocular measurements for a range of ages over a number years.

[0110] Clinical trial data can also be used, relying on baseline measurements in all participants and follow up visits in a control group to avoid interactions with any treatment. Appropriate data can also be specifically obtained for this purpose.

[0111] In addition, data sources can be used that have not been published. Such data sources include the individual patient data from published studies that provide additional information, anonymised electronic medical records from ophthalmological and optometric practices, and unpublished population studies.

[0112] “Patient demographic parameter” refers to the age, gender, ethnicity or geography of the patient. The age may be the age of the patient in years, or an age band that the patient fits into (for example 2-4 years, 5-7 years, 8-10 years etc). The gender is generally male or female. The ethnicity of the patient may be selected from an appropriate list for the target population as ethnic classifications vary from country to country. For example in the UK this is specified as part of the census process (see https: / / www.ethnicitv-facts-ficiures.service.ciov.uk / ethnic-ciroups). In the US the following list is used for census purposes: Alaska Native, American Indian, Asian, Black or African American, Hispanic or Latino, Native Hawaiian and Pacific Islander, Some Other Race, Two or More Races, White.

[0113] “Myopia control therapy” refers to any form of treatment that is designed to reduce the progression of myopia development or reduce the rate of axial elongation of the eye.

[0114] The system of the invention may comprise a determination system (to take measurements of anatomical features), a storage system (for storing measurements), and / or a comparison system (for comparing input data with population data). These functional modules can be executed on one, or multiple, computers, or by using one, or multiple, computer networks. The determination system has computer executable instructions to provide e.g., sequence information in computer readable form.

[0115] The information determined in the determination system can be read by the storage system. As used herein the “storage system” is intended to include any suitable computing or processing apparatus or other device configured or adapted for storing data or information. Examples of an electronic apparatus suitable for use with the present invention include a stand-alone computing apparatus, data telecommunications networks, including local area networks (LAN), wide area networks (WAN), Internet, Intranet, and Extranet, and local and distributed computer processing systems. Storage devices also include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage media, magnetic tape, optical storage media such as CD-ROM, DVD, electronic storage media such as RAM, ROM, EPROM, EEPROM and the like, general hard disks and hybrids of these categories such as magnetic / optical storage media. The storage system is adapted or configured for having recorded thereon growth response information and growth response fingerprint information. Such information may be provided in digital form that can be transmitted and read electronically, e.g., via the Internet, on diskette, via USB (universal serial bus) or via any other suitable mode of communication.

[0116] The storage system may have population data for ocular parameters stored thereof. As used herein, "stored" refers to a process for encoding information on the storage device. In one embodiment the population data stored in the storage device to be read by the comparison module is compared, e.g., comparison of input age and ocular measurements with population data to provide age-matched centile parameters for an ocular parameter.

[0117] The “comparison system” can use a variety of available software programs and formats for the comparison operative to compare input data with population data and generate an ocular parameter centile parameter for the patient. The comparison module may be configured using existing commercially available or freely available software, and may be optimised for particular data comparisons that are conducted. The comparison module provides computer readable information related to the genotype of the sample. Preferably, the comparison system employs a computational model for comparison purposes.

[0118] The comparison module, or any other module of the invention, may include an operating system (e.g., UNIX) on which runs a relational database management system, a World Wide Web application, and a World Wide Web server. World Wide Web application includes the executable code necessary for generation of database language statements (e.g., Structured Query Language (SQL) statements or support for web-accessible statistical analysis software such as Shiny Server that facilitates deployment of R based code (e.g. htp: / / www.rstudio.com / shiny / ). Generally, the executables will include embedded SQL statements or other database query languages. In addition, the World Wide Web application may include a configuration file which contains pointers and addresses to the various software entities that comprise the server as well as the various external and internal databases which must be accessed to service user requests. The Configuration file also directs requests for server resources to the appropriate hardware-as may be necessary should the server be distributed over two or more separate computers. In one embodiment, the World Wide Web server supports a TCP / IP protocol. Local networks such as this are sometimes referred to as "Intranets." An advantage of such Intranets is that they allow easy communication with public domain databases residing on the World Wide Web (e.g., the GenBank or Swiss Pro World Wide Web site). Thus, in a particular preferred embodiment of the present invention, users can directly access data (via Hypertext links for example) residing on Internet databases using a HTML interface provided by Web browsers and Web servers. The comparison system is ideally implemented as a computer based API (application programming interface). This allows for a single cloud based comparison system to receive input data from a wide range sources. These include electronic health record systems, a web interface or web / network enabled ocular measurement devices.

[0119] The web-based offering of specific embodiments may also provide for end-user customisable implementations (so-called white label services), whereby an end user can provide their own company or medical or optometric practice branding.

[0120] Web-based solutions may also be used to provide this invention as a function within web-enabled biometric measurement devices (e.g. devices that measure one or more relevant biometric parameter such as refraction, axial length, lens thickness, corneal radius, vitreous chamber depth (VCD), lens power and ALCR ratio). Such devices may communicate ocular biometric parameters, as measured by such a device, to a remote server for analysis and receive back data including the calculated centiles or graphical data with which data in the specified refractogram format can be presented to the user of the device. In an alternative embodiment, the serverbased functionality can be embodied within a stand-alone biometric measurement device. The comparison module typically provides a computer readable comparison result that can be processed in computer readable form by predefined criteria, or criteria defined by a user, to provide a content based in part on the comparison result that may be stored and output as requested by a user using a display system.

[0121] In one embodiment of the invention, the outputs are displayed on a computer monitor. In one embodiment of the invention, the outputs are displayed through printable media. The display module can be any suitable device configured to receive from a computer and display computer readable information to a user. Non-limiting examples include, for example, general-purpose computers such as those based on Intel PENTIUM-type processor, Motorola PowerPC, Sun UltraSPARC, Hewlett- Packard PA-RISC processors, any of a variety of processors available from Advanced Micro Devices (AMD) of Sunnyvale, California, or any other type of processor, visual display devices such as flat panel displays, cathode ray tubes and the like, as well as computer printers of various types.

[0122] In one embodiment, a World Wide Web browser is used for providing a user interface for display of the content based on the comparison result. It should be understood that other modules of the invention can be adapted to have a web browser interface. Through the Web browser, a user may construct requests for retrieving data from the comparison module. Thus, the user will typically point and click to user interface elements such as buttons, pull down menus, scroll bars and the like conventionally employed in graphical user interfaces.

[0123] An international white paper [Flitcroft DI, He M, Jonas JB, Jong M, Naidoo K, Ohno- Matsui K, Rahi J, Resnikoff S, Vitale S, Yannuzzi L. IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies. Invest Ophthalmol Vis Sci. 2019 Feb 28;60(3):M20-M30.] included a range of definitions for myopia. This white paper included definitions related to the optical basis of myopia as a refractive error: Myopia: A refractive error in which rays of light entering the eye parallel to the optic axis are brought to a focus in front of the retina when ocular accommodation is relaxed. This usually results from the eyeball being too long from front to back, but may also result from an overly curved cornea and / or a lens with increased optical power. It also is called near-sightedness.

[0124] Axial Myopia: A myopic refractive state primarily resulting from a greater than normal axial length.

[0125] Refractive Myopia: A myopic refractive state that can be attributed to changes in the structure or location of the image forming structures of the eye, i.e. the cornea and lens.

[0126] Secondary Myopia: A myopic refractive state for which a single, specific cause (e.g., drug, corneal disease and / or systemic clinical syndrome) can be identified that is not a recognized population risk factor for myopia development.

[0127] A definition for the anatomical complications of myopia was also included: Pathologic Myopia: Excessive axial elongation associated with myopia that leads to structural changes in the posterior segment of the eye (including posterior staphyloma, myopic maculopathy, and high myopia-associated optic neuropathy) and that can lead to loss of best- corrected visual acuity.

[0128] This anatomical definition encompasses a range of potential anatomical complications of myopia but does not include all the potential anatomical changes and the elements contributing to this are mostly qualitative, not quantitative. In addition, there is no unifying metric to be able to quantitatively compare the different anatomical effects on the same comparison scale. For the optical definition of myopia there are many different metrics used to define the optical quality of an eye and its refractive error (see Table 1). Each metric is best suited for a specific application. The most common two are the sphero-cylindrical prescription, comprising sphere, cylinder and axis, used to make corrective lenses and the spherical equivalent refraction (SER, calculated as sphere + 0.5 x cylinder power). Sphere, cylinder and SER are typically defined in units of Dioptres (D). Common accepted definitions used for various applications are illustrated in Table 1.

[0129] Table 1

[0130] Now with the availability of treatments to alter myopic progression, a need has arisen for definitions that address different questions as show in Table 2. A standard method has yet to emerge to explain why an eye has a certain refractive error (Q1) or to fully explain the myopia intervention changes eye growth (Q 2). European Patent Publication EP4078615A2, titled ‘A SYSTEM AND METHOD OF DETERMINING AND ANALYSING THE OCULAR BIOMETRIC STATUS OF A PATIENT’ included the concept of the Refractive Mechanism map (Figure 1) that addresses these two questions. That invention defines the individual contributions in dioptres of axial length, corneal power, and the lens (combing the power and position as a single measure of Internal Dioptric Power, IDP). It shows the contribution of each component to the overall refractive error compared to age / gender matched emmetropes. The direction of triangle shows dominant refractive contribution. A precise “mechanism axis” marked by white pointer in outer colour-coded axis based on the centroid of the triangle and can classify the refractive error of an eye as axial, corneal, lenticular or combined.

[0131] Table 2

[0132] Another pressing clinical need is for a unified metric to define the impact of myopic and other refractive errors on the anatomical structure of the eye, as a clinical tool to assess risk, monitor treatment response and clinical prognosis. These issues relate to Q3 of Table 2 and are addressed by the present invention. At least the technical problem described above is solved, namely, how to create quantitative metric or index that can quantitatively compare the complex and diverse anatomical effects of refractive errors on the same comparison scale. This invention has a range of applications including risk profiling eyes to assess the appropriate level of interventions, monitoring / assessing the impact of interventions for myopia control, monitoring / assessing interventions for prevention of vision loss in established myopia. As noted above, there no proven interventions currently prevent the age- related structural complications of myopia that can lead to vision impairment, so this latter application is particularly important. Clinical trials are essential to evaluate the benefits of new treatments. Clinical trials need a well-defined, quantifiable primary endpoint.

[0133] While measures such as axial length are often used as a measure of the anatomical changes associated with refractive errors and the two factors are correlated and linearly related, the statistical association explains less than half the variability. This means that eye of the same optical refraction can have very different axial lengths. For other important anatomical measurements, such as choroidal thickness, the correlation with optical measures such as Spherical Equivalent Refraction (SER) is even weaker at < 15%. Therefore, trying to infer or estimate such anatomical changes from the optical refraction (SER) is very inaccurate. This is important clinically, as the visual impairment associated with myopic maculopathy is more strongly associated with anatomical changes such as choroidal thickness than refraction or axial length [Liu R, Xuan M, Wang DC, Xiao O, Guo XX, Zhang J, Wang W, Jong M, Sankaridurg P, Ohno-Matsui K, Yin QX, He MG, Li ZX. Using choroidal thickness to detect myopic macular degeneration. Int J Ophthalmol. 2024 Feb 18;17(2):317-323].

[0134] Exemplification

[0135] This invention creates an entirely non-optical definition of refractive error but uses the universally recognised dioptric scale as the unifying metric. It can include all the known anatomical consequences of refractive errors and creates a metric called the Anatomical Equivalent Refraction (AER): the expected refraction based on one or more measured anatomical characteristics of the eye, taking into account demographic factors comprising one or more of age, sex, or ethnicity.

[0136] Although there are no optical factors included in the definition it can be expressed in terms of dioptres due to the methods used to calculate the metric. This provides clinicians with an easily understandable way of evaluating the anatomical impact of a given refraction on the eye and explaining this to patients.

[0137] To help indicate the complex interactions and dependencies between various anatomical features and the refraction of an eye, data from a recent study undertaken by the applicant will be shown. The choroid has long been identified as an important structure in the development of myopia. Choroidal thinning is a better predictive marker for myopic maculopathy than axial length or refraction. Central macular choroidal thickness < 300 pm is also associated with reduced best corrected vision. This points to the need for choroidal thickness to be used in risk assessment and treatment monitoring for myopia management. Choroidal thickness measurements from multiple clinical studies were combined in this analysis, including:

[0138] • Myopia Outcome Study of Myopia in Children (MOSAIC); and

[0139] • T reatment Optimization of Atropine Study (TOAST)

[0140] Data from 1732 eyes were analysed with Age range 6 -30 years (median 15.4), Spherical Equivalent Refraction range -11 .5 to + 8 D (median -2.3D)

[0141] Multiple linear regression and Machine Learning approaches were applied to create prediction models for choroidal thickness for the 9 ETDRS (Early Treatment of Diabetic Retinopathy Study) macular Zones. A reverse model allowed estimation of the “Anatomical Equivalent Refraction (AER)” based on sub-foveal choroidal thickness.

[0142] The results of this are shown in Figure 2. There is a significant but weak correlation (r2 <= 0.15, explaining only 15% of the variability) between both axial length and refraction with central choroidal thickness. This indicates that most of the biological variability is not explained by either of these two variables alone. This indicates the need for more detailed modelling. In this sample study, an optimized Random Forest Al model including age, sex, ser and axial length explains 62% of the variance, allowing useful and novel metrics to be generated based on the expected choroidal thickness. As an example, this invention can demonstrate that the eye of a 9-year- old boy, with a minimal refractive error of -0.75 D already has the anatomical changes that would be expected at this age in an extremely high myope (as determined by a refractive measurement such as SER) of -13 D. This points to the need for aggressive treatment, provides a single metric to monitor that treatment and indicates a poor visual prognosis if the anatomical changes cannot be ameliorated. The anatomical success of a myopia control treatment or other intervention can therefore be determined by improvements in previously described anatomical refractive risk score. In this case, a reducing anatomical refractive risk score over time would indicate that treatment is serving to reduce the initially increased risk of a given eye developing later complications. An example of one embodiment of this invention is shown in Figure 3. The complex interpretation of anatomical characteristics (including choroidal thickness) in this individual can be reduced to a single metric as the Anatomical Equivalent Refraction of -13.1 D, providing an accurate indicator of the anatomical state of the eye and an indicator of the urgency of intervention. The measured values, and their deviations shown in this figure are:

[0143] • oSUP: Outer Superior (upper outer quadrant)

[0144] • oNAS: Outer Nasal (inner side towards the nose)

[0145] • oTEM: Outer Temporal (outer side towards the temple)

[0146] • oINF: Outer Inferior (lower outer quadrant)

[0147] • cCT: Central Corneal Thickness (central thickness of the outer layer)

[0148] • iSUP: Inner Superior (upper inner quadrant)

[0149] • iNAS: Inner Nasal (inner side towards the nose)

[0150] • iTEM: Inner Temporal (inner side towards the temple)

[0151] • iINF: Inner Inferior (lower inner quadrant)

[0152] Implementation

[0153] In one embodiment, the technical implementation of this invention comprises a range of devices or a single multifunctional device capable of capturing measurements or images representing the anatomical state of the eye. Many such devices are well known and are in regular clinical use in the field of ophthalmology and optometry. These include optical biometers capable of measuring corneal thickness, anterior chamber depth, lens thickness, vitreous chamber depth, overall axial length. Devices based on the principle of Ocular Coherence Tomography (OCT) can measure thickness of the retinal layers, the choroid and derive measures related to blood flow in the retina and the choroid. Imaging techniques such as Autofluorescence can assess the anatomical and functional status of the retinal pigment epithelium layer. Adaptive optics imaging can capture detail down to the level of individual photoreceptors and RPE cells, which are both influenced by myopic progression. Ocular fundal imaging cameras can be used to collect images which can be analysed by standard morphometric techniques or using artificial intelligence (Al) to determine a wide variety of anatomical changes related to posterior segment and retinal stretch and impact on the structure of the optic nerve.

[0154] In addition to measurements generated by the OCT system’s native software, the acquired imaging data may serve as an input forfurther computational analysis. Such post-processing can include derivation of choroidal volume or thickness maps, application of machine-learning or Al-based image analysis to quantify choroidal vascularity, and assessment of retinal curvature at the retinal pigment epithelium (RPE) interface. These additional analyses provide parameters not routinely reported by conventional OCT devices but which may offer predictive value in models employed to calculate axial elongation risk.

[0155] The development of artificial intelligence (Al) in ophthalmology has seen significant milestones, particularly in the diagnosis and management of various eye diseases. One of the earliest breakthroughs was the successful application of Al in the automated detection of diabetic retinopathy, which led to the first FDA-approved Al device for eye care, marking a critical step toward integrating Al into clinical practice. Following this, Al systems have been developed to identify and monitor other retinal diseases, including age-related macular degeneration and glaucoma, with increasing accuracy. In the context of myopia, Al has made strides in automated refraction detection from retinal images, and more recently, research has focused on utilizing Al to detect myopic structural complications, such as myopic maculopathy. These advancements are particularly relevant as they hold the potential to improve early detection and monitoring of myopia-related complications, paving the way for personalized treatment strategies that could significantly alter the natural progression of myopia and reduce the risk of vision impairment in later life.

[0156] Automated systems have been developed to classify refraction from fundal images [Varadarajan AV, Poplin R, Blumer K, Angermueller C, Ledsam J, Chopra R, Keane PA, Corrado GS, Peng L, Webster DR. Deep Learning for Predicting Refractive Error From Retinal Fundus Images. Invest Ophthalmol Vis Sci. 2018 Jun 1 ;59(7):2861- 2868], Since the actual refraction can be more easily assessed directly, this invention deploys such Al image analysis techniques in a different way, i.e. to determine what anatomical features are different to those expected in an eye of a given refraction. When applied to this invention, such a model would be optimised to detect the retinal changes that are best predictors of the mismatch between the actual refraction and the estimated refraction from the fundal image. When implementing this aspect of the current invention the following steps are an example of those that need to be followed to optimise such as process:

[0157] • Select an appropriate large dataset of high-quality retinal (fundal) images from diverse populations, ensuring representation across different ages, ethnicities, and refractive errors.

[0158] • Obtain the corresponding actual refraction data (Spherical Equivalent Refraction, SER) obtained through clinical refraction tests and other potential covariates such as age, sex and ethnicity.

[0159] • Generate initial estimates of refraction using traditional image analysis techniques or best available machine learning models.

[0160] • Calculate the mismatch for each image, defined as the difference between the actual refraction (SER) and the estimated refraction from the initial model or analysis. This mismatch serves as the target variable that the Al model will predict.

[0161] • Use advanced image processing techniques to extract features from the fundal images. These features might include structural details of the retina, optic nerve head, blood vessel patterns, and choroidal thickness. Alternatively, use convolutional neural networks (CNNs) to automatically extract features directly from the raw fundal images, identifying patterns and structures that correlate with refractive error.

[0162] • Validate predictions against actual to ensure no residual bias or trend errors remain Such an Al model can help to identify the specific retinal changes that lead to discrepancies in refraction estimates, these changes represent the anatomical manifestation of refractive error and hence will be most closely correlated with the anatomical equivalent refraction. This not only improves the accuracy of refraction predictions but also enhances the understanding of how various retinal features contribute to visual acuity and refractive errors.

[0163] One or more anatomical measurements are required to estimate the anatomical equivalent refraction (AER) of an eye. The algorithms to calculate AER may be embedded within one of the devices described above or equivalent devices. Alternatively, the measurements may be exported from a single device to another computer-based platform for the calculation, or measurements may be collected from multiple devices into an electronic health record management system.

[0164] The algorithms to calculate AER, require datasets that include refraction and the required anatomical measurements from eyes of a range of ages and refractive errors from both males and females. This data is easily available from clinical trials baseline measurements and data from untreated controls groups. Such data should ideally also be collected from multiple populations. Multifactorial models are created from such data using standard machine learning regression techniques such Generalized Linear Models or non-linear techniques such as Random Forest or Gradient Boosting.

[0165] These models define the relationship between each anatomical parameter and the primary demographic characteristics such as refraction, age and sex. Additional parameters such as ethnicity, height or ocular measurements relating to eye size such as axial length and corneal diameter may contribute for certain parameters. This can be usefully applied to data relating to a single anatomical feature, especially when such a feature is strongly associated with subsequent visual loss as is the case with choroidal thickness (shown in Figure 2). A more comprehensive risk assessment of the eye is achievable with the addition of other factors that can impact on visual function, with more precise indications provided by modelling more features. Their statistical relevance is easily validated from such models by anyone skilled in the art of such analysis.

[0166] For a linear model the following equation describes one example of such an algorithm:

[0167] Equation 1

[0168] SER = (30 + (31(Axial Length) + (32(Anterior Chamber Depth)

[0169] + (33(Vitreous Chamber Depth) + (33(Scleral Thickness)

[0170] + (36(Ch.oroidal Thickness) + (37 (Choroidal Vascularity)

[0171] + (38(Retinal Pigment Epithelium) + (39(Retinal Thickness)

[0172] + (310(Optic Nerve to foveal distance) + ylt lge) + y2 Sex)

[0173] + y3(Ethnicity) + y4(Height) + Sl(Corneal Diameter)

[0174] + S2(Additional Ocular Measurements)

[0175] Wherein:

[0176] - SER: Spherical Equivalent Refraction, the dependent variable we aim to predict. In this case it is a modelled value, not the actually measured SER.

[0177] - / ?0: Intercept term, representing the baseline SER when all other factors are zero.

[0178] - / ?1 , (32, ... / ?10: Coefficients representing the relationship between SER and various anatomical parameters like axial length, anterior chamber depth, lens thickness, etc.

[0179] - y1 , y2, ... y4: Coefficients for demographic factors such as age, sex, ethnicity, and height, where sex and ethnicity are categorical variables, height and age are continuous variables.

[0180] - 51 , 52: Coefficients for additional ocular measurements such as corneal diameter and any other relevant measurements.

[0181] This model allows calculation of the expected spherical equivalent for a given eye based solely on demographic and anatomical measurements; this value represents the Anatomical Equivalent Refraction. Refraction is a variable used to create the model along with the other variables from the source datasets, but when applied to a given patient it is not dependent on any optical parameters at all. It is therefore an entirely non-optical metric but is mathematically expressed in the units of dioptres.

[0182] When incorporating an Al trained model that estimated refraction from a fundal image or other images, Equation 1 can be modified to include additional terms such as:

[0183] + 91(AI — Estimated Refraction from Retinal Image)

[0184] Where this term represents the output from an Al model based on analysis of a retinal image. As this value will be in the units of dioptres the coefficient 01 shows how much importance the Al-estimated refraction has in predicting the overall SER.

[0185] This equation represents a linear model, which is easily expressed in mathematical terms. More sophisticated non-linear and machine learning models cannot be simply expressed in this manner, as they often involve complex interactions and dependencies between variables. Instead, the algorithms are encapsulated within computational frameworks that utilize advanced techniques such as neural networks, decision trees, or ensemble methods like random forests and gradient boosting. These models are typically implemented within specialized software or machine learning libraries, making them accessible for practical applications while maintaining their sophisticated, non-linear nature.

[0186] An important element in Al applications within medicine is the explainability and understandability of an output for both the healthcare professional and patient. This invention can provide a calculation of the mismatch between the actual refraction and the anatomical equivalent refraction for each of the inputted anatomical measurements, even if the analysis of a given parameter used advanced, ‘black-box’ Al techniques. The mismatch between the SER and AER can be calculated in terms of dioptres. The mismatch can also be calculated in the units of each measurement (microns in the case of choroidal thickness) by calculating the expected choroidal thickness for the patient’s refraction and other demographic factors. To achieve this, sub-models are created isolating each component and the difference between each contribution. The calculation of the mismatch is partially designed to improve explainability for professionals and parents. For example, this allows a professional to provide explanations for interventions, such as: “although your daughter’s prescription isn’t very high at the moment at -2, the impact on the eye is -4, twice as high, making treatment all the more urgent”

[0187] Such models can initially be trained on a large sample of ophthalmic phenotype population data, including biometric measurements and / or ophthalmic images for example, to determine the anatomical changes associated with a given refractive error and set of demographic factors including age. The model is then used to derive an estimate of the refractive error from the anatomical changes and demographic factors in a specific patient.

[0188] A refractive risk assessment can then be made, termed a relative refractive risk assessment. The relative refractive risk assessment can include an anatomical refractive risk score, wherein the anatomical refractive risk score comprises the difference between the anatomical equivalent refraction and the measured optical refractive score of the eye. If the anatomical equivalent refraction is more myopic than a patient’s optical refraction, then that indicates greater than expected anatomical changes and hence higher risk. If the anatomical equivalent refraction is less myopic than a patient’s optical refraction, then that indicates less than expected anatomical changes and hence lower risk.

[0189] For example, if choroidal thickness, axial length and a fundal image are used (as might be a typical example) a model is generated that relates each parameter and the individual AER calculated for that variable. Here is the linear model example for looking at the isolated contribution of choroidal thickness (CT), where the model parameters are generated from the reference dataset using SER, choroidal thickness, age, sex.

[0190] Equation 2 Choroidal_AER = / ?0 + / ?1(CT) + yl^ge) + y2(Se%)

[0191] The contribution of the choroid to the overall AER can then simply calculated as: Choroidal_contribution = Choroidal_AER — SER_actual

[0192] , where: SER_actual is the measured spherical equivalent refraction, and Choroidal_AER is the refraction predicted by the Al model based on anatomical and demographic data.

[0193] For example, if the SER is -3 D and the Choroidal_AER is -4.5 D, this can be interpreted as meaning that on the basis of the choroid alone, this eye has the choroid of an eye that is -1 .5 D more myopic than it is and is more like an eye that has a 50% higher level of myopia. In simple terms this can be explained as being analogous to the difference between chronological age and biological age, e.g. telling a 40-year- old patient that they have the heart of a 60-year-old.

[0194] Equation 3 below expresses the mismatch between the observed anatomical measurement (e.g., choroidal thickness, CT) and the expected value calculated by the model for the given refraction and demographics. This allows interpretation of the contribution in terms of units in which a parameter is conventionally measured (microns in this case).

[0195] Equation 3

[0196] Mismatch_CT = CT_actual — CT_expected(SER, age, sex)

[0197] , where: CT_actual is the observed choroidal thickness and CT_expected SER, age, sex) is the choroidal thickness predicted by the model based on the patient’s refraction (SER), age, sex, and possibly other factors.

[0198] These equations provide a clear way to quantify the difference between actual and predicted values, making the output of the Al model understandable to both healthcare professionals and patients. In particular, it can indicate which factor or factors were most dominant in the final AER result. In the case of image analysis, where the image mismatch information is not easily interpreted by humans, the AER mismatch can still provide that information avoiding the ‘black-box’ issue while using advanced techniques that are otherwise hindered by this problem.

[0199] One value of AER is in relation to visual prognosis. The relationship between refractive error and visual impairment has been defined in large population studies [Tideman JW, Snabel MC, Tedja MS, van Rijn GA, Wong KT, Kuijpers RW, Vingerling JR, Hofman A, Buitendijk GH, Keunen JE, Boon CJ, Geerards AJ, Luyten GP, Verhoeven VJ, Klaver CC. Association of Axial Length With Risk of Uncorrectable Visual Impairment for Europeans With Myopia. JAMA Ophthalmol. 2016 Dec 1 ; 134(12): 1355-1363]. Visual impairment is a functional consequence of the anatomical changes that result from the combination of aging and the abnormal eye growth that leads to myopia. For a given refraction and age, there will be a population anatomically better than expected eyes and a population with anatomically worse than expected eyes. AER is expected to provide a more realistic estimate of that risk as the risk of ocular complications, as referenced above, has been demonstrated to be more closely related to anatomical factors than refraction.

[0200] An alternative approach can be taken to calculating overall AER for a given patient where multiple different anatomical parameters are available. As described above, an AER value can be calculated for each anatomical parameter using a parameter specific model. When considering the negative consequences of myopic eye growth, a simple average of the resulting AER values may be misleading as visual impairment will reflect the most severe anatomical changes. Conversely, there is not a simple one to one relationship between a given anatomical parameter and such visual impairment. Some factors such as choroidal thickness and fundal features suggestive myopic maculopathy are more strongly associated with visual impairment than axial length. To combine multiple estimates of AER from different parameters, it is therefore advantageous to weight each parameter according to the strength of its linkage with future visual impairment (as defined by the best available studies). In addition, it may be advantageous to emphasise the highest risk factors which will have the most negative values. One clinical benefit of deriving estimates of AER from different anatomical components within an eye, is that it can help to identify which complications are most likely and direct a clinician to perform other tests and develop an appropriate follow up plan. For example an elevated risk derived from images of the optic nerve or derived metrics from such images would point to the increased risk of glaucoma, one of the well-defined conditions related to myopia. This finding can be used to prompt a clinician to obtain appropriate baseline investigations such visual fields.

[0201] This can be achieved with the following example algorithm that takes three input variables: axl (for axial length AER), cht (for choroidal AER), and fundus (for fundal AER). More variables or measurements can be included e.g. choroidal thickness at various locations rather than just sub-foveal, retinal thickness, measures of retinal stretch derived from the fundal image or fovea to optic disc distance, the three most likely one of axl, cht, and fundus are used in this example. Each of these inputs is assigned a pre-specified weight that determines its prognostic value in relation to future vision impairment. The pre-specified weights can be determined or derived from studies which report the predictive value of the different factors for visual impairment, such studies can be cross sectional or longitudinal in nature. The algorithm then finds the most negative value among the inputs and calculates an exponential weighting for each input based on its distance from this most negative value. The pre-specified weights are then multiplied by these exponential weights, and the resulting weights are normalized so that their sum is 1. Finally, the function calculates a weighted average of the inputs using these normalized weights.

[0202] A more detailed description of an example of such an algorithm follows. Define prespecified visual impairment importance weights:

[0203] • axl_weight-. Pre-specified weight for axial length (AER)

[0204] • cht_weight-. Pre-specified weight for choroidal thickness (AER)

[0205] • fundus_weighf. Pre-specified weight for fundal AER Identify the Most Negative Input Value:

[0206] • Determine the most negative value among the three inputs:

[0207] • min_value = min(axl, cht, fundus)

[0208] Calculate Exponential Weighting Based on Distance from the Most Negative Value:

[0209] Compute the exponential weights for each input based on the distance from the most negative value (such that the result goes from 0 in the minus direction so the exponential produces a value of 1 or less), an additional scaling constant (“scaling”) could also be added to adjust the relative weighting of the factors:

[0210] • exp_weight_axl = exp((axl — min _value) / scaling)

[0211] • exp_weight_cht = exp((cht — min _value) / scaling)

[0212] • exp _weight _fundus = exp ((fundus — min _value) / scaling)

[0213] Multiply each pre-specified weight by its corresponding exponential weight:

[0214] • weighted_axl = axl_weight * exp_weight_axl

[0215] • weighted_cht = cht_weight * exp_weight_cht

[0216] • weighted_fundus = fundus_weight * exp_weight_fundus

[0217] Normalize the Weights so that their Sum Equals 1 :

[0218] • total_weight = weighted_axl + weighted_cht + weighted_fundus

[0219] • normalized_weight_axl = weighted_axl / total_weight

[0220] • normalized_weight_cht = weighted_cht / total_weight

[0221] • normalized-Weight-fundus = weighted_fundus / total_weight

[0222] Finally, calculate the weighted average of the inputs using the normalized weights:

[0223] • AER_final = (normalized_weight_axl * ax I) + (normalized_weight_cht * cht) + (normalized_weight_fundus * fundus)

[0224] This final value, AER_final, represents the combined prognostic value of the inputs, taking into account both their relative importance (pre-specified weights) and their relative positions (exponential weights based on distance from the most negative value).

[0225] An implementation of this algorithm in a computerized analysis platform that could be incorporated into a fundus camera or multifunctional device could provide analysis and outputs as is shown in Figure 4. This implementation incorporates the exponential weighting function described above. It also incorporates the individual calculation of AER for axial length, choroidal thickness and fundal analysis that provides the benefit of explaining the contribution of each factor to ensure understandability of the final output value for healthcare professionals and patients.

[0226] Another implementation of this algorithm in a computerized analysis platform that could be incorporated into a fundus camera or multifunctional device could provide analysis and outputs as is shown in Figure 5. This shows a calculation of AER using the axial length and choroidal thickness as the anatomical inputs and shows the contributory values in mm and microns for the axial length and microns respectively.

[0227] In clinical practice, use of a method in accordance with an embodiment would be useful in a multitude of scenarios. For example, the clinician measures the refraction of the patient, which can be expressed as SER in dioptres. The mismatch is then the difference between the actual refraction and the AER. So, an eye with an SER refraction of -3 D and an AER of -6.5 has a mismatch (AER-SER) of -3.5D meaning that the anatomical changes in that eye are just over twice as significant as would be expected from the refraction alone. This is expressed in dioptres which is a familiar metric to clinicians. The mismatch could also be described as a ratio or percentage 117% in this case, indicating anatomical impact is 117% higher than expected. If the SER is -3 and the AER is -2.25, the mismatch value is +0.75 and anatomical changes would 25% lower than expected. In a further example, If the choroidal thickness is higher than expected then that patient would be at lower risk. So, for example, if they are responding reasonably well to treatment but with some myopic progression, a clinician may deem it appropriate to stay on this treatment rather than add in another treatment that perhaps has more side effects (such as adding atropine drops on top of myopia control glasses or contact lenses). Conversely a low myope who might be considered low risk but who has a thin choroid, may be offered maximal therapy to limit their progression and optimise their long-term outlook. The AER in such situations helps clinicians to make a better risk-benefit analysis of different treatments.

[0228] Equivalents

[0229] The foregoing description details presently preferred embodiments of the present invention. Numerous modifications and variations in practice thereof are expected to occur to those skilled in the art upon consideration of these descriptions. Those modifications and variations are intended to be encompassed within the claims appended hereto.

Claims

1. CLAIMS:

1. A computer implemented method for analysing a refractive risk of an eye of a patient based on anatomical characteristics, comprising the steps of:(a) measuring a set of anatomical features of the eye;(b) obtaining demographic information of the patient; and(c) generating an anatomical equivalent refractive score of the eye based on results of steps (a) and (b).

2. The computer implemented method as claimed in claim 1 , further comprising the steps of:(d) measuring an optical refractive score of the eye; and(e) providing a relative refractive risk assessment based on a mismatch between scores generated in step (c) and measured in step (d).

3. The computer implemented method as claimed in claim 2, wherein the relative refractive risk assessment comprises an anatomical refractive risk score, wherein the anatomical refractive risk score comprises a difference between the anatomical equivalent refraction and the measured optical refractive score of the eye.

4. The computer implemented method as claimed in claim 2 or claim 3, wherein: an anatomical equivalent refraction indicating greater myopic refractive error compared to the measured optical refraction indicates greater than expected anatomical changes; and an anatomical equivalent refraction indicating lesser myopic refractive error compared to the measured optical refraction indicates less than expected anatomical changes.

5. The computer implemented method as claimed in claim 4, wherein the indication of greater than expected anatomical changes indicates a higher risk of visually damaging consequences and / or associated ocular diseases, and wherein theindication of less than expected anatomical changes indicates a lower risk of visually damaging consequences and / or associated ocular diseases.

6. The computer implemented method as claimed in any of claims 2 to 5, further comprising the step of determining an anatomical success of a myopia control treatment or other intervention implemented for a patient, wherein the determining an anatomical success of a myopia control treatment or other intervention comprises tracking the provided relative refractive risk assessment of the patient over a course of the myopia control treatment or other intervention.

7. The computer implemented step as claimed in claim 6, wherein the relative refractive risk assessment comprises an anatomical refractive risk score, and wherein a reduction overtime of the anatomical refractive risk score indicates that treatment is serving to reduce the risk of the eye developing visually damaging consequences or associated ocular diseases.

8. The computer implemented method as claimed in claim 7, wherein the expected anatomical changes comprise one or more of: age and sex dependent alterations in a choroid, retina, retinal pigment epithelium, bruch's membrane, and a optic nerve.

9. The computer implemented method as claimed in any of claims 3 to 8, wherein the anatomical refractive risk score is equal to an absolute difference in dioptres or a relative difference as a percentage.

10. The computer implemented method as claimed in any preceding claim, wherein the anatomical features comprise at least one of axial length, choroidal thickness, scleral thickness, anterior chamber depth, vitreous chamber depth, retinal thickness, retinal pigment epithelium, choroidal vascularity, or optic nerve head measurements such as alpha-zone, beta-zone, or gamma-zone areas.

11. The computer implemented method as claimed in any of the preceding claims, wherein the anatomical features are obtained from measurements of ophthalmic images using one or more of standard morphometric techniques or artificial intelligence analysis.

12. The computer implemented method as claimed in any preceding claim, wherein the demographic information of the patient comprises one or more of their age, sex, or race / ethnicity.

13. The computer implemented method as claimed in any previous claim, wherein step (c) comprises inputting the results of steps (a) and (b) into a model, wherein the model has been trained on ophthalmic phenotype population data.

14. The computer implemented method as claimed in claim 13, wherein the model comprises a machine learning model or an Al model.

15. The computer implemented method as claimed in claim 13 or claim 14, wherein the model is configured to: determine the anatomical changes associated with a given refractive error and set of demographic factors; and derive an estimate of the refractive error from the anatomical changes and demographic factors in a specific patient.

16. The computer-implemented method as claimed in any of claims 13 to 15, wherein the ophthalmic phenotype population data for each member of the population comprises: demographic information of the population member; refraction data for the population member's eyes; and one or more of the following: measurements of anatomical features of the population member's eyes; orimages of the population member's eyes from which anatomical features can be derived.

17. The computer implemented method as claimed in claim 16, wherein the refraction data is spherical equivalent refraction SER, obtained through clinical refraction tests.

18. The computer implemented method as claimed in any preceding claim, wherein the anatomical equivalent refractive score of the eye is comprises a plurality of sub-scores, wherein each of the plurality of sub-scores relates to one of the one or more anatomical features of the eye.

19. The computer implemented method as claimed in claim 18 when dependant on claim 2, further comprising the steps of:(f) identifying the anatomical features most likely to cause future vision impairment based on their impact the anatomical equivalent refractive score and / or the relative refractive risk assessment; and(g) providing this information to a user to inform a decision related to myopia control treatment or other intervention.

20. The computer implemented method as claimed in claim 18 or claim 19, wherein the anatomical equivalent refractive score of the eye is a weighted sum of the plurality of sub-scores, and wherein: the weights are unit-sum normalised; and / or the weights are exponential decays based on a difference between a subscore value and a minimum value of the plurality of sub-scores.21 .The method as claimed in any previous claim, wherein step (a) comprises: receiving ophthalmic images representing the anatomical features of the eye; and analysing image data in the received ophthalmic images.

22. The method as claimed in claim 21 , wherein the image analysis comprises one or more of: deriving a choroidal volume; deriving a thickness map; quantifying a choroidal vascularity; and quantifying a retinal curvature at the retinal pigment epithelium RPE interface.

23. The method as claimed in claim 22, wherein the results of the image analysis are utilised to calculate a prediction of risks associated with axial elongation, wherein the risks associated with axial elongation optionally comprise retinal detachment and / or myopic maculopathy.

24. A system for analysing the refractive risk of an eye of a patient based on anatomical characteristics, comprising: an ophthalmic measurement device configured to capture data representing one or more anatomical features of the eye; and a computer device, comprising a processor, wherein the processor is configured to execute instruction to:(a) measure a set of anatomical features of the eye in the captured data;(b) obtain demographic information of the patient;(c) generate an anatomical equivalent refractive score of the eye based on the results of steps (a) and (b);(d) measure an optical refractive score of the eye; and(e) provide a refractive risk assessment based on a mismatch between scores generated in step (c) and measured in step (d).

25. The system as claimed in claim 24, wherein the processor is further configured to execute instructions to:(f) identifying the anatomical features most likely to cause future vision impairment based on their impact on the anatomical refractive risk score and / or the relative refractive risk assessment; and(g) providing this information to a user to inform a decision on possible interventions.

26. The system as claimed in claim 24 or 25, wherein: the data representing one or more anatomical features of the eye comprise images; the processor is further configured to execute instructions to receive the captured images and analyse image data in the received images, wherein the image data analysis comprises one or more of: deriving a choroidal volume of the eye; deriving a thickness map of the eye; quantifying a choroidal vascularity of the eye; and quantifying a retinal curvature at a retinal pigment epithelium RPE interface of the eye.

27. The system as claimed in any of claims 24 to 26, wherein the ophthalmic measurement device is an optical biometer configured to measure one or more of the following: corneal thickness, anterior chamber depth, lens thickness, vitreous chamber depth, or overall axial length.

28. The system as claimed in any of claims 24 to 26, wherein the ophthalmic measurement device is based on Optical Coherence Tomography (OCT) and is configured to measure one or more of the following: a thickness of retinal layers, a choroid, or derive measures related to blood flow in a retina and a choroid.

29. The system as claimed in any of claims 24 to 26, wherein the ophthalmic measurement device comprises one or more of the following:an autofluorescence imaging device configured to assess the anatomical and functional status of a retinal pigment epithelium layer; an adaptive optics imaging device configured to capture details down to a level of individual photoreceptors and retinal pigment epithelium (RPE) cells; and an ocular fundal imaging camera configured to collect images for analysis using standard morphometric techniques or artificial intelligence (Al) to determine anatomical changes related to posterior segment and retinal stretch and impact on a structure of an optic nerve.

30. A computer system comprising hardware, software and firm ware for implementing the method as claimed in any of claims 1 to 23.

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