Intraocular lenses that improve post-surgical spectacle independent and methods of manufacturing thereof

Abstract

A Bayesian model for predicting spectacle independence of one or more IOLs based on pre-clinical data (e.g., visual acuity value for one or more defocus values) of an IOL. The Bayesian model is trained to assign appropriate weights for different combinations of defocus values.

Description

BACKGROUND OF THE INVENTION

Field of the Invention

This application is related to systems and methods of selecting, designing and manufacturing intraocular lenses that improve post-surgical spectacle independence in cataract patients.

Description of the Related Art

Retinal image quality of intraocular lenses when implanted in the eye of a patient can be estimated from different metrics obtained from pre-clinical measurements. For example, through focus visual acuity for psuedophakic patients can be predicted from metrics based on various pre-clinical measurements. However, many of the metrics used to predict post-surgical optical performance of intraocular lenses are unable to reliably predict spectacle independence for psuedophakic patients. Spectacle independence is a desired outcome following cataract surgery for most patients. Accordingly, it would be desirable to develop new techniques to reliably predict the spectacle independence for pseudophakic patients receiving different IOLs.

SUMMARY OF THE INVENTION

This application contemplates systems and methods of predicting spectacle independence utilizing pre-clinical data to simulate and predict the expected percentage of patients that will be spectacle independent following cataract surgery with an IOL in which pre-clinical (measured or simulated) data is available. One piece of pre-clinical data that can be used to predict spectacle independence is through-focus visual acuity at one or more defocus positions. For example, it may be possible to predict spectacle independence based on through-focus visual acuity at near distances, such as, for example, near distances greater than or equal to about 25 cm and less than or equal to about 50 cm. However, there is limited information on whether a high peak in through-focus visual acuity at one near distance (e.g., 40 cm) is a better predictor of spectacle independence or whether a flat but lower through-focus visual acuity at a plurality of near distance values between about 25 cm and about 50 cm is a better predictor of spectacle independence. The methods and systems contemplated in this application are based on applying Bayesian models to an initial data set including known spectacle independence information obtained from clinical studies gathering responses to questions in a questionnaire from different patients implanted with different intraocular lenses for which measured or simulated pre-clinical data is available and calculating the probability that a patient would be spectacle independent for a certain value of visual acuity at a certain defocus distance. Since the initial data set is based on a small number of patients (e.g., less than or equal to about 500, less than or equal to about 1000, or less than or equal to about 2000), the prediction of spectacle independence from through-focus visual acuity values at one or more defocus positions can be calculated using “medium data” solutions that operate on medium sized databases. Additionally, machine learning can be employed to appropriately weight and scale the contribution of through-focus visual acuity performance at various defocus distances to spectacle independence.

One innovative aspect of the subject matter disclosed herein is implemented in an optical system configured to select an intraocular lens (IOL) from a plurality of IOLs for manufacture or for implantation into a patient eye, the selected IOL configured or to be manufactured to improve post-surgical spectacle independence outcome for the patient. The optical system comprises a processor configured to execute programmable instructions stored in a non-transitory computer storage medium; and a population database comprising clinical data for a plurality of patients less than or equal to about 5000 implanted with one of the plurality of IOLs, the clinical data comprising information related to spectacle independence for a plurality of values of visual acuity between about −0.2 log MAR and about 1 log MAR at various defocus conditions between about −5 D and 0 D, wherein the information related to spectacle independence is based on responses of the patients implanted with one of the plurality of IOLs to a questionnaire. The processor is configured to calculate for each of the plurality of IOLs, a probability of being spectacle independent for visual acuity equal to a threshold value between about −0.2 log MAR and about 1 log MAR at at least one defocus conditions between about −5 D and 0 D based on the information related to spectacle independence obtained from the population database; and identify one of the plurality of IOLs having a higher probability of being spectacle independent for manufacture or for implantation into the patient's eye.

The processor can be further configured to calculate for each of the plurality of IOLs, a probability of being spectacle independent for visual acuity equal to a threshold value between about −0.2 log MAR and about 1 log MAR at at least two or more defocus conditions between about −5 D and 0 D. The processor can be further configured to assign a weight to the probability of being spectacle independent for visual acuity equal to a threshold value between about −0.2 log MAR and about 1 log MAR at at least two or more defocus conditions between about −5 D and 0 D. The processor can be configured to execute a machine learning algorithm to determine the weight.

Another innovative aspect of the subject matter disclosed herein can be embodied in an optical system configured to identify an intraocular lens (IOL) that will improve post-surgical spectacle independence. The system comprises a processor configured to execute programmable instructions stored in a non-transitory computer storage medium to calculate a probability of achieving spectacle independence for at least two IOLs based on clinical data providing visual acuity at a first defocus position for the at least two IOLs in a population of patients implanted with one of the at least two IOLs; and identify one of the at least two IOLs having higher probability of achieving spectacle independence.

The processor can be further configured to calculate the probability of achieving spectacle independence for at least two IOLs based on at least one of: clinical data providing visual acuity at a second defocus position for the at least two IOLs in the population; standard deviation of pre-clinical visual acuity for the at least two IOLs at the first or the second defocus positions; clinical data providing minimum readable print size in mm in the population; modulation transfer function (MTF) at one or more frequencies at different distances for different pupil sizes; or area under the modulation transfer function at one or more frequencies at different distances for different pupil sizes.

The first defocus position can be about −2.5 D corresponding to a distance of about 40 cm. The second defocus position can have a value between about −5 D and about −0.5 D. A size of the population can be less than about 1000. The processor can be configured to execute programmable instructions stored in a non-transitory computer storage medium to transmit one or more parameters of the identified IOL to a display device or an IOL manufacturing system.

Yet another innovative aspect of the subject matter disclosed herein is implemented in a system for predicting post-surgical spectacle independence of one or more IOLs, the system comprising a processor configured to execute programmable instructions stored in a non-transitory computer storage medium to calculate a probability of achieving spectacle independence for the one or more IOLs based on clinical data providing visual acuity at one or more defocus position for the one or more IOLs in a population of patients implanted with one of the one or more IOLs.

The processor can be further configured to execute programmable instructions stored in a non-transitory computer storage medium to calculate the probability of achieving spectacle independent based on one or more combinations of visual acuity at at least two or more defocus positions for the one or more IOLs in the population. The processor can be further configured to execute programmable instructions stored in a non-transitory computer storage medium to assign weights to the one or more combinations of visual acuity. The weights can be determined based on a machine learning algorithm.

An innovative aspect of the subject matter disclosed herein is implemented in a method of manufacturing an IOL comprising: receiving one or more parameters of an IOL selected from a plurality of IOLs based on calculating a probability of achieving spectacle independence for the plurality of IOLs from clinical data providing visual acuity at one or more defocus position for the plurality of IOLs in a population of patients implanted with one of the plurality of IOLs; and manufacturing the selected IOL.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention may be better understood from the following detailed description when read in conjunction with the accompanying drawings. Such embodiments, which are for illustrative purposes only, depict novel and non-obvious aspects of the invention. The drawings include the following figures:

FIG. 1 is a flowchart illustrating a method of predicting values using Bayesian analysis.

FIG. 2 shows an example method of calculating probability of an event using Bayesian statistics.

FIG. 3A is data from clinical studies for 162 pseudophakic patients that are spectacle independent. FIG. 3B is data from clinical studies for 159 pseudophakic patients that are not spectacle independent.

FIG. 4 is a graph showing a predicted percentage of patients who are spectacle independent obtained using a simple model based on near distance visual acuity.

FIG. 5 illustrates the predicted spectacle independence based on pre-clinical data for different implementations of intraocular lenses.

FIG. 6 is a graphical representation of the elements of computing system for designing or selecting an ophthalmic lens.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present invention provided that the features included in such a combination are not mutually inconsistent.

As used herein, the terms “about” or “approximately”, when used in reference to a Diopter value of an optical power, mean within plus or minus 0.25 Diopter of the referenced optical power(s). As used herein, the terms “about” or “approximately”, when used in reference to a percentage (%), mean within plus or minus one percent (±1%). As used herein, the terms “about” or “approximately”, when used in reference to a linear dimension (e.g., length, width, thickness, distance, etc.) mean within plus or minus one percent (1%) of the value of the referenced linear dimension.

Spectacle independence is a highly desired outcome following cataract surgery. It is possible to predict through-focus visual acuity (VA) for different implementations of intraocular lenses (IOLs) based on pre-clinical data using mathematical models. Through-focus VA can be predicted for one or more defocus values based on available pre-clinical data for an IOL including but not limited to IOL characteristics such as refractive index of the IOL, radii of curvature, diffraction power, diffraction step height, transition zones and IOL thickness. These characteristics can be used in a ray tracing simulation software to predict through-focus MTF, which can predict through-focus VA. IOL designs can be optimized to achieve a desired optical performance based on the predicted values of through-focus VA.

The predicted through-focus VA at one or more defocus values can be based on an output generated by an electronic processor from available pre-clinical data of the IOL input to the electronic processor. The electronic processor can be configured to execute instructions stored on a non-transitory hardware storage medium to generate the output. An example electronic processing system is discussed in detail below with reference to FIG. 6. The through-focus VA at one or more defocus values for an IOL can be measured using a Log MAR chart that can comprise rows of letters. The through-focus VA of an implementation of an IOL is 0 Log MAR if the implementation of the IOL can resolve details as small as 1 minute of visual angle. A series of negative powered lenses can be placed in front of the IOL to simulate near distance vision. In this manner through-focus VA at a plurality of defocus values can be measured to obtain a defocus curve. Without any loss of generality, a defocus value of −2.5 Diopters can correspond to a near distance value of about 40 cm. Defocus values less than −2.5 Diopters correspond to near distance values less than about 40 cm.

This application contemplates systems and methods to predict the expected percentage of patients that will be spectacle independent based when implanted with an IOL whose pre-clinical data is available. The spectacle independence can be estimated using Bayesian analysis. Bayesian analysis is a statistical procedure which combines prior distribution of one or more population parameters before any data is observed with observed information in a sample to obtain an updated probability distribution for the one or more parameters. FIG. 1 illustrates an implementation of the Bayesian analysis. As shown in FIG. 1, the Bayesian analysis begins with a starting model 101. The starting model can be a prior probability density function (pdf) of different hypotheses associated with certain probabilities of being true. New data is collected from a sample of the population as shown in block 103. The new data can be conditional on the different hypotheses. The pdf of the different hypotheses is updated based on the prior pdf and the new data using Bayes' rule shown in block 102. Mathematical Bayes' rule is given by the equation

$P\left(A❘B\right)=\frac{P\left(B❘A\right)*P\left(A\right)}{P\left(B\right)}$

When using Bayes analysis to estimate spectacle independence from pre-clinical data, A can correspond to the pdf of different percentages of spectacle independence, and B can correspond to the clinical data that is used to predict spectacle independence.

The clinical data can be a singular value or a multidimensional value. Through-focus VA is an example of a multidimensional value (for example, VA at −3 D, at −2.5 D, at −2 D, . . . , at 0 D of defocus). A singular value can also be used to predict the percentage of spectacle independence. Predicting spectacle independence based on a singular value can be simple and computationally less intensive. Singular values used for predicting spectacle independence can include (i) VA at near distance (e.g., 40 cm), (ii) VA at any other distance, (iii) standard deviation of VA in a certain distance/defocus range which can be a measure of the variability/consistency of VA in the distance/defocus range, (iv) minimum readable print size in mm calculated by predicted angular VA which is converted to stroke width of letters in mm at that distance and taking the minimum value. This corresponds to best distance at which a patient can view small print, (v) modulation transfer function (MTF) at certain spatial frequencies at certain distances and pupil sizes, or (vi) Area under MTF curve at certain distances and pupil sizes.

For singular value metrics B, A can comprise a plurality of probabilities i of spectacle independence, such as 1%, 2%, 3%, . . . , 99%, 100%. The conditional probability of P(A_i|B) can be calculated using Bayes' rule by the equation

$P\left(\mathrm{A\_i}❘B\right)=\frac{P\left(B❘\mathrm{A\_i}\right)*P\left(\mathrm{A\_i}\right)}{P\left(B\right)}$

The plurality of probabilities of different percentages of spectacle independence P(A_i) can be determined based on a prior model of spectacle independence, such as having a linear function between 5% and 95%, in the VA range from 0.6 Log MAR to 0 Log MAR at a certain defocus value (e.g., −2.5 D). Bayes analysis can then be used to estimate the probability P(B|A_i), the probability of the set given clinical data assuming spectacle independence A_i through direct calculation from the clinical data as well as the model. P(B) can be considered as a normalization factor.

The method discussed above can be applied for multidimensional values as well. However, some modification and additional techniques may be required when the multidimensional value metric is through-focus VA at different defocus values, since VA at different defocus values may be correlated. Due to the relatively large number of defocus positions, correcting for interaction effects may not possible. In some implementations of Bayesian analysis that employs through-focus VA at different defocus values as the pre-clinical metric, a multidimensional matrix including all possible combinations of VA values at different defocus values may be generated. This matrix can be sampled, for example, in steps of 0.5 D and 0.1 Log MAR. At each such combination there is a pdf for different percentages of spectacle independence. The data added into the matrix could be additive for any VA at any value higher than the given curve for, thus phrasing the probabilities as “having VA of x or higher”.

This method is illustrated in FIG. 2 which has a sample of 20 subjects. Five of the 20 subjects are spectacle independent as shown in block 201 and the remaining fifteen wear spectacles as shown in block 202. Thus, the probability of spectacle independence P(SI) is equal to 5/20 or 25%. Of the five subjects who are spectacle independent, three have a visual acuity greater than 0.1 as shown in block 203 while two have a visual acuity less than 0.1 as shown in block 204. Thus, the conditional probability of having visual acuity greater than 0.1 when being spectacle independent P(VA>0.1|SI) is equal to 3/5 or 60%. Four of the 20 subjects have visual acuity greater than 0.1 as shown in block 205 while sixteen of the 20 subjects have visual acuity less than 0.1 as shown in block 206. Thus, the probability that visual acuity is greater than 0.1 P(VA>0.1) is equal to 4/20 or 20%. Three subjects having visual acuity greater than 0.1 are spectacle independent as shown in block 207 while 1 subject having visual acuity greater than 0.1 is not spectacle independent as shown in block 208. Thus, the probability of being spectacle independent given visual acuity is greater than 0.1 P(SI|VA>0.1) is equal to 3/4 or 75%. The probability of being spectacle independent given visual acuity is greater than 0.1 P(SI|VA>0.1) can also be calculated using Bayes' rule as

$P\left(\mathrm{SI}❘\mathrm{VA}>0.1\right)=\frac{P\left(\mathrm{VA}>0.1❘\mathrm{SI}\right)*P\left(\mathrm{SI}\right)}{P\left(\mathrm{VA}>0.1\right)}$ which is equal to 75%.

Another example to illustrate the method of determining spectacle independence based on visual acuity is described below. For the sake of simplicity a singular value metric is used to estimate spectacle independence, but the same techniques can be generalized when a multidimensional value is used.

Consider that it is desired to investigate the probability of being spectacle independent if VA at −2.5 D is −0.05 Log MAR. From a clinical data set obtained from observation of 321 subjects, it is found that there are four subjects who are not spectacle independent and have VA at −2.5 D greater than or equal to −0.05 and there are 16 subjects who are spectacle independent and have VA at −2.5 D greater than or equal to −0.05. From the clinical data set, it is further observed that there are 155 subjects who are not spectacle independent and have VA at −2.5 D less than −0.05 and 146 subjects who are spectacle independent and have VA at −2.5 D less than −0.05. FIG. 3A shows the defocus curve for spectacle independent subjects and FIG. 3B shows the defocus curve for subjects who wear spectacles.

Based on the information, the probability of being spectacle independent when VA at −2.5 D is greater than or equal to −0.05 P(SI|VA at −2.5 D>−0.05)=P(VA at −2.5 D>−0.05|SI)*P(SI)/P(VA at −2.5 D>−0.05) which is equal to (16/162)*(162/321)/(20/321) which is equal to 80%.

FIG. 4 shows the percentage of spectacle independence for different values of near distance VA obtained using a singular value as described above. It is noted from FIG. 4 that using only near distance VA as a predictor for spectacle independence has an 80% of achieving spectacle independence, regardless of the value of near distance VA of the lens.

It is further noted from FIG. 4 that if VA at all distances is low then the probability of being spectacle independent is about 50%. However, in the clinical data set, there were 102 subjects who are not spectacle independent and have a VA of 0.3 Log MAR or worse, 56 subjects who are not spectacle independent and have VA better than 0.3 Log MAR, 13 subjects who are spectacle independent VA of 0.3 Log MAR or worse, and 144 subjects who are spectacle independent and have VA better than 0.3 Log MAR.

Using Bayes analysis, the probability of being spectacle dependent and having a VA of 0.3 Log MAR or worse P(SD|VA of 0.3 Log MAR or worse)=P(VA of 0.3 Log MAR or worse|SD)*P(SD)/P(VA of 0.3 Log MAR or worse) which is equal to (102/158)*(158/315)/(115/315)=88.7%. Thus, there is an 11.3% chance of being spectacle independent if the VA is 0.3 Log MAR or worse. Thus, the model of predicting spectacle independence based on singular VA value can be updated by combining the two estimates to better predict the chance of being spectacle independent given a certain VA value as described below.

Consider a vector t of length 100 with the probabilities of having 1%, 2%, 3% . . . , 99%, 100% spectacle independent at a certain value of VA. The vector t can be updated according to the example below.

Consider that the vector t has a length of 2 with a 0.5 probability of 80% being spectacle independent and 0.5 probability of 70% being spectacle independent. For VA values above −0.05 we have 4 subjects who wear spectacles and 16 subjects who don't wear spectacles. The probability of being spectacle independent for VA above −0.05, can be calculated using the P(x)=(N!/(x! (N−x)!))*(fx)*(l−t){circumflex over ( )}(N−x) where N is total number, x is the number of spectacle independent and t is the probability. For the example above, N=20, x=16 and t=0.7 and 0.8. Accordingly, P(x) is equal to 0.13 for t=0.7 and 0.21 for t=0.8. If the initial prior pdf P(A) is [0.5, 0.5], and P(B) is applied as a standard normalization factor, P(A|B)=[0.5*0.13, 0.5*0.21]/(0.5*0.13+0.5*0.21)=0.38 for t=0.7, and 0.62 for t=0.8. In this manner the vector t is updated. A similar technique can be applied for estimating spectacle independence for VA worse than a certain value, and the results combined using a range of methods. A skilled person would understand that it is advantageous to start with a reasonable prior pdf as the posterior probability distribution can skew towards the prior pdf when the number of subjects is low.

The abovementioned technique can also be applied to the multidimensional case, where a larger matrix is used and a combination of VA applicable to all defocus positions is selected. In such a case, the sampling may be limited. The sampling limitation can be overcome by using a two-step process, wherein first a coarse sampling is applied, e.g. steps of 0.1 Log MAR. Thereafter, if the VA of interest to test is e.g. 0.12, we combine the two nearby steps, with 80% weight to estimates for VA=0.1 and 20% weight to the estimate with VA=0.2.

The Bayesian analysis method can be expanded to cover more than a binary outcome of spectacle dependent/spectacle independent, and instead describe the probability of never wearing spectacles, of wearing spectacles a little bit of the time, some of the time, or all of the time.

The Bayesian analysis method can be expanded to incorporate other characteristics of the patients, such as age, gender, eye length, pupil size, ethnicity, corneal aberrations, life style or combinations thereof.

The Bayesian analysis method of estimating spectacle independence for different parameters can be incorporated in an IOL design and/or manufacturing process. The parameter space of IOL design allows variation of IOL characteristics such as radii of curvature, diffraction power, diffraction step height, transition zones and IOL thickness. These characteristics can be used in a ray tracing simulation software to predict through focus MTF, which can predict VA. Using Bayesian analysis, the probability of spectacle independence can be calculated, and the IOL characteristics optimized such that the highest possible spectacle independence is achieved, in conjunction with other simulated and desired constraints such as distance image quality. Bayesian analysis can also be used to predict how suitable certain treatment techniques, such as making the patients slightly myopic postoperatively can positively affect spectacle independence. Bayesian analysis to estimate spectacle independence can also be used to select an IOL for implantation in a patient that would increase the chance of the patient to be spectacle independent for a variety of tasks such as reading, viewing a smartphone, computer use or combinations thereof.

The spectacle independence of five different implementations of IOLs was predicted based on pre-clinical data based on the Bayesian analysis method described above. To predict spectacle independence, a data set of 321 patients from three different studies was used. The patients were bilaterally implanted with five different implementations of IOLs. Spectacle independence was coded as a binary outcome. Through focus VA was varied in steps of 0.5 D between −3 D and 0 D. A Bayesian model to estimate rate of spectacle independence was developed. The Bayesian model was configured to calculate probability of spectacle independence for VA better than a certain value as well as probability of spectacle dependence for VA worse than a certain value. The Bayesian model was further configured to calculate probability with different combinations of VA at different defocus values. For example, the Bayesian model was configured to (i) calculate probability of VA greater than or worse than a certain value for different single defocus values (e.g., 0 D, −0.5 D, −1 D, −1.5 D, −2 D, −2.5 D, −3 D), (ii) calculate probability of VA greater than or worse than a certain value for combinations of two different defocus values (e.g., −3 D and −2.5 D, −2 D and −1 D), and (iii) calculate probability of VA greater than or worse than a certain value for combinations of three or more different defocus values. For example, the model was configured to calculate probability of VA greater than or worse than a certain value for combination of seven different defocus values (e.g., 0 D, −0.5 D, −1 D, −1.5 D, −2 D, −2.5 D, and −3 D).

The model was trained to combine and weight the different probabilities in order to have outcomes closest to the reported rates of spectacle independence. For example, probability of VA greater than or worse than a certain value for combination of two or more different defocus values that are closer to each other was assigned a higher weight than probability of VA greater than or worse than a certain value for combination of two or more different defocus values that are farther from each other. As another example, probability of VA greater than or worse than a certain value for different defocus values corresponding to near distances between 25 cm and about 40 cm can be assigned a higher weight than VA at other defocus values.

The table below shows the clinically measured percentage spectacle independence for five different IOL implementations Lens 1, Lens 2, Lens 3, Lens 4 and Lens 5. The predicted percentage of spectacle independence using a Bayesian model with multidimensional values as described herein as well as a single through-focus VA at one defocus value is also shown in the table below. The average error and the r{circumflex over ( )}2 values for the different Bayesian models are also included in the table below.

	Lens 1	Lens 2	Lens 3	Lens 4	Lens 5	Error	r{circumflex over ( )}2
Clinical	93%	76%	66%	62%	1%
Bayesian	95%	70%	74%	51%	2%	5%	0.96
Model with
VA at a
plurality of
defocus
values
Bayesian	87%	71%	59%	36%	15%	12%	0.84
Model with
VA at
defocus value
of −3D only
Bayesian	71%	73%	66%	38%	13%	12%	0.83
Model with
VA at
defocus
value −2.5D only
Bayesian	37%	63%	69%	50%	12%	19%	0.45
Model with
VA at
defocus
value −2D only
Bayesian	36%	44%	60%	59%	34%	26%	0.07
Model with
VA at
defocus
value −1.5D only

It is noted from the table above that the Bayesian model based on through-focus VA at a plurality of defocus values as described herein had the highest degree of correlation (r{circumflex over ( )}2 of 0.96 with the clinically measured spectacle independence.

The benefit of inducing 0.5 D of myopia for mini-monovision can also be evaluated using the through focus VA predicted from pre-clinical methods. FIG. 5 shows the through-focus VA based on pre-clinical data for an implementation of an IOL (curve 502) and the same curved shifted by 0.5 D (curve 501). Using the Bayesian model discussed herein, it was estimated that an extended range of vision IOL with a spectacle independence rate of 62% could have that rate increased to 83.2% if the patients were made 0.5 D myopic.

Referring to FIG. 6, in certain embodiments, a computer system 600 for estimating the probability of being spectacle independent based on available or measured pre-clinical data for an IOL comprises an electronic processor 602 and a computer readable memory 604 coupled to the processor 602. The computer readable memory 604 has stored therein an array of ordered values 608 and sequences of instructions 610 which, when executed by the processor 602, cause the processor 602 to perform certain functions or execute certain modules. For example, a module can be executed that is configured to calculate spectacle independence for one or more IOLs. As another example, a module can be executed that is configured to perform the Bayesian analysis discussed herein and select an IOL that has the highest probability of being spectacle independent. As another example, a module can be executed that is configured to determine an improved or optimal IOL design that improves the probability of being spectacle independent.

The array of ordered values 608 may comprise, for example, one or more ocular dimensions of an eye or plurality of eyes from a database, a desired refractive outcome, parameters of an eye model based on one or more characteristics of at least one eye, and data related to an IOL or set of IOLs such as a power, clinical data providing the number of subjects who are spectacle dependent at one or more VA values, and/or clinical data providing the number of subjects who are spectacle independent at one or more VA values. In some embodiments, the sequence of instructions 610 includes variation of IOL characteristics such as radii of curvature, diffraction power, diffraction step height, transition zones and IOL thickness, using these characteristics in a ray tracing simulation software to predict through-focus VA, using Bayesian analysis to predict the probability of spectacle independence, optimize IOL characteristics to increase spectacle independence or select an IOL having the highest probability of spectacle independence.

The computer system 600 may be a general purpose desktop or laptop computer or may comprise hardware specifically configured performing the desired calculations. In some embodiments, the computer system 600 is configured to be electronically coupled to another device such as a phacoemulsification console or one or more instruments for obtaining measurements of an eye or a plurality of eyes. In other embodiments, the computer system 600 is a handheld device that may be adapted to be electronically coupled to one of the devices just listed. In yet other embodiments, the computer system 600 is, or is part of, refractive planner configured to provide one or more suitable intraocular lenses for implantation based on physical, structural, and/or geometric characteristics of an eye, and based on other characteristics of a patient or patient history, such as the age of a patient, medical history, history of ocular procedures, life preferences, and the like.

In certain embodiments, the system 600 includes or is part a phacoemulsification system, laser treatment system, optical diagnostic instrument (e.g, autorefractor, aberrometer, and/or corneal topographer, or the like). For example, the computer readable memory 604 may additionally contain instructions for controlling the handpiece of a phacoemulsification system or similar surgical system. Additionally or alternatively, the computer readable memory 604 may additionally contain instructions for controlling or exchanging data with an autorefractor, aberrometer, tomographer, and/or topographer, or the like.

Rates of spectacle independence can be predicted from through focus VA. It is better to use a combination of VA at many distances than any one distance. Models based on combining clinical data from many studies can offer greater understanding of potential patient outcomes, such as predicting benefits from mini-monovision using EDOF IOLs.

The above presents a description of the best mode contemplated of carrying out the concepts disclosed herein, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains to make and use the concepts described herein. The systems, methods and devices disclosed herein are, however, susceptible to modifications and alternate constructions from that discussed above which are fully equivalent. Consequently, it is not the intention to limit the scope of this disclosure to the particular embodiments disclosed. On the contrary, the intention is to cover modifications and alternate constructions coming within the spirit and scope of the present disclosure as generally expressed by the following claims, which particularly point out and distinctly claim the subject matter of the implementations described herein.

Although embodiments have been described and pictured in an example form with a certain degree of particularity, it should be understood that the present disclosure has been made by way of example, and that numerous changes in the details of construction and combination and arrangement of parts and steps may be made without departing from the spirit and scope of the disclosure as set forth in the claims hereinafter.

As used herein, the term “processor” refers broadly to any suitable device, logical block, module, circuit, or combination of elements for executing instructions. For example, the processor 1002 can include any conventional general purpose single- or multi-chip microprocessor such as a Pentium® processor, a MIPS® processor, a Power PC® processor, AMD® processor, ARM processor, or an ALPHA® processor. In addition, the processor 602 can include any conventional special purpose microprocessor such as a digital signal processor. The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Processor 302 can be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

Computer readable memory 604 can refer to electronic circuitry that allows information, typically computer or digital data, to be stored and retrieved. Computer readable memory 604 can refer to external devices or systems, for example, disk drives or solid state drives. Computer readable memory 1004 can also refer to fast semiconductor storage (chips), for example, Random Access Memory (RAM) or various forms of Read Only Memory (ROM), which are directly connected to the communication bus or the processor 602. Other types of memory include bubble memory and core memory. Computer readable memory 604 can be physical hardware configured to store information in a non-transitory medium.

Methods and processes described herein may be embodied in, and partially or fully automated via, software code modules executed by one or more general and/or special purpose computers. The word “module” can refer to logic embodied in hardware and/or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamically linked library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software instructions may be embedded in firmware, such as an erasable programmable read-only memory (EPROM). It will be further appreciated that hardware modules may comprise connected logic units, such as gates and flip-flops, and/or may comprised programmable units, such as programmable gate arrays, application specific integrated circuits, and/or processors. The modules described herein can be implemented as software modules, but also may be represented in hardware and/or firmware. Moreover, although in some embodiments a module may be separately compiled, in other embodiments a module may represent a subset of instructions of a separately compiled program, and may not have an interface available to other logical program units.

In certain embodiments, code modules may be implemented and/or stored in any type of computer-readable medium or other computer storage device. In some systems, data (and/or metadata) input to the system, data generated by the system, and/or data used by the system can be stored in any type of computer data repository, such as a relational database and/or flat file system. Any of the systems, methods, and processes described herein may include an interface configured to permit interaction with users, operators, other systems, components, programs, and so forth.

Claims

1. An optical system configured to identify an intraocular lens (IOL) that will improve post-surgical spectacle independence, the system comprising a processor configured to execute programmable instructions stored in a non-transitory computer storage medium to: calculate, using a ray tracing simulation, predicted pre-clinical visual acuity at a first defocus position for at least two IOLs based on pre-clinical data comprising at least one parameter of an eye model and at least one IOL characteristic of the at least two IOLs, the at least one IOL characteristic selected from the group consisting of refractive index, radii of curvature, diffraction power, diffraction step height, transition zones, and IOL thickness;calculate, using Bayesian analysis, a probability of achieving spectacle independence for the at least two IOLs based on the predicted pre-clinical visual acuity and clinical data providing visual acuity at the first defocus position for the at least two IOLs in a population of patients implanted with one of the at least two IOLs; andidentify one of the at least two IOLs having a higher probability of achieving spectacle independence, the one of the at least two IOLs comprising the at least one IOL characteristic.

2. The optical system of claim 1, wherein the processor is further configured to calculate the probability of achieving spectacle independence for at least two IOLs based on at least one of: clinical data providing visual acuity at a second defocus position for the at least two IOLs in the population;standard deviation of pre-clinical visual acuity for the at least two IOLs at the first or the second defocus positions;clinical data providing minimum readable print size in mm in the population;modulation transfer function (MTF) at one or more frequencies at different distances for different pupil sizes; orarea under the modulation transfer function at one or more frequencies at different distances for different pupil sizes.

3. The optical system of claim 1 or claim 2, wherein the first defocus position is about −2.5 D corresponding to a distance of about 40 cm, or wherein the second defocus position has a value between about −5 D and about −0.5 D.

4. The optical system of any of claims 1-3, wherein a size of the population is less than about 1000.

5. The optical system of claim 1, wherein the processor is configured to execute programmable instructions stored in a non-transitory computer storage medium to transmit one or more parameters of the identified IOL to a display device or an IOL manufacturing system.

6. A system for predicting post-surgical spectacle independence of one or more IOLs, the system comprising a processor configured to execute programmable instructions stored in a non-transitory computer storage medium to: calculate, using a ray tracing simulation, predicted pre-clinical visual acuity at one or more defocus positions for the one or more IOLs based on pre-clinical data comprising at least one parameter of an eye model and at least one IOL characteristic of the one or more IOLs, the at least one IOL characteristic selected from the group consisting of refractive index, radii of curvature, diffraction power, diffraction step height, transition zones, and IOL thickness; andcalculate, using Bayesian analysis, a probability of achieving spectacle independence for the one or more IOLs based on the predicted pre-clinical visual acuity and clinical data providing visual acuity at the one or more defocus positions for the one or more IOLs in a population of patients implanted with one of the one or more IOLs.

7. The system of claim 6, wherein the processor is further configured to execute programmable instructions stored in a non-transitory computer storage medium to calculate the probability of achieving spectacle independent based on one or more combinations of visual acuity at at least two or more defocus positions for the one or more IOLs in the population.

8. The system of claim 7, wherein the processor is further configured to execute programmable instructions stored in a non-transitory computer storage medium to assign weights to the one or more combinations of visual acuity.

9. The system of claim 8, wherein the weights are determined based on a machine learning algorithm.