Characterization of vitreous floaters using aberrometry

Abstract

Certain embodiments include receiving, by a computing device (500), one or more wavefront elevation maps of a patient's eye and identifying one or more attributes of the one or more wavefront elevation maps corresponding to vitreous floaters. The one or more attributes may include local spatial variation of the one or more wavefront elevation maps, temporal variation between multiple wavefront elevation maps, and depth information indicative of light scattering from within the vitreous of the patient's eye. A machine learning model (518) may be trained and utilized to characterize vitreous floaters based on the one or more wavefront elevation maps and other patient data. The wavefront elevation maps may be measured using an aberrometer. The aberrometer (102) may be integrated with a LIDAR system (202) to estimate the depth of the scattered light. A common laser light source may be used for both the aberrometer (102) and the LIDAR system (202).

Description

[Technical Field]

[0001] The present invention relates to the characterization of vitreous floaters using aberrometry. [Background technology]

[0002] The back of the eye, between the lens and the retina, is filled with a clear gel known as the vitreous. Floaters can be present in the vitreous due to a variety of causes. Floaters are typically formed from clumps of cells and can vary in size and opacity. Every eye will have some floaters. However, the size, number, and opacity of floaters in the eye can become such that vision is significantly impaired. In such cases, treatment may involve removing the vitreous and replacing it with saline or bubbles made of gas or oil. Summary of the Invention [Means for solving the problem]

[0003] The present disclosure generally relates to a system for characterizing vitreous floaters using aberrometry.

[0004] In one aspect, a method performed by a computing device includes receiving one or more wavefront elevation maps of a patient's eye. The wavefront elevation maps are processed to identify one or more attributes of the one or more wavefront elevation maps that correspond to vitreous floaters. The one or more attributes may include local spatial variation of the one or more wavefront elevation maps, temporal variation between multiple wavefront elevation maps, and depth information indicative of scattering of light from within the vitreous of the patient's eye.

[0005] The following description and the related drawings set forth in detail certain illustrative features of the one or more embodiments.

[0006] The accompanying drawings depict certain aspects of one or more embodiments and therefore should not be considered as limiting the scope of the disclosure. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic block diagram of a system for characterization of floaters using aberrometry, in accordance with certain embodiments. [Figure 2] FIG. 1 is a schematic block diagram of a system for performing aberration measurements in conjunction with scattered light depth measurements, according to certain embodiments. [Figure 3] FIG. 1 illustrates a process flow diagram of a method for characterizing floaters using aberrometry, according to certain embodiments. [Figure 4] FIG. 1 illustrates a process flow diagram of a method for using aberrometry data to train a machine learning model to characterize floaters, according to certain embodiments. [Figure 5] FIG. 1 illustrates an exemplary computing device that at least partially implements one or more functions for creating and presenting a primary treatment plan and one or more backup plans. DETAILED DESCRIPTION OF THE INVENTION

[0008] For ease of understanding, the same reference numerals have been used, where possible, to indicate identical elements that are common to the various figures, and it is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.

[0009] Aberrometry is a relatively inexpensive technique for characterizing the refractive error of the eye. The output of an aberrometer is a wavefront elevation map, where the elevation at each point indicates the phase delay of that point on a plane wavefront that passes through the cornea, lens, and vitreous of the eye to the retina. The wavefront elevation map can then be analyzed to characterize the refractive error of the eye. An aberrometer can be used to characterize the clinical significance of floaters in the eye using the techniques described herein.

[0010] The system 100 includes an aberrometer 102 configured to detect refractive error of the eye 110. The aberrometer 102 may perform wavefront aberration measurements, measuring the propagation of a wavefront through the eye 110. In particular, the aberrometer 102 measures the propagation of a wavefront through the cornea 112, the lens 114, the lens capsule 116, and the vitreous body 118 to the retina 120. For example, the aberrometer 102 may be implemented as, or similar to, an Optiwave Refractive Analysis (ORA) system by Alcon. The output of the aberrometer 102 is a wavefront elevation map comprising a two-dimensional array of values, each value representing a point from which light was scattered, and the elevation at each point representing the phase delay of the wavefront as it impinges on that point. In some implementations, for each index (e.g., X, Y coordinates), a two-dimensional array of magnitude values ​​is stored, where the phase and amplitude values ​​indicate the phase delay and amplitude of light scattered from the point in space corresponding to the X, Y coordinates. The light beam emitted by the aberrometer 102 will be scattered by any floaters 122 in the vitreous. The degree of scattering will correspond to the number of floaters 122, the size of the floaters 122, and the opacity of the floaters 122.

[0011] The aberrometer 102 may be used alone to characterize floaters 122 or may be used in combination with other ophthalmic measurement devices, such as a scanning laser ophthalmoscope (SLO) 104 and / or an optical coherence tomography (OCT) device 106. For example, the SLO 104 and / or the OCT device 106 may be used to confirm the presence of floaters 122 detected using the aberrometer 102 and / or to precisely identify the location of any detected floaters 122, as described below. In some implementations, the aberrometer 102, the SLO 104, and the OCT device 106 are contained within the same housing.

[0012] Referring to FIG. 2, the aberrometer 102 can be used as part of the illustrated system 200 to detect floaters 122. The system 200 includes a light detection and ranging (LIDAR) laser source 202 controlled by a laser driver 204, which causes the LIDAR laser source 202 to emit a series of pulses 206 having a known period between pulses. The pulses 206 pass through a beam splitter 207 to the eye 110. Return pulses 208, comprising light reflected from the eye 110, pass through the beam splitter, a portion of which is received by a time-of-flight (TOF) sensor / camera 210. TOF measurements from the TOF sensor / camera 210 are input to a depth processor 212, which interprets the TOF measurements to estimate the depth of the point within the eye 110 that scattered the return pulses 208.

[0013] The pulse 206 may reach the eye 110 by passing through a collimating lens 402 and a dichroic mirror 216, which directs light from the collimating lens 402 onto a rotating mirror 218, such as a galvo scanner, that rotates about one or two axes. Light reflected by the rotating mirror 218 is incident on a dichroic mirror 220. A portion of the light transmitted through the dichroic mirror is incident on one or more objective lenses 222, 224, which focus the light output from the objective lenses onto the eye 110. Light scattered by the eye 110, including floaters 122 within the eye 110, returns through the objective lenses 222, 224, passes through the dichroic mirror 220, is back-scanned by the rotating mirror 2187, enters the dichroic mirror 216, passes through the lens 214, and enters the beam splitter 207. A portion of the light scattered by the eye 110 is directed by a beam splitter 207 onto a TOF sensor / camera 210 .

[0014] A portion of the light scattered by the eye 110 is directed by a dichroic mirror 220 onto a rotating mirror 226, which can be rotatable about one or two axes and can be implemented as a galvo mirror. The rotating mirror 226 back-scans a portion of the scattered light onto a dichroic mirror 228, which directs a portion of the back-scanned light onto the aberrometer 102. A portion of the back-scanned light is transmitted through the dichroic mirror onto a fixation target used to calibrate the TOF sensor / camera 210.

[0015] In the illustrated embodiment, a floater treatment laser 232 is also included. For example, light from the floater treatment laser 232 may pass through a collimating lens 234, through a dichroic mirror 216, and onto a rotating mirror 218, which scans the light from the floater treatment laser 232 onto the eye 110, through a dichroic mirror 220, and through objective lenses 222, 224, and onto the eye 110. The floater treatment laser 232 may generate pulses of light focused on the floater 122 and having sufficient intensity to disrupt the floater 122. The timing and focal depth of the pulses generated by the floater treatment laser 232 may be controlled using the position and depth of the floater 122 determined using the TOF sensor / camera 210 and the depth processor 212. For example, the focal depth of a treatment pulse from the floater treatment laser 232 may be selected using a depth estimate from the depth processor 212, and the treatment pulse may be emitted when the rotating mirror 218 is in the same position as when the pulse 206 used to obtain the depth estimate was emitted.

[0016] In the foregoing description, various beam splitters and dichroic mirrors are discussed. It should be understood that the arrangement of elements that receive transmitted or reflected light from a beam splitter or dichroic mirror may be reversed, i.e., a first element that receives transmitted light and a second element that receives reflected light from a beam splitter or dichroic mirror may be replaced with a first element that receives reflected light and a second element that receives transmitted light.

[0017] The system 200 uses a LIDAR laser source 202 to bounce low-power light pulses off a target, i.e., the eye 110, and detects the reflected light using a TOF sensor / camera 210. The TOF sensor / camera 210 captures a time-stamped image. A depth processor 212 then calculates the time-of-flight based on the timestamp and the known transmission time of each pulse 206. The distance to the structure from which the scattered light is detected in the image can then be calculated using the speed of light. This distance can be combined with the known orientation of the rotating mirror 218 to obtain high-resolution information in all three dimensions. In particular, for each received pulse 208, the position and distance estimate of the rotating mirror 218 can be used to obtain the three-dimensional coordinates of the point within the eye 110 that scattered the pulse 208. The amplitude of each pulse 208 indicates the reflectivity (and corresponding opacity) of the point within the eye. A point cloud is thus obtained, with each point having a three-dimensional coordinate and a reflectivity.

[0018] The LIDAR laser source 202 may emit hundreds of thousands of pulses 206 per second. A portion of the light from each pulse may be reflected from the floaters 122 and returned to the TOF sensor / camera 210. The point cloud therefore includes points corresponding to the pulses 206 reflected from the floaters 122. Due to the small size of the floaters 122, a resolution of less than 100 mm is desired and may be acquired using a pulsing frequency of greater than 350 MHz.

[0019] The aberrometer 102 measures the wavefront aberrations of the eye, such as by using reflected light from the same pulse 206. Floaters 122 inside the eye, which may be moving, act as perturbations to the optical path. The floaters 122 cause variations in the refractive index along the beam path and scatter light, both of which add noise to the wavefront measurements. By simultaneously performing TOF and wavefront measurements, wavefront deviations along different sections of the beam path through the vitreous 118 can be correlated with the location of structures, such as floaters 122, that caused the wavefront deviations. The aberrometer 102 can also provide continuous sampling of pupil size. The 3D measurements can be further used with tomographic reconstruction software to generate a refractive map of the vitreous 118. Such a refractive map indicates the location, size, and opacity of the floaters 122. Once floaters 122 are identified as described above, their clinical significance can be determined using the following parameters: For example, the distance between the floater and the retina, the shadow areas and darkness caused by the retained floater, etc. The magnitude of the floater disturbance can be estimated using the local variance (or roughness) of the waveform profile.

[0020] 3 illustrates a method 300 for characterizing floaters using aberrometry. Method 300 may be performed by floater detection module 108 using wavefront elevation maps received from aberrometer 102. Method 300 may be performed with or without system 200, as described in more detail below. Method 300 identifies one or more wavefront elevation map attributes that may correspond to vitreous floaters to determine their clinical significance. These attributes include some or all of local spatial variation, temporal variation, and depth value, as described in more detail below.

[0021] The method 300 includes, in step 302, performing an aberration measurement of the patient's eye 110. Step 302 may simply involve performing multiple aberration measurements at a fixed or variable period. Step 302 may involve performing the aberration measurements simultaneously with distance measurements, as described above with respect to the system 200. The result of each aberration measurement is a wavefront elevation map. The wavefront elevation map may be viewed as an image in which each pixel location (X, Y coordinates) is associated with an elevation value indicating a phase delay. As described above, each X, Y coordinate may also have an amplitude value. The wavefront elevation map will have an approximately circular shape corresponding to the pupil of the eye 110.

[0022] The method 300 may include, at step 304, measuring local variations in each wavefront elevation map. Refractive error in the eye 110 will result in a non-planar wavefront elevation map. Similarly, the presence of a cataract will result in uniformly distributed scattering. However, scattering caused by floaters 122 may result in local elevation variations in the wavefront elevation map that are distinguishable from elevation variations caused by refractive error. Local variations may be measured in various ways. For example, a sliding-window two-dimensional spatial Fourier transform may be calculated for each elevation map, or for discrete regions at some or all points (e.g., X, Y coordinates) within each elevation map selected for one or both of the phase and amplitude values. For example, the discrete regions may have heights and widths 0.05 to 0.15 times the diameter of the wavefront elevation map. Frequencies in the Fourier transform that are above a frequency threshold and have amplitudes above an amplitude threshold may be considered to correspond to vitreous floaters. Thus, the output of step 304 may be a function of the magnitude (e.g., mean, maximum, integral) of the portion of the two-dimensional Fourier transform that exceeds a threshold frequency. Many other methods exist for measuring and analyzing the local variations (phase elevation map) of a wavefront, such as landmark-based geometric morphometry, principal component analysis, and analysis of global variations in morphological features, such as morphological integration. Wavefronts acquired over a period of time can also be analyzed to obtain decorrelation of wavefront changes / variations over time. Decorrelation of wavefront changes / variations over time can be analyzed to determine which regions of the phase elevation map are most variable due to floaters.

[0023] Method 300 may include measuring temporal variation between wavefront elevation maps in step 306. Floaters 122 may move within the vitreous 118 such that any scattering caused by them will be different in wavefront elevation maps captured at different times. In contrast, variations in wavefront elevation maps caused by cataracts or refractive errors do not change over time. Step 306 may involve performing eye tracking and compensating for eye movement when performing aberration measurements in step 302. In this way, movement in the location of regions of local variation can reasonably be attributed to movement of floaters 122 rather than eye movement. Measuring temporal variation may be performed in various ways. For example, a Z coordinate may be defined as an index assigned to each elevation map, where the index corresponds to the temporal order in which the wavefront elevation maps were acquired in step 302. Thus, temporal variation may be obtained by characterizing variation along the Z axis. For example, for a given X, Y coordinate, a volume may be defined that includes the range of X, Y, and possible Z coordinates. Thus, in step 302, a three-dimensional Fourier transform of this volume may be calculated for this volume for one or both of the phase and amplitude values. Alternatively, the substitute volume may be defined as the Fourier transform from step 304 for all Z coordinates. Any three-dimensional Fourier transform values ​​having a frequency above a frequency threshold and an amplitude above an amplitude threshold may be deemed to correspond to vitreous floaters. Thus, the output of step 306 may be a function of the magnitude (e.g., mean, maximum, integral) of the three-dimensional Fourier transform above a threshold frequency.

[0024] If system 200 is used, method 300 may further include measuring scattering depth, such as using a TOF measurement as described above, in step 308. For example, scattering found at a depth between the lens capsule 116 and the retina 120, i.e., the vitreous body 118, may be considered to correspond to vitreous floaters 122. Step 308 may be omitted to characterize floaters using only the aberrometer 102. Step 308 may include measuring the reflectance of points within the vitreous body 118 and generating a score therefrom, such as a sum or weighted sum of the reflectance of points within the vitreous body 118.

[0025] Step 308 can also be performed using another imaging modality, such as the SLO 104 or the OCT device 106. The SLO 104 can measure the distance from the floaters to the retina by focusing on the retina and then shifting the SLO focus to the floaters. The distance to the retina can be measured by converting the change in focus diopter to distance. The OCT device 106 can also measure the distance of the floaters to ocular structures in a similar manner. Long-range OCT can be used to detect the entire vitreous in a single scan, or short-range OCT can be used with multiple scans to detect floaters in the vitreous. The distance to the retina can be measured with appropriate calibration of the OCT system and by converting pixel positions to distance measurements.

[0026] Method 300 may include evaluating whether clinical criteria have been met in step 310. For example, some or all of the local variation measured in step 304, the temporal variation measured in step 306, and the scattering depth measured in step 308 may be evaluated with respect to individual thresholds, or may be combined (e.g., summed, weighted and summed, multiplied, etc.) and compared to a single threshold. If some or all of the individual thresholds or only a single threshold are exceeded, the patient's eye 110 may be deemed to have clinically significant floaters, such as those requiring laser ablation or vitrectomy. Alternatively or additionally, if the criteria of step 310 are met, other actions may be performed, such as scanning the eye 110 using another imaging modality, such as the SLO 104 or OCT device 106, to map the size and location of the vitreous floaters.

[0027] If the clinical criteria are found to be met, a characterization of the vitreous floaters may be output in step 312, which may include any of the information evaluated in step 310 (the local variation measured in step 304, the temporal variation measured in step 306, and / or the scattering depth measured in step 308).

[0028] 4 illustrates a method 400 that may be used to characterize vitreous floaters. Method 400 may be used in conjunction with or in place of method 300. For example, method 400 may be used in step 310 to determine whether clinically significant floaters are present.

[0029] Method 400 may include, at step 402, clustering training data entries. The training data entries may be clustered using k-means clustering, k-nearest neighbor clustering, or other clustering techniques. The training entries may include as input patient data, including information such as demographic data (age, gender, ethnicity) and comorbidities (cataract, retinal disease). The input for each entry further includes aberrometry data in the form of a single wavefront elevation map or a series of wavefront elevation maps acquired over time, as described above with respect to step 302. Each wavefront elevation map may include one or both of phase and amplitude values. Each training data entry may further include as a desired output a metric of floaters detected in the patient's eye, such as those acquired using an SLO or OCT device. The floater metric may include a human-generated estimate of clinical significance, a score indicating the composite reflectance of points in the vitreous 118, or some other metric of floaters. The clustering in step 402 may include performing multiple linear regression using the inputs of the training data entries as explanatory data and the desired outputs of the training data entries as response variables.

[0030] Then, the training data entries of each cluster can be used to train a machine learning model for each cluster, so that each cluster has a corresponding machine learning model. Each machine learning model can be trained to output an estimate of the clinical significance of vitreous floaters for a given input set. Each machine learning model can be any machine learning model known in the art, such as a deep neural network (DNN), a convolutional neural network (CNN), a multiple polynomial regression (MPR) (quadratic, cubic, or higher) model, a support vector regression model (SVM), or an SVM-radial bias function (SVM-RBF).

[0031] During utilization, the patient data (demographic data, comorbidities, wavefront elevation map) may be processed to select corresponding clusters in step 406, such as using the same clustering technique as used in step 402 or a different clustering technique. The patient data may then be processed in step 408 using a machine learning model for the selected cluster to obtain a predicted clinical significance of floaters in the patient's eye. The utilization of steps 406 and 408 may be performed using a different computing device than that used to perform steps 402 and 404.

[0032] Method 400 is merely exemplary. For example, clustering 402 may be omitted, and a single machine learning model may be trained to predict the clinical significance of floaters using training patient data. The training patient data used for each entry may include fewer or more data items than those listed above. For example, comorbidities may be omitted, and the machine learning model may be trained to predict the clinical significance of floaters without a priori knowledge of comorbidities such as cataracts or retinal disease. In particular, cataracts will scatter light used to perform aberrometry, and the machine learning model may be trained to account for this phenomenon.

[0033] 5 illustrates an exemplary computing system 500 that at least partially implements one or more of the described functions, such as method 300, one or both of method 400, and the processing described above with respect to system 200. Computing system 500 may be integrated with an imaging device, such as aberrometer 102, or may be a separate computing device that receives images of the patient's eye from an imaging device.

[0034] As shown, computing system 500 includes a central processing unit (CPU) 502, one or more I / O device interfaces 504 that may allow various I / O devices 514 (e.g., keyboard, display, mouse device, pen input, etc.) to be connected to computing system 500, a network interface 506 through which computing system 500 is connected to a network 590 (which, as described in connection with FIG. 1, may be a local network, an intranet, the Internet, or any other group of computing devices communicatively connected to each other), memory 508, storage 510, and an interconnect 512.

[0035] If the computing system 500 is an imaging system such as a digital microscope, the computing system 500 may further comprise one or more optical components for acquiring ophthalmic images of a patient's eye, as well as any other components known to those skilled in the art. If the computing system 500 is a surgical microscope, the computing system 500 may further comprise many other components known to those skilled in the art for performing the ophthalmic procedures described herein, as known to those skilled in the art.

[0036] CPU 502 may retrieve and execute programming instructions stored in memory 508. Similarly, CPU 502 may retrieve and store application data in memory 508. Interconnect 512 transfers programming instructions and application data between CPU 502, I / O device interface 504, network interface 506, memory 508, and storage 510. CPU 502 is included as representative of a single CPU, multiple CPUs, a single CPU with multiple processing cores, etc.

[0037] Memory 508 represents volatile memory, such as random access memory, and / or non-volatile memory, such as non-volatile random access memory or phase-change random access memory. As shown, memory 508 may store executable code executable by CPU 502 to implement part or all of the processes described above with respect to floatable object detection module 108, system 200, and method 300. Memory 508 may additionally or alternatively store executable code implementing a training algorithm 516 used during steps 402-404 of method 400.

[0038] Storage 510 may be non-volatile memory such as a disk drive, a solid state drive, or a collection of storage devices distributed across multiple storage systems. Storage 510 may optionally store a machine learning model 518 or multiple machine learning models 518, trained as described above with respect to Figure 4. If computing system 500 is used to train a machine learning model 514, storage 510 may further store training data 520, which may include multiple training data entries including patient data, as described above with respect to Figure 4.

[0039] Additional considerations The above description is provided to enable those skilled in the art to practice various embodiments described herein. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments. For example, changes may be made to the function and arrangement of elements discussed without departing from the scope of the disclosure. In various examples, various actions or elements may be omitted, substituted, or added, as appropriate. Features described with respect to some examples may be combined in several other examples. For example, an apparatus may be implemented or a method may be practiced using any number of aspects described herein. Furthermore, the scope of the present disclosure is intended to cover similar apparatuses or methods that are implemented using structure, functionality, or structure and functionality in addition to or other than various aspects of the disclosure described herein. It should be understood that any aspect of the disclosure disclosed herein may be implemented by one or more elements recited in the claims.

[0040] As used herein, a phrase referring to "at least one of" a list of items refers to any combination of those items, including single members. As an example, "at least one of a, b, or c" is intended to cover a, b, c, ab, ac, bc, and abc, as well as any combination with multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or a, b, and c in any other order).

[0041] As used herein, the term "determining" encompasses a variety of actions. For example, "determining" may include calculating, computing, processing, deriving, investigating, querying (e.g., querying a table, database, or other data structure), ascertaining, etc. "Determining" may also include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), etc. "Determining" may also include resolving, selecting, choosing, establishing, etc.

[0042] The methods disclosed herein include one or more steps or actions for achieving the method. Method steps and / or actions may be interchangeable with one another without departing from the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the claims. Furthermore, various actions of the methods described above may be performed by any suitable means capable of performing the corresponding functions. These means may include various hardware and / or software elements and / or modules, including, but not limited to, circuits, application specific integrated circuits (ASICs), or processors. In general, where there are actions illustrated in figures, those actions may include corresponding means-plus-function elements that are similarly numbered.

[0043] The various illustrative logic blocks, modules, and circuits described in connection with this disclosure may be implemented or performed by 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 (PLD), discrete gate or transistor logic, discrete hardware elements, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0044] The processing system may be implemented with a bus architecture. The bus may include any number of interconnected buses and bridges, depending on the particular application and overall design constraints of the processing system. The bus may interconnect various circuits, including, among other things, a processor, machine-readable media, and input / output devices. A user interface (e.g., keypad, display, mouse, joystick, etc.) may also be connected to the bus. The bus may also connect various other circuits, such as timing sources, peripherals, voltage regulators, power management circuits, etc., which are known in the art and will not be described further. The processor may be implemented with one or more general-purpose and / or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuits capable of executing software. Those skilled in the art will recognize how to best implement the described functionality of a processing system, depending on the particular application and the overall design constraints imposed on the overall system.

[0045] If implemented in software, the functions described above may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Software should be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Computer-readable media includes both computer storage media and communication media, such as any medium that facilitates transfer of a computer program from one place to another. A processor may be responsible for managing the bus and general processing, including the execution of software modules stored on the computer-readable storage medium. The computer-readable storage medium may be coupled to the processor such that the processor can read information from and write information to the storage medium. Alternatively, the storage medium may be integral to the processor. By way of example, the computer-readable medium may include a transmission line, a carrier wave modulated with data, and / or a computer-readable storage medium on which instructions are stored separately from a wireless node, all of which may be accessed by the processor via a bus interface. Alternatively or additionally, the computer-readable medium, or any portion thereof, may be integrated into the processor, such as in the case of a cache and / or general-purpose register file. Examples of machine-readable storage media include, for example, RAM (random access memory), flash memory, ROM (read-only memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium or any combination thereof. The machine-readable medium may be embodied in a computer program product.

[0046] A software module may include a single instruction or many instructions and may be distributed across several different code sections, across different programs, and across multiple storage media. A computer-readable medium may include many software modules. A software module contains instructions that, when executed by a device such as a processor, cause a processing system to perform various functions. A software module may include a transmitting module and a receiving module. Each software module may reside on a single storage device or be distributed across multiple storage devices. For example, a software module may be loaded into RAM from a hard drive when a trigger event occurs. During execution of a software module, a processor may load some of the instructions into a cache to speed access. One or more cache lines may then be loaded into a general-purpose register file for execution by the processor. When referring to the functionality of a software module, it is understood that such functionality is implemented by the processor when executing instructions from that software module.

[0047] The following claims are not limited to the embodiments set forth herein but are to be accorded the full scope consistent with the language of the claims. In the claims, when an element is referred to in the singular, it does not mean "one and only one" unless specifically stated otherwise, but rather "one or more." The term "some" refers to one or more unless specifically stated otherwise. No element of a claim is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase "means for," or, in the case of a method claim, unless the element is recited using the phrase "step of." All structural and functional equivalents of the elements of various aspects described throughout this disclosure that are known or later become known to those skilled in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Furthermore, nothing disclosed herein is intended to be made available to the public, regardless of whether such disclosure is expressly recited in the claims.

Claims

1. 1. An ophthalmic system for guiding ophthalmic surgery, comprising: an aberrometer, one or more processing devices; When coupled to and executed by the one or more processing devices, the one or more processing devices receiving one or more wavefront elevation maps of the patient's eye from the aberrometer; one or more memory devices having executable code stored thereon for identifying one or more attributes of the one or more wavefront elevation maps corresponding to vitreous floaters; An ophthalmology system comprising:

2. The ophthalmic system of claim 1 , wherein the one or more attributes include local spatial variation of the one or more wavefront elevation maps.

3. 2. The ophthalmic system of claim 1, wherein the one or more wavefront elevation maps comprise a plurality of wavefront elevation maps captured at different times, and the one or more attributes comprise temporal variation between the plurality of wavefront elevation maps.

4. 2. The ophthalmic system of claim 1, wherein the one or more wavefront elevation maps comprise multiple wavefront elevation maps captured at different times, and the one or more attributes comprise temporal variation between the multiple wavefront elevation maps and local spatial variation of the multiple wavefront elevation maps.

5. The ophthalmic system of claim 1 , wherein the aberrometer is an Optiwave Refractive Analysis (ORA) system.

6. and a Light Integrating and Ranging (LIDAR) system coupled to the aberrometer and configured to capture depth information simultaneously with the one or more wavefront elevation maps. The ophthalmic system of claim 1 , wherein the one or more attributes include the depth information.

7. The ophthalmic system of claim 6 , wherein the one or more attributes include an attribute of whether the depth information indicates scattering within the vitreous of the patient's eye.

8. The ophthalmic system of claim 6 , wherein the aberrometer and the LIDAR system have a common laser source and a common scanning mirror.

9. 10. The ophthalmic system of claim 1, wherein the executable code, when executed by the one or more processing devices, causes the one or more processing devices to identify the one or more attributes of the one or more wavefront elevation maps that correspond to the vitreous floaters by processing the one or more wavefront elevation maps with a machine learning model.

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