Improved ocular aberrometry system and method

Abstract

The present application relates to improved ocular aberrometry systems and methods. Techniques are disclosed for systems and methods for providing improved ocular aberrometry measurements. An ocular aberrometry system (100) includes a wavefront sensor (120) configured to provide wavefront sensor data associated with an optical target (102) monitored by the ocular aberrometry system (100) and a logic device (140) configured to communicate with the wavefront sensor (120). The logic device (140) is configured to receive (502) ocular aberrometry measurement output data including at least wavefront sensor data provided by the wavefront sensor (120), determine (504) an estimated eye alignment deviation corresponding to a relative position and / or orientation of the optical target (102) monitored by the ocular aberrometry system (100) based at least in part on the received ocular aberrometry measurement output data, and generate (508) user feedback corresponding to the received ocular aberrometry measurement output data based at least in part on the estimated eye alignment deviation.

Description

[0001] This application is a divisional application of the application filed on September 25, 2020, with application number 202080058353.0 and invention title "Improved Eye Aberration Meter System and Method". Technical Field

[0002] One or more embodiments of this disclosure relate generally to ocular aberration measurement, and more specifically, for example, to systems and methods for improving clinical or intraoperative ocular aberration measurement. Background Technology

[0003] Eye surgery may include reshaping the cornea and / or surface of the eye, inserting and / or replacing intraocular devices and / or artificial intraocular lenses (IOLs), and / or other surgical manipulations of the eye's active optical components. For optimal postoperative visual outcomes, sound preoperative clinical assessment and surgical planning, as well as intraoperative monitoring of the execution of the surgical plan, are crucial.

[0004] Ocular aberration measurements performed by an ocular aberrometer are typically a common method used to characterize the eye before surgery, monitor surgical progress, and assess surgical success. Conventional ocular aberrometers often suffer from various measurement errors associated with eye movement and / or misalignment of the aberrometer itself and optical aberrations, which can lead to inaccurate surgical planning and suboptimal surgical or visual outcomes for the patient.

[0005] Therefore, there is a need in the art for systems and methods to improve clinical and / or intraoperative ocular aberration measurements, thereby leading to optimized surgical or visual outcomes for patients. Summary of the Invention

[0006] Techniques for providing improved eye aberration measurements are disclosed. According to one or more embodiments, the eye aberration measurement system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, and logic means configured to communicate with the wavefront sensor. The logic means may be configured to determine a complex analysis engine for the eye aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are based at least in part on the wavefront sensor data provided by the wavefront sensor. The logic means may also be configured to generate a compact analysis engine for the eye aberration measurement system based at least in part on the determined complex analysis engine.

[0007] In other embodiments, a method may include determining a complex analysis engine for an eye aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are based at least in part on wavefront sensor data provided by wavefront sensors of the eye aberration measurement system; and generating a compact analysis engine for the eye aberration measurement system based at least in part on the determined complex analysis engine.

[0008] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions, which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method. The method may include determining a complex analysis engine for an eye aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are based at least in part on wavefront sensor data provided by wavefront sensors of the eye aberration measurement system; and generating a compact analysis engine for the eye aberration measurement system based at least in part on the determined complex analysis engine.

[0009] In a further embodiment, the eye aberration measurement system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, and logic means configured to communicate with the wavefront sensor. The logic means may be configured to receive eye aberration measurement output data including at least the wavefront sensor data provided by the wavefront sensor, determine, at least in part, an estimated eye alignment deviation corresponding to the relative position and / or orientation of the optical target monitored by the eye aberration measurement system based on the received eye aberration measurement output data, and generate user feedback corresponding to the received eye aberration measurement output data based, at least in part, on the estimated eye alignment deviation.

[0010] In other embodiments, the method may include receiving eye aberration measurement output data from an eye aberration measurement system including a wavefront sensor, wherein the eye aberration measurement output data includes at least wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, determining an estimated eye alignment deviation corresponding to the relative position and / or orientation of the optical target monitored by the eye aberration measurement system based at least in part on the received eye aberration measurement output data, and generating user feedback corresponding to the received eye aberration measurement output data based at least in part on the estimated eye alignment deviation.

[0011] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions, which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method. The method may include receiving eye aberration measurement output data from an eye aberration measurement system including a wavefront sensor, wherein the eye aberration measurement output data includes at least wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, determining, at least in part, an estimated eye alignment deviation corresponding to the relative position and / or orientation of the optical target monitored by the eye aberration measurement system based on the received eye aberration measurement output data, and generating user feedback corresponding to the received eye aberration measurement output data based at least in part on the estimated eye alignment deviation.

[0012] The scope of this invention is defined by the claims, which are incorporated herein by reference. By considering the following detailed description of one or more embodiments, those skilled in the art will gain a more complete understanding of the embodiments of the invention, and additional advantages of the invention may be realized. Reference will be made to the accompanying drawings, which will first be briefly described. Attached Figure Description

[0013] Figure 1 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is shown.

[0014] Figures 2A to 2B The diagram illustrates a block diagram of an aberration measurement characterization target of an eye aberration measurement system according to an embodiment of the present disclosure.

[0015] Figure 3 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is shown.

[0016] Figure 4 A flowchart illustrating the process of characterizing an eye aberration measurement system according to an embodiment of this disclosure is shown.

[0017] Figure 5 The illustration shows a flowchart of the process of operating an eye aberration measurement system according to an embodiment of the present disclosure.

[0018] Figure 6 A diagram illustrating a multilayer neural network according to an embodiment of this disclosure is shown.

[0019] The embodiments and advantages of the present invention can be best understood by referring to the following detailed description. It should be understood that the same reference numerals are used to identify the same elements illustrated in one or more figures. Detailed Implementation

[0020] According to various embodiments of this disclosure, the ocular aberration measurement system and method provide substantially real-time measurement and monitoring of aberrations in a patient's eye, reducing typical system and measurement errors in conventional systems. For example, when a patient's eye fixates during an ocular aberration measurement or examination, the eye naturally drifts. This fixation drift includes eye rolls, squints, torsions, and changes in x, y, and z positions. These alignment deviations can cause errors in the calculation of wavefront characterization aberrations of the eye. If the average of these alignment deviations is close to zero, averaging the wavefront aberration measurements across individual frames of the examined image stream is sufficient to eliminate most of the errors caused by misalignment. When the average alignment deviation of a known examination sequence is not close to zero, or when it cannot be confirmed that the alignment deviation has a near-zero mean, various strategies described herein can be used to reduce measurement errors or noise. These include averaging wavefront sensor data (e.g., represented by Zernike polynomial expansion) or a combination thereof, where the alignment deviation does not exceed a preset threshold; using sophisticated analytical methods (described herein) to correct for misalignment-based errors in the wavefront sensor data; determining eye fixation status based on clustering analysis applied to a series of wavefront measurements and / or based on eye-tracking data (e.g., for each wavefront measurement). The estimated alignment deviation and / or its impact on the wavefront measurements can be reduced to an eye alignment deviation metric and provided to the user of the ocular aberration measurement system as a display view with corresponding graphics, or used to allow the ocular aberration measurement system to ignore images from the examination image sequence or to abort and / or restart the examination. Thus, the embodiments provide substantially real-time monitoring feedback while providing more reliable and accurate aberration measurements than conventional systems, for example, by reducing the variability in clinical and intraoperative ocular aberration measurements due to eye movement during the examination image sequence.

[0021] In additional embodiments of this disclosure, the ocular aberration measurement system and method provide a platform and technique for accurately characterizing systemic aberrations and correcting wavefront sensor data that would otherwise be degraded by alignment biases associated with the patient's eye. For example, to account for potential errors caused by systemic aberrations or generated during eye movements, the ocular aberration measurement system described herein can employ one or more of two distinct calibration methods for accurate high-order aberration (HOA) analysis: a systemic characterization process and a complex analysis training process.

[0022] For the system characterization process, an eye aberration measurement system can be used to measure a series of reference interferograms (e.g., in the form of wavefront sensor data) generated by a model target configured to present a substantially single type of variable aberration (e.g., defocus aberration) to an eye aberration measurement system with substantially zero alignment bias. The reference interferograms can be used to characterize and / or quantify any systematic aberrations of a particular eye aberration measurement system, which can be used to correct wavefront sensor data provided by the wavefront sensor of that particular eye aberration measurement system, for example, by removing systematic aberrations prior to subsequent analysis.

[0023] For complex analysis training processes, an eye aberration measurement system can be used to capture a set of wavefront measurements generated using a model target configured to present different types and varying intensities of aberrations (e.g., Zernike expansion coefficients up to order 6) to an eye aberration measurement system with variable alignment biases in the aberration elements of the model target, such as rollover, squinting, torsion, and x, y, and z positions. The set of wavefront measurements can be used to train and / or improve a complex analysis engine performed by the eye aberration measurement system, and can be configured, for example, to generate substantially accurate estimates of alignment biases and / or corrected wavefront sensor data based on uncorrected wavefront sensor data, or a combination of uncorrected wavefront sensor data and eye-tracking data as described herein. Thus, embodiments provide more reliable and accurate aberration measurements than conventional systems, for example, by increasing the precision and accuracy of aberration measurements due to reduced errors caused by system aberrations and off-axis or skewed eye aberration measurements. Moreover, the embodiments provide a more robust (e.g., reliable and fast) aberration measurement system by increasing the range of alignment deviations that can be accurately compensated or corrected and thus included in a particular inspection (e.g., present and / or detected in a given image of an inspected image sequence).

[0024] Figure 1 A block diagram of an eye aberration measurement system 100 according to an embodiment of this disclosure is shown. Figure 1 In the illustrated embodiment, the ocular aberration measurement system 100 can be implemented to provide substantially real-time (e.g., 30 Hz update) monitoring of an optical target 102 (e.g., a patient's eye) while continuously compensating for common characterization errors, such as patient motion, system optical aberrations, thermal changes, vibrations, and / or other characterization errors that would otherwise degrade the ocular aberration measurements provided by the ocular aberration measurement system 100.

[0025] like Figure 1As shown, the eye aberration measurement system 100 includes a beacon 110 that generates a probe beam 111 for illuminating an optical target 102 for the wavefront sensor 120 and / or other components of the eye aberration measurement system 100. The eye aberration measurement system 100 may also include various other sensor-specific beacons and / or light sources, such as a light-emitting diode (LED) array 132 for illuminating the optical target 102 for the eye tracker 130, and an OCT beacon 123 for generating an OCT probe beam 124 to illuminate the optical target 102 for the OCT sensor 122. Beam splitters 112-116 provide the probe beam 111 to the optical target 102 and generate associated sensor beams 113, 115, 117 from the optical target 102 (e.g., probe beam 111, OCT probe beam 124, and a portion of the light generated by the LED array 132 and reflected by the optical target 102). Each sensor element of beacon 110, OCT beacon 123, LED array 132, and eye aberration measurement system 100 can be controlled by controller 140 (e.g., via communication links 141-144), and controller 140 can also serve as an interface between the sensor elements of beacon 110, OCT beacon 123, LED array 132, and eye aberration measurement system 100 and other elements of eye aberration measurement system 100, including user interface 146, server 150, distributed server 154, and other modules 148 as shown (e.g., accessed via optional communication links 145, 149, and 155).

[0026] In typical operation, controller 140 initializes one or more of wavefront sensor 120, optional OCT sensor 122, and optional eye tracker 130; controls beacon 110, OCT beacon 123, and / or LED array 132 to illuminate optical target 102; and receives eye aberration measurement output data (e.g., wavefront sensor data, eye tracker data, OCT sensor data) from various sensor elements of eye aberration measurement system 100. As described herein, controller 140 may process the eye aberration measurement output data itself (e.g., to detect or correct alignment errors and / or extract aberration parameters from wavefront sensor data) or may provide the eye aberration measurement output data to server 150 and / or distributed server system 154 (e.g., via network 152) for processing. Controller 140 and / or server 150 may be configured to receive user input (e.g., to control the operation of the ocular aberration measurement system 100) and / or generate user feedback at user interface 146 to display to a user via a display of user interface 146, such as a display view of ocular aberration measurement output data and / or characteristics of the ocular aberration measurement output data as described herein. Controller 140, server 150, and / or distributed server system 154 may be configured to store, process, and / or otherwise manipulate data associated with the operation and / or characterization of the ocular aberration measurement system 100, for example, including machine learning and / or training complex analysis engines (e.g., neural network-based classification and / or regression engines) to characterize systematic aberrations of the ocular aberration measurement system 100, detect alignment deviations associated with optical target 102, and / or correct wavefront sensor data and / or associated aberration classification coefficients as described herein. In various embodiments, the ocular aberration measurement system 100 can be configured to provide substantially real-time monitoring and user feedback (e.g., updates at 30 Hz or higher) of optical aberration measurements of the optical target 102, while continuously compensating for common characterization errors as described herein, which makes the ocular aberration measurement system 100 particularly suitable for clinical and intraoperative examinations.

[0027] Beacon 110 can be implemented using a laser source (e.g., generating highly coherent light) and / or a superluminescent diode (e.g., a “SLD” generating relatively low coherent light), which can be controlled by controller 140 to generate a probe beam 111 primarily for wavefront sensor 120. OCT beacon 123 can be implemented using a laser source and / or a superluminescent diode (e.g., generating relatively low coherent light, which is particularly suitable for OCT sensor 122), which can be controlled by controller 140 to generate an OCT probe beam 124 primarily for OCT sensor 122. In various embodiments, the OCT beacon can be integrated with OCT sensor 122, as shown, can be integrated with, for example, beacon 110, and / or can be implemented as its own independent beacon, similar to beacon 110 (e.g., arranged using a suitable beam splitter). LED array 132 can be implemented using a shaped or patterned LED array, which can be controlled by controller 140 to illuminate target 102 primarily for eye tracker 130. Beam splitters 112-116 can be implemented by any of a plurality of optical components (e.g., thin-film beam splitters, mirror surfaces) configured to aim the probe beam 111 and / or through the optical target 102 and to direct at least a portion of the probe beam 111 and / or the source beam generated by the optical target 102 (e.g., the reflected portion of light emitted from the probe beam 111 or 124 and / or the light emitted from the LED array 132) toward various sensor elements of the ocular aberration measurement system 100 to form sensor bundles 113-117 (e.g., sensor bundles). The optical target 102 can be, for example, a patient's eye, or can be implemented by a one-way (probe beam 111 off while the optical target 102 generates its own illumination) or two-way (e.g., normal operation with the probe beam 111 on) model target, for example, as described herein.

[0028] Wavefront sensor 120 can be implemented as any one or combination of devices or device architectures configured to measure aberrations of an optical wavefront (such as the optical wavefront of sensor beam 117 generated by at least one reflection from optical target 102 via probe beam 111), and wavefront sensor 120 can be configured to provide associated wavefront sensor data. For example, wavefront sensor 120 can be implemented as a Shack-Hartmann wavefront sensor, a phase-shifting schlieren wavefront sensor, a wavefront curvature sensor, a pyramid wavefront sensor, a common-path interferometer, a polygonal shearing interferometer, a Ronchi tester, a shearing interferometer, and / or any one or combination of wavefront sensors that can be configured for ophthalmic use. The wavefront sensor data provided by wavefront sensor 120 can be represented in various formats, including Zernike coefficients, such as Fourier, cosine, or Hartley transforms, or Taylor polynomials in cylindrical or Cartesian coordinates, or as interferograms.

[0029] OCT sensor 122 can be implemented as any one or a combination of devices or device architectures configured to capture two-dimensional and three-dimensional images at micron and / or submicron resolution from an optical scattering medium (such as optical target 102) using relatively low coherence light and low coherence interferometry, and is configured to provide associated OCT sensor data. For example, OCT sensor 122 can be implemented as any one or a combination of OCT sensor architectures that can be configured for ophthalmic use. Eye tracker 130 can be implemented as any one or a combination of devices or device architectures configured to track the orientation and / or position and / or features of optical target 102 (e.g., retina, pupil, iris, cornea, lens), including conventional eye trackers or fundus cameras, and is configured to provide associated eye tracker data. In some embodiments, eye tracker 130 can be configured to capture images of one or more types of Purkinje reflections associated with target 102.

[0030] The controller 140 can be implemented as any suitable logic device (e.g., a processing device, microcontroller, processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), memory storage device, memory reader, or other device or combination thereof) that can be adapted to execute, store, and / or receive appropriate instructions, such as software instructions that implement control loops or processes for controlling, for example, various operations of the aberration measurement system 100 and / or its components. Such software instructions can also implement methods for processing sensor signals, determining sensor information, providing user feedback (e.g., via user interface 146), querying device operating parameters, selecting device operating parameters, or performing any of the various operations described herein (e.g., operations performed by the logic devices of the various devices of the aberration measurement system 100).

[0031] Furthermore, a machine-readable medium may be provided for storing non-transitory instructions to be loaded into and executed by the controller 140. In these and other embodiments, the controller 140 may be implemented with other components as appropriate, such as volatile memory, non-volatile memory, one or more interfaces, and / or various analog and / or digital components for connection to means of the eye aberration measurement system 100. For example, the controller 140 may be adapted to store, for example, sensor signals, sensor information, complex analysis parameters, calibration parameters, calibration point sets, training data, reference data, and / or other operating parameters over time, and to provide such stored data to other elements of the eye aberration measurement system 100. In some embodiments, the controller 140 may be integrated with a user interface 146.

[0032] User interface 146 may be implemented as one or more of a display, touchscreen, keyboard, mouse, joystick, knob, virtual reality headset, and / or any other device capable of accepting user input and / or providing feedback to the user. In various embodiments, user interface 146 may be adapted to provide user input to other devices of the eye aberration measurement system 100 (such as controller 140). User interface 146 may also be implemented with one or more logic devices adapted to execute instructions, such as software instructions, to implement any of the various processes and / or methods described herein. For example, user interface 146 may be adapted to establish communication links, send and / or receive communications (e.g., sensor data, control signals, user input, and / or other information), determine parameters for one or more operations, and / or perform various other processes and / or methods described herein.

[0033] In some embodiments, the user interface 146 may be adapted to accept user input, for example, to establish a communication link (e.g., to server 150 and / or distributed server system 154), select specific parameters for the operation of the aberration measurement system 100, select a method for processing sensor data, adjust the position and / or orientation of the articulated model target, and / or otherwise facilitate the operation of the aberration measurement system 100 and devices within the aberration measurement system 100. Once the user interface 146 has accepted user input, the user input may be transmitted to other devices of the system 100 via one or more communication links. In one embodiment, the user interface 146 may be adapted to display time series of various sensor data and / or other parameters as part of a display of graphs or maps including such data and / or parameters. In some embodiments, the user interface 146 may be adapted to accept user input to, for example, modify control loop or process parameters of controller 140, or control loop or process parameters of any other element of the aberration measurement system 100.

[0034] Other modules 148 may include any one or a combination of sensors and / or devices configured to facilitate operation of the aberration measurement system 100. For example, other modules 148 may include a temperature sensor configured to measure one or more temperatures associated with operation of one or more elements of the aberration measurement system 100; a humidity sensor configured to measure ambient humidity around the aberration measurement system 100; a vibration sensor configured to measure vibration amplitude and / or presence associated with operation of the aberration measurement system 100; a patient sensor configured to measure the posture, motion, or other characterization of the patient supplying the optical target 102; and / or other sensors capable of providing sensor data that facilitates operation of the aberration measurement system 100 and / or corrects for common systematic errors typical in the operation of the aberration measurement system 100. In additional embodiments, other modules 148 may include an additional illumination and camera system, similar to the combination of the LED array 132 and the eye tracker 130, configured to capture images of one or more types of Purkinje reflections associated with the target 102.

[0035] For example, server 150 may be implemented similarly to controller 140 and may include various elements of a personal computer or server computer for storing, processing, and / or otherwise manipulating relatively large datasets associated with one or more patients, such as relatively large training datasets as described herein, to train neural networks or implement other types of machine learning. For example, distributed server system 154 may be implemented as a distributed combination of multiple embodiments of controller 140 and / or server 150 and may include networking and storage devices, as well as the ability to facilitate the storage, processing, and / or other manipulation of relatively large datasets (including relatively large training datasets as described herein) to train neural networks or implement other types of machine learning in a distributed manner. Network 152 may be implemented as one or more of wired and / or wireless networks, local area networks, wide area networks, the Internet, cellular networks, and / or according to other network protocols and / or topologies.

[0036] Figures 2A to 2B The illustration shows a block diagram of aberration measurement characterization targets 202A-B for an eye aberration measurement system 100 according to an embodiment of this disclosure. Figure 2A In the illustrated embodiment, the aberration measurement characterization target 202A can be implemented as a one-way model target, which is configured to characterize optical aberrations associated with the eye aberration measurement system 100. For example... Figure 2A As shown, the aberration measurement characterization target / single-pass model target 202A includes a laser or SLD source 260, which generates a source beam 211 via a lens system 262 and is oriented along the optical axis of the optical aberration system 100 (e.g., aligned with the probe beam 111 exiting the beam splitter 116). In various embodiments, for example, the source 260 may be configured to generate a diverging spherical wavefront with controllable convergence power, a converging spherical wavefront with controllable convergence power, or a plane wave with zero power. The lens system 262 is coupled to a linear motion actuator 264 via a mounting base 265, which allows the lens system 262 to move along its optical axis to change the defocus aberration of the single-pass model target 202A to generate multiple reference interferograms and corresponding wavefront sensor data (e.g., provided by wavefront sensor 120). For example, such reference interferograms and / or corresponding wavefront sensor data may be aggregated and stored as... Figure 3 The aberration meter model 360 is used to correct system aberrations associated with the eye aberration measurement system 100 as described herein.

[0037] In some embodiments, the lens system 262 may be implemented as a trackable lens from the National Institute of Standards and Technology (NIST), and the linear motion actuator 264 may be implemented as a relatively high-precision actuator stage configured to position the lens system 262 at a set of locations spaced apart from the source 260 to generate a source beam 211 with known and predefined defocus powers (e.g., defocus aberrations), such as -12 to 6 diopters or 0 to ±5 diopters, for example, with a step size of 5.0D or at a higher resolution step size, based on the range of defocus aberrations typically experienced by the patient as monitored by the ocular aberration measurement system 100. The resulting aberrometer model 360 can be used to compensate for various systemic aberrations, including those attributable to shot noise, thermal variations, and vibrations.

[0038] like Figure 2B As shown, the aberration measurement characterization target / two-way model target 202B includes an interchangeable eye aberration model 270, which is releasably coupled to a six-degree-of-freedom (6DOF) motion actuator 272 via one or more mounts 273. The 6DOF motion actuator 272 is configured to change the position and / or orientation of the interchangeable eye aberration model 270 to generate multiple selected (e.g., known) alignment biases (e.g., relative to the optical axis of the eye aberration measurement system 100) and corresponding multiple sets of wavefront sensor data. For example, such alignment biases and corresponding wavefront sensor data can be aggregated and stored as... Figure 3 The eye model 370 is used to train a complex analysis engine or a compact analysis engine as described herein to detect alignment deviations associated with the eye target 102 and correct the corresponding wavefront sensor data.

[0039] In some embodiments, the interchangeable eye aberration model 270 can form one element of a set of interchangeable eye aberration models, each model forming a precise amount of predefined eye aberration (e.g., represented by precise and predefined Zernike coefficient amplitudes, such as the amplitude of a Zernike expansion to the 6th order expressed in micrometers). In one embodiment, such an interchangeable eye aberration model can be cut using transparent poly(methyl methacrylate) (PMMA) on a contact lens lathe. More generally, such an interchangeable eye aberration model can be measured by a third-party profilometer with traceability to NIST for ground truth comparison with measurements performed by the eye aberration measurement system 100. In various embodiments, the 6DOF motion actuator 272 can be configured to provide micrometer-resolution positioning of the interchangeable eye aberration model 270 along the x, y, and z axes, and microradian orientation of the interchangeable eye aberration model 270 about the θy (rolling eyes), θx (oblique gaze), and θz (torsion) directions, as shown in representative coordinate systems 280A-B.

[0040] In general operation, each interchangeable ocular aberration model 270 in this group (e.g., a group of 10 or more interchangeable models) can be sequentially mounted to a 6DOF motion actuator 272, and the controller 140 can be configured to control the 6DOF motion actuator 272 to position or orient the interchangeable ocular aberration model 270 at a set of relative positions and / or orientations (e.g., relative to the optical axis of the ocular aberration measurement system 100) within a range of alignment deviations typically experienced by the patient as monitored by the ocular aberration measurement system 100. In one embodiment, the alignment deviation group may include approximately 40,000 different alignment deviations. In various embodiments, combining the alignment deviation group with a corresponding set of wavefront sensor data (e.g., provided by wavefront sensor 120) can form a supervised data set (e.g., eye model 370), which can be used to determine a complex or compact analysis engine as described herein. Such an analysis engine can be used to compensate for alignment deviations of the optical target 102, which may also be measured by the eye tracker 130. More generally, the combination of characterizations performed by aberration measurement characterizing targets 202A-B can be used to compensate for or correct both systematic aberrations and errors in the wavefront sensor data caused by misalignment of optical target 102.

[0041] Figure 3 A block diagram of an eye aberration measurement system 300 according to an embodiment of this disclosure is shown. Figure 3 In the illustrated embodiment, the eye aberration measurement system 300 can be configured to generate a complex analysis engine 350 and / or a compact analysis engine 340 using characterization data generated by the eye aberration measurement system 100 through aberration measurement characterization of targets 202A-B. These engines can be used during operation of the eye aberration measurement system 100 to provide substantially real-time monitoring and user feedback (e.g., updates at 30 Hz or higher) of optical aberration measurements of optical targets 102, while continuously compensating for common characterization errors as described herein.

[0042] like Figure 3As shown, the eye aberration measurement system 300 is similar to the eye aberration measurement system 100, but has additional details regarding the various data structures and executable program instructions used in the operation of the eye aberration measurement system 100 or 300. For example, the controller 140 is shown to be implemented with a compact analysis engine 340, and the server 150 and the distributed server system 154 are each shown to be implemented with or storing one or more of the following: an aberration meter model 360, an eye model 370, training data 392, a supervised learning engine 390, a complex analysis / neural network engine 350, and a compact analysis engine 340. Dashed lines generally indicate optional storage and / or implementation of particular elements, but in various embodiments, each of the controller 140, server 150, and distributed server system 154 may implement or store any identified elements and / or additional elements as described herein.

[0043] Generally, the aberration meter model 360 can be generated by aggregating sensor data associated with the use of the single-pass model target 202A to characterize the eye aberration measurement system 100, and the eye model 370 can be generated by aggregating sensor data associated with the use of the two-pass model target 202B to characterize the parameter space associated with the optical target 102. Training data 392 can be generated by combining the aberration meter model 360 and / or the eye model 370 and / or by generating and aggregating simulated training data sets, as described in this paper. Figure 6 The elements described herein. Supervised learning engine 350 can be implemented as a static learning engine and / or a learning engine generated according to a program configured to generate complex analysis engine 350 using training data 392 (e.g., a genetic algorithm that can update the learning engine). For example, complex analysis engine 350 can be implemented as a deep neural network, and / or can be implemented using other complex analysis methodologies, including various other neural network architectures or complex analysis methodologies, including dense K-nearest neighbor (k-NN) databases for classification and / or regression as described herein. Compact analysis engine 340 can be implemented as a compact form of complex analysis engine 350, such as a relatively low-resource but high-performance form more suitable for execution by controller 140, and thus can be implemented as a deep neural network and / or other complex analysis methodologies. In a particular embodiment, compact analysis engine 340 can be implemented as a neural network with fewer hidden layers and / or neurons / layers than complex analysis engine 350.

[0044] Figure 4 The illustration shows a flowchart of a process 400 characterizing an eye aberration measurement system 100 and / or 300 according to an embodiment of this disclosure. It should be understood that any step, substep, subprocess, or block of process 400 may differ from... Figure 4The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to a subsequent part of the corresponding process. While process 400 is a reference, Figures 1 to 3 The system, process, control loop, and image described herein may be used to describe a process 400, but process 400 may be performed by other systems that are different from those systems, processes, control loops, and images and include different selections of, for example, electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0045] In block 402, an aberration meter model associated with the eye aberration measurement system is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to control source 260 of a single-pass model target 202A (arranged as an optical target 102 monitored by the eye aberration measurement system 100) to generate source beam 211 via lens system 262 to illuminate wavefront sensor 120 and / or other elements of the eye aberration measurement system 100. For example, controller 140 may be configured to vary the defocus aberration of the single-pass model target 202A according to a plurality of selected defocus powers to generate a plurality of wavefront sensor data sets provided by wavefront sensor 120. Controller 140, server 150, and / or distributed server system 154 may be configured to determine system aberrations associated with the eye aberration measurement system 100 based at least in part on the plurality of wavefront sensor data sets provided by wavefront sensor 120. System aberrations and / or associated wavefront sensor data sets can be stored (e.g., on server 150 and / or distributed server system 154) as aberration meter model 360.

[0046] In block 404, an eye model associated with the aberration measurement system is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to control beacon 110 of aberration measurement system 100 to generate probe beam 111 to illuminate two-way model target 202B (an optical target 102 arranged to be monitored by aberration measurement system 100), thereby sequentially illuminating (e.g., via reflection from probe beam 111) one or more of the wavefront sensor 120, eye tracker 130, OCT sensor 122, and / or other elements of aberration measurement system 100. For example, controller 140 may be configured to change the position and / or orientation of interchangeable aberration model 270 of two-way model target 202B relative to the optical axis 111 of aberration measurement system 100 based on a plurality of selected alignment deviations to generate a plurality of corresponding wavefront sensor data sets provided by wavefront sensor 120. Multiple selected alignment deviations and / or corresponding sets of multiple wavefront sensor data can be stored (e.g., on server 150 and / or distributed server system 154) as eye model 370. Similar techniques can be used to combine eye-tracking data from eye tracker 130 and OCT sensor data from OCT sensor 122 into eye model 370.

[0047] In block 406, a complex analytics engine is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine complex analytics engine 350 based at least in part on aberration model 360 generated in block 402 and / or eye model 370 generated in block 404. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to form a deep neural network 600, which includes an input layer 620, an output layer 640, and at least one hidden layer 630-639 connecting the input layer 620 and the output layer 640, each layer including multiple neurons. Controller 140, server 150, and / or distributed server system 154 may be configured to train at least one trainable weighting matrix W associated with each neuron of the input, output, and hidden layers of neural network 600 via supervised learning engine 390, using alignment biases of eye model 370 as ground truth output data and corresponding wavefront sensor data sets of eye model 370 as training input data as described herein. The resulting deep neural network may be stored and used as complex analysis engine 350. In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate, at least in part, multiple corrected wavefront sensor data sets corresponding to multiple selected alignment biases of eye model 370 based on system aberrations associated with the eye aberration measurement system in aberrometer model 360, then form neural network 600 and train one or more complex analysis parameters of neural network 600 using supervised learning engine 390 to determine complex analysis engine 350 as described herein.

[0048] In box 408, a compact analysis engine is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to form a compact neural network 600 (including an input layer 620, an output layer 640, and a single hidden layer 630 connecting the input layer 620 and the output layer 640), and to generate a weighted matrix W associated with each neuron of the input, output, and / or hidden layer of the compact neural network 600, based at least in part on one or more complex analysis parameters associated with the hidden layers 630-639 of the plurality of complex analysis engines 350. During generation, the compact analysis engine 340 may be stored or otherwise integrated with or implemented by controller 140, which may use the compact analysis engine 340 to generate substantially real-time (e.g., 30 frames / second) user feedback (e.g., display views including various graphics) and reliable and accurate monitoring of eye alignment deviation, eye aberration, and / or other characteristics of the optical target 102 as described herein.

[0049] Figure 5The illustration shows a flowchart of process 500 for operating the eye aberration measurement system 100 and / or 300 according to an embodiment of this disclosure. It should be understood that any step, substep, subprocess, or block of process 500 may differ from... Figure 5 The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to a subsequent part of the corresponding process. While process 500 is for reference only... Figures 1 to 3 The system, process, control loop, and image described herein may be used to describe a process 500, but process 500 may be performed by other systems that are different from those systems, processes, control loops, and images and include, for example, different choices of electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0050] In block 502, eye aberration output is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive eye aberration measurement output data that includes at least wavefront sensor data provided by wavefront sensor 120. More generally, the eye aberration measurement output data may include any one or more of the wavefront sensor data provided by wavefront sensor 120 as described herein, OCT sensor data provided by OCT sensor 122, eye tracker data provided by eye tracker 130, and / or other output data provided by eye aberration measurement system 100.

[0051] In block 504, an estimated eye alignment deviation is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine, at least in part, the estimated eye alignment deviation corresponding to the relative position and / or orientation of an optical target 102 (e.g., a patient's eye) monitored by eye aberration measurement system 100, based at least in part on eye aberration measurement output data received in block 502. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation based at least in part on eye tracker sensor data received in block 502. In other embodiments, the wavefront sensor data received in block 502 includes a time series of wavefront sensor measurements, and controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation by determining a wavefront-estimated eye alignment deviation corresponding to each wavefront sensor measurement.

[0052] For example, in one embodiment, controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation by: determining the corresponding estimated relative position and / or orientation of optical target 102 for each wavefront sensor measurement (e.g., estimated eye alignment of optical target 102); identifying one or more clusters of estimated relative position and / or orientation of optical target at least in part based on one or more preset or adaptive cluster thresholds; determining gaze alignment at least in part based on the centroid of the largest of one or more identified clusters; and determining the estimated eye alignment deviation for each wavefront sensor measurement by at least in part based on the difference between gaze alignment and estimated relative position and / or orientation of optical target corresponding to wavefront sensor measurements (e.g., the difference between gaze alignment and estimated eye alignment of optical target 102 in a time series corresponding to wavefront sensor measurements). In a related embodiment, the eye aberration measurement system 100 includes an eye tracker 130, a controller 140, a server 150, and / or a distributed server system 154. The eye tracker, controller, server, and / or distributed server system can be configured to determine the estimated eye alignment deviation by: determining the corresponding gaze state of the optical target for each wavefront sensor measurement based at least in part on a gaze threshold parameter and eye tracker sensor data corresponding to wavefront sensor measurements, omitting subgroups of wavefront sensor measurements; and then determining the corresponding estimated relative position and / or orientation of the optical target 102 based at least in part on the determined corresponding gaze state. For example, this technique can eliminate wavefront sensor measurements acquired when the optical target 102 is detected as (e.g., by the eye tracker 130) not fixed.

[0053] In block 506, corrected eye aberration measurement output data is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the corrected eye aberration measurement output data based at least in part on the estimated eye alignment deviation determined in block 504 and / or wavefront sensor data received in block 502. In some embodiments, where the wavefront sensor data comprises a time series of wavefront sensor measurements, controller 140, server 150, and / or distributed server system 154 may be configured to determine the corrected eye aberration measurement output data by determining an estimated eye alignment deviation associated with each wavefront sensor measurement, and generating an average wavefront sensor measurement as the corrected wavefront sensor measurement based at least in part on each wavefront sensor measurement having an associated estimated eye alignment deviation equal to or less than a preset maximum permissible deviation. This preset maximum permissible deviation may be selected or set by the manufacturer and / or user of the eye aberration measurement system 100.

[0054] In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine corrected eye aberration measurement output data by: determining an estimated eye alignment deviation associated with each wavefront sensor measurement for each wavefront sensor measurement; determining a corrected wavefront sensor measurement for each wavefront sensor measurement having an associated estimated eye alignment deviation equal to or less than a preset maximum permissible deviation based at least in part on the wavefront sensor measurement and / or the associated estimated eye alignment deviation; and generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine corrected wavefront sensor measurements by: applying the complex analysis engine 350 or compact analysis engine 340 of the eye aberration measurement system 100 to each wavefront sensor measurement to generate a corresponding wavefront estimated eye alignment deviation and / or corrected wavefront sensor measurement as described herein.

[0055] In block 508, user feedback is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback corresponding to the eye aberration measurement output data received in block 502, based at least in part on the estimated eye alignment deviation determined in block 504. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback by determining an eye alignment deviation metric based at least in part on the estimated eye alignment deviation and reporting the eye alignment deviation metric via user interface 146 of eye aberration measurement system 100. In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback based at least in part on the estimated eye alignment deviation determined in block 504 and / or the corrected eye aberration measurement output data determined in block 506. In various embodiments, such user feedback may include a substantially real-time display view of an eye alignment deviation metric based at least in part on an estimated eye alignment deviation determined in block 504, a substantially real-time display view of an eye aberration map based at least in part on corrected eye aberration measurement output data determined in block 504, and / or an auditory and / or visual alarm indicating that at least one estimated eye alignment deviation is greater than a preset maximum permissible deviation as described herein.

[0056] Therefore, embodiments of this disclosure can provide substantially real-time (e.g., 30 frames per second) user feedback (e.g., display views including various graphics) and reliable and accurate monitoring of eye alignment deviation, eye aberrations, and / or other characteristics of the optical target 102 as described herein. Such embodiments can be used to assist in various types of clinical and intraoperative ophthalmological examinations and help provide improved surgical outcomes.

[0057] Figure 6 A diagram illustrating a multilayer or “deep” neural network (DNN) 600 according to an embodiment of this disclosure is shown. In some embodiments, the neural network 600 may represent a neural network for implementing each of one or more models and / or analysis engines described in conjunction with system 100 and / or 300. The neural network 600 uses an input layer 620 to process input data 610. In various embodiments, as described herein, the input data 610 may correspond to aberration measurement output data and / or training data provided to one or more models and / or analysis engines to generate and / or train the one or more models and / or analysis models. In some embodiments, the input layer 620 may include a plurality of neurons or nodes for conditioning the input data 610 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning it for processing by the remainder of the neural network 600. In other embodiments, the input layer 620 may be configured to echo the input data 610 (e.g., where the input data 610 has been appropriately scaled, biased, filtered, range limited, and / or otherwise conditioned). Each neuron in input layer 620 generates an output that is provided to the neurons / nodes in hidden layer 630. Hidden layer 630 includes multiple neurons / nodes that process the output from input layer 620. In some embodiments, each neuron in hidden layer 630 generates an output that is then propagated through one or more additional hidden layers (ending in hidden layer 639). Hidden layer 639 includes multiple neurons / nodes that process the output from the previous hidden layer. Figure 6 In the illustrated embodiment, the output of hidden layer 639 is fed to output layer 640. In various embodiments, output layer 640 includes one or more neurons / nodes that can be used to modulate the output from hidden layer 639 by scaling, biasing, filtering, range limiting, and / or otherwise adjusting to form output data 650. In alternative embodiments, neural network 600 can be implemented according to different neural network or other processing architectures, including neural networks with only one hidden layer, neural networks with recurrent layers, and / or other various neural network architectures or complex analytical methodologies, including K-nearest neighbor (k-NN) databases for classification and / or regression.

[0058] In some embodiments, each of the input layer 620, hidden layers 631-639, and / or output layer 640 includes one or more neurons. In one embodiment, each of the input layer 620, hidden layers 631-639, and / or output layer 640 may include the same or different numbers of neurons. In a particular embodiment, the neural network 600 may include approximately six layers in total, each with up to 2,000-4,000 neurons. In various embodiments, each neuron in this constitutive neuron may be configured to receive a combination of its input x (e.g., a weighted sum generated using a trainable weighted matrix / vector W), receive an optional trainable bias b, and apply an activation function f to generate an output a, such as according to the equation a = f(Wx + b). For example, the activation function f may be implemented as a modified linear unit activation function, or an activation function with an upper and / or lower bound, any or a combination of the log-sigmoid function, the hyperbolic tangent function, and / or according to other activation function forms. Each neuron in such a network may be configured to operate according to the same or different activation functions and / or different types of activation functions as described herein. In a particular embodiment, corresponding to a regression application, only the neurons of the output layer 640 can be configured to apply this linear activation function to generate their respective outputs.

[0059] In various embodiments, the neural network 600 may be trained using supervised learning (e.g., implemented as a supervised learning engine 390), for example by systematically providing the neural network 600 with selected sets of training data (e.g., training data 392), each set of training data comprising a set of input training data and a corresponding set of ground truth (e.g., expected) output data (e.g., a combination of aberration meter model 360 and eye model 370), and then determining and / or otherwise comparing the differences between the resulting output data 650 (e.g., training output data provided by the neural network 600) and the ground truth output data (e.g., “training error”). In some embodiments, the training error may be fed back to the neural network 600 to adjust various trainable weights, biases, and / or other complex analysis parameters of the neural network 600. In some embodiments, such training error may be provided to the neural network 600 as feedback using one or more backpropagation techniques (including, for example, stochastic gradient descent and / or other backpropagation techniques). In one or more embodiments, a relatively large set of selected training data can be presented to the neural network 600 multiple times until the total loss function (e.g., mean squared error based on the difference between each set of training data) converges to or below a preset maximum allowable loss threshold.

[0060] In additional embodiments, the supervised learning engine 390 may be configured to include semi-supervised learning, weakly supervised learning, active learning, structured prediction, and / or other general machine learning techniques to aid in training complex analysis parameters of the neural network 600 as described herein (e.g., and generating a complex analysis engine 350). For example, the supervised learning engine 390 may be configured to generate simulated training data sets, each set including simulated input training data and a corresponding simulated ground truth output data set, and to perform supervised learning at least in part based on the simulated training data sets. Each of the simulated input training data and ground truth data sets may be generated by modifying the input training data (e.g., adjusting aberration parameters and / or alignment biases associated with the simulated two-way model objective) and interpolating non-simulated ground truth data to generate the corresponding simulated ground truth data. In various embodiments, the supervised learning engine 390 may be configured to simulate one or millions of simulated training data sets, each set at least slightly deviating from the training data set corresponding to the eye model 370, and to train the neural network 600 based on one or millions of such simulated training data sets.

[0061] Where applicable, the various embodiments provided in this disclosure may be implemented using hardware, software, or a combination of hardware and software. Similarly, where applicable, the various hardware and / or software components described herein may be combined into composite components comprising software, hardware, and / or both, without departing from the spirit of this disclosure. Where applicable, the various hardware and / or software components described herein may be divided into sub-components comprising software, hardware, or both, without departing from the spirit of this disclosure. Furthermore, where applicable, it is contemplated that a software component may be implemented as a hardware component, and vice versa.

[0062] Software according to this disclosure, such as non-transitory instructions, program code, and / or data, may be stored on one or more non-transitory machine-readable media. It is also conceivable that the software identified herein may be implemented using one or more general-purpose or special-purpose computers and / or networked and / or other computer systems. Where applicable, the order of the various steps described herein may be changed, combined into compound steps, and / or divided into sub-steps to provide the features described herein.

[0063] The embodiments described above are illustrative and do not limit the invention. It should also be understood that many modifications and variations are possible based on the principles of the invention. Therefore, the scope of the invention is defined only by the following claims.

Claims

1. An eye aberration measurement system, comprising: A wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the eye aberration measurement system; as well as A logic device configured to communicate with the wavefront sensor, wherein the logic device is configured to: A complex analysis engine for the eye aberration measurement system is determined at least in part based on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are at least in part based on wavefront sensor data provided by the wavefront sensor; and A compact analysis engine for the eye aberration measurement system is generated, at least in part, based on the determined complex analysis engine.

2. The eye aberration measurement system as described in claim 1, wherein, The logic device is configured as follows: Generate the eye model associated with the eye aberration measurement system; and The complex analysis engine is determined at least in part based on the generated eye model.

3. The eye aberration measurement system as described in claim 2, wherein, Generating the eye model includes: A two-way model target arranged as an optical target monitored by the eye aberration measurement system illuminates at least the wavefront sensor of the eye aberration measurement system; The position and / or orientation of the interchangeable eye aberration model of the two-way model target relative to the optical axis of the eye aberration measurement system are altered based on multiple selected alignment deviations to generate corresponding sets of wavefront sensor data provided by the wavefront sensor; and The selected alignment deviations and the corresponding sets of wavefront sensor data are stored as the eye model.

4. The eye aberration measurement system as described in claim 3, wherein, The objectives of the two-way model include: The interchangeable eye aberration model; and A six-degree-of-freedom motion actuator, releasably coupled to the interchangeable eye aberration model and configured to change the position and / or orientation of the interchangeable eye aberration model to generate the plurality of selected alignment deviations and the corresponding plurality of sets of wavefront sensor data.

5. The eye aberration measurement system as described in claim 2, wherein, The complex analysis engine is defined as including: A neural network is formed, the neural network including a neuron input layer, a neuron output layer, and at least one neuron hidden layer connected between the neuron input layer and the neuron output layer; and At least one trainable weighted matrix associated with each neuron in the input, output, and hidden layers of the neural network is trained via a supervised learning engine, the training using alignment bias of the eye model as ground truth output data and corresponding wavefront sensor data sets of the eye model as training input data.

6. The eye aberration measurement system as described in claim 1, wherein, The logic device is configured as follows: Generate the aberration meter model associated with the eye aberration measurement system; and The complex analysis engine is determined at least in part based on the generated aberration model.

7. The eye aberration measurement system as described in claim 6, wherein, Generating the aberration meter model includes: A one-way model target arranged as an optical target monitored by the eye aberration measurement system illuminates at least the wavefront sensor of the eye aberration measurement system; The defocus aberration of the single-pass model target is changed according to multiple selected defocus powers to generate multiple sets of wavefront sensor data provided by the wavefront sensor. The system aberrations associated with the eye aberration measurement system are determined, at least in part, based on the multiple sets of wavefront sensor data; and The system bias and / or the multiple sets of wavefront sensor data are stored as the aberration meter model.

8. The eye aberration measurement system as described in claim 7, wherein, The objectives of the one-way model include: A plane-wave retinal source, including a laser or a superluminescent diode, said laser or superluminescent diode being configured to generate a source beam oriented along the optical axis of the eye aberration measurement system; and A lens system coupled to a linear motion actuator configured to alter the defocus aberration of the single-pass model target to generate the plurality of reference interferograms and corresponding wavefront sensor data.

9. The eye aberration measurement system as described in claim 6, wherein, The complex analysis engine is defined as including: Based at least in part on the system aberrations associated with the eye aberration measurement system in the aberrometer model, multiple sets of corrected wavefront sensor data corresponding to multiple selected alignment deviations of the eye model are generated; A neural network is formed, the neural network including a neuron input layer, a neuron output layer, and a plurality of hidden neuron layers connected between the neuron input layer and the neuron output layer; and At least one trainable weighting matrix associated with each neuron in the neuronal input layer, output layer, and hidden layer of the neural network is trained via a supervised learning engine, the training using multiple selected alignment biases of the eye model as ground truth output data and multiple sets of corrected wavefront sensor data of the eye model as training input data.

10. The eye aberration measurement system as claimed in claim 1, wherein, The compact analytics engine is generated by: A compact neural network is formed, the compact neural network including a neuron input layer, a neuron output layer, and at least one neuron hidden layer connected between the neuron input layer and the neuron output layer; and A weighted matrix associated with each neuron in the input, output, and / or hidden layers of the compact neural network is generated, based at least in part on one or more complex analysis parameters associated with multiple neuronal hidden layers of the complex analysis engine.

11. A method comprising: A complex analysis engine for the eye aberration measurement system is determined at least in part based on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are at least in part based on wavefront sensor data provided by the wavefront sensor of the eye aberration measurement system; and A compact analysis engine for the eye aberration measurement system is generated, at least in part, based on the determined complex analysis engine.

12. The method of claim 11, further comprising: Generate the eye model associated with the eye aberration measurement system; as well as The complex analysis engine is determined at least in part based on the generated eye model.

13. The method of claim 12, wherein, Generating the eye model includes: A two-way model target arranged as an optical target monitored by the eye aberration measurement system illuminates at least the wavefront sensor of the eye aberration measurement system; The position and / or orientation of the interchangeable eye aberration model of the two-way model target relative to the optical axis of the eye aberration measurement system are altered based on multiple selected alignment deviations to generate corresponding sets of wavefront sensor data provided by the wavefront sensor; and The selected alignment deviations and the corresponding sets of wavefront sensor data are stored as the eye model.

14. The method of claim 13, wherein, The objectives of the two-way model include: The interchangeable eye aberration model; and A six-degree-of-freedom motion actuator, releasably coupled to the interchangeable eye aberration model and configured to change the position and / or orientation of the interchangeable eye aberration model to generate the plurality of selected alignment deviations and the corresponding plurality of sets of wavefront sensor data.

15. The method of claim 12, wherein, The complex analysis engine is defined as including: A neural network is formed, the neural network including a neuron input layer, a neuron output layer, and at least one neuron hidden layer connected between the neuron input layer and the neuron output layer; and At least one trainable weighted matrix associated with each neuron in the input, output, and hidden layers of the neural network is trained via a supervised learning engine, the training using alignment bias of the eye model as ground truth output data and corresponding wavefront sensor data sets of the eye model as training input data.

16. The method of claim 11, further comprising: Generate the aberration meter model associated with the eye aberration measurement system; as well as The complex analysis engine is determined at least in part based on the generated aberration model.

17. The method of claim 16, wherein, Generating the aberration meter model includes: A one-way model target arranged as an optical target monitored by the eye aberration measurement system illuminates at least the wavefront sensor of the eye aberration measurement system; The defocus aberration of the single-pass model target is changed according to multiple selected defocus powers to generate multiple sets of wavefront sensor data provided by the wavefront sensor. The system aberrations associated with the eye aberration measurement system are determined, at least in part, based on the multiple sets of wavefront sensor data; and The system bias and / or the multiple sets of wavefront sensor data are stored as the aberration meter model.

18. The method of claim 17, wherein, The objectives of the one-way model include: A plane-wave retinal source, including a laser or a superluminescent diode, said laser or superluminescent diode being configured to generate a source beam oriented along the optical axis of the eye aberration measurement system; and A lens system coupled to a linear motion actuator configured to alter the defocus aberration of the single-pass model target to generate the plurality of reference interferograms and corresponding wavefront sensor data.

19. The method of claim 16, wherein, The complex analysis engine is defined as including: Based at least in part on the system aberrations associated with the eye aberration measurement system in the aberrometer model, multiple sets of corrected wavefront sensor data corresponding to multiple selected alignment deviations of the eye model are generated; A neural network is formed, the neural network including a neuron input layer, a neuron output layer, and a plurality of hidden neuron layers connected between the neuron input layer and the neuron output layer; and At least one trainable weighting matrix associated with each neuron in the neuronal input layer, output layer, and hidden layer of the neural network is trained via a supervised learning engine, the training using multiple selected alignment biases of the eye model as ground truth output data and multiple sets of corrected wavefront sensor data of the eye model as training input data.

20. The method of claim 11, wherein, The compact analytics engine is generated by: A compact neural network is formed, the compact neural network including a neuron input layer, a neuron output layer, and at least one neuron hidden layer connected between the neuron input layer and the neuron output layer; and A weighted matrix associated with each neuron in the input, output, and / or hidden layers of the compact neural network is generated, based at least in part on one or more complex analysis parameters associated with multiple neuronal hidden layers of the complex analysis engine.

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