Dry eye mitigation for ocular aberration analysis systems and methods

Abstract

Techniques are disclosed for systems and methods for providing monitoring of dry eye and / or tear film hydration in association with ocular aberration analysis. The ocular aberration analysis system (100) includes a wavefront sensor (120) configured to provide wavefront sensor data associated with an optical target (102) monitored by the ocular aberration analysis system (100), and a logic device (140) configured to communicate with the wavefront sensor (120). The logic device (140) is configured to receive (1102) ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor (120), determine (1106) a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data, and generate (1110) user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state.

Description

[Technical field]

[0001] One or more embodiments of the present disclosure relate generally to ocular aberration analysis and, more particularly, to systems and methods for improving ocular aberration analysis, for example, clinically or intraoperatively. [Background technology]

[0002] Ophthalmic surgery may involve reshaping the cornea and / or ocular surface, insertion and / or replacement of intraocular devices and / or artificial intraocular lenses (IOLs), and / or other surgical manipulation of the active optical components of the eye. To obtain optimal postoperative visual outcomes, good preoperative clinical evaluation and surgical planning, as well as intraoperative monitoring of the execution of the surgical plan, are important.

[0003] Ocular aberration analysis performed by an ocular aberrometer is a common technique typically used to characterize an eye preoperatively, to monitor the progress of surgery, and to evaluate the success of surgery. Conventional ocular aberrometers often suffer from various measurement errors related to misalignment and optical aberrations of eye movements and / or the aberrometer itself, which may lead to inaccurate surgical planning and suboptimal surgical or visual outcomes for the patient. Furthermore, conventional ocular aberrometers often incorporate measurement and characterization techniques that may introduce inaccuracies into the resulting aberration characteristics of the eye, thus complicating the retrieval of an accurate aberration analysis of the eye.

[0004] Thus, there is a need in the art for systems and methods for improving clinical and / or intraoperative ocular aberration analysis that leads to optimal surgical or visual outcomes for patients. Summary of the Invention [Means for solving the problem]

[0005] Techniques are disclosed for systems and methods for providing improved ocular aberration analysis. According to one or more embodiments, an ocular aberration analysis system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to determine a complex analysis engine for the ocular aberration analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, the aberrometer model and the eye model being based at least in part on wavefront sensor data provided by the wavefront sensor. The logic device may also be configured to generate a compact analysis engine for the ocular aberration analysis system based at least in part on the determined complex analysis engine.

[0006] In other embodiments, the method may include determining a complex analysis engine for the ocular aberration analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, the aberrometer model and eye model being based at least in part on wavefront sensor data provided by a wavefront sensor of the ocular aberration analysis system, and generating a compact analysis engine for the ocular aberration analysis system based at least in part on the determined complex analysis engine.

[0007] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions adapted, when executed by one or more processors, to cause the one or more processors to perform a method that may include determining a complex analysis engine for an ocular aberration analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, where the aberrometer model and the eye model are based at least in part on wavefront sensor data provided by a wavefront sensor of the ocular aberration analysis system, and generating a compact analysis engine for the ocular aberration analysis system based at least in part on the determined complex analysis engine.

[0008] In a further embodiment, the ocular aberration analysis system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to receive ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor, determine an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data, and generate user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment.

[0009] In other embodiments, a method may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, where the ocular aberration analysis output data includes at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; and generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment.

[0010] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions adapted, when executed by one or more processors, to cause the one or more processors to perform a method that may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, determining an estimated ocular alignment shift corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data, and generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment shift.

[0011] In a further embodiment, the ocular aberration analysis system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to receive ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor, identify singularities caused by image artifacts in the received ocular aberration analysis output data, determine corrected aberration analysis output data based at least in part on the identified singularities and the received ocular aberration analysis output data, and generate user feedback corresponding to the received ocular aberration analysis output data based at least in part on the corrected aberration analysis output data.

[0012] In other embodiments, a method may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; identifying singularities caused by image artifacts in the received ocular aberration analysis output data; determining corrected aberration analysis output data based at least in part on the identified singularities and the received ocular aberration analysis output data; and generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the corrected aberration analysis output data.

[0013] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions adapted, when executed by one or more processors, to cause the one or more processors to perform a method that may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, identifying singularities caused by image artifacts in the received ocular aberration analysis output data, determining corrected aberration analysis output data based at least in part on the identified singularities and the received ocular aberration analysis output data, and generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the corrected aberration analysis output data.

[0014] In further embodiments, the ocular aberration analysis system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to receive ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor, determine a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data, and generate user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state.

[0015] In other embodiments, a method may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; determining a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data; and generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state.

[0016] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions adapted, when executed by one or more processors, to cause the one or more processors to perform a method that may include receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system, determining a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data, and generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state.

[0017] The scope of the present invention is defined by the claims, which are incorporated by reference into this paragraph. A more complete understanding of the embodiments of the present invention, together with a realization of further advantages thereof, will be afforded to those skilled in the art by a consideration of the following detailed description of one or more embodiments. First, reference will be made to the accompanying drawings, which are briefly described. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 illustrates a block diagram of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 2A-2B] 2A-2B show block diagrams of an aberration analysis characterization target of an ocular aberration analysis system according to one embodiment of the present disclosure. [Diagram 3] FIG. 3 illustrates a block diagram of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 4] FIG. 4 illustrates a flow diagram of a process for characterizing an ocular aberration analysis system according to one embodiment of the present disclosure. [Diagram 5] FIG. 5 illustrates a flow diagram of an operational process of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 6] FIG. 6 shows a diagram of a multi-layer neural network according to one embodiment of the present disclosure. [Figure 7A] FIG. 7A illustrates a flow diagram of a process for reducing errors caused by image artifacts in an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 7B] FIG. 7B illustrates a flow diagram of a process for reducing errors caused by image artifacts in an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 7C] FIG. 7C illustrates a flow diagram of a process for reducing errors caused by image artifacts in an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 8] FIG. 8 illustrates a flow diagram of an operational process of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 9] FIG. 9 illustrates a flow diagram of a process for monitoring tear film hydration in an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 10] FIG. 10 illustrates a display view for monitoring tear film hydration of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 11] FIG. 11 illustrates a flow diagram of an operational process of an ocular aberration analysis system according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Embodiments of the present invention and their advantages are best understood by referring to the following detailed description, it should be understood that like reference numerals are used to identify like elements shown in one or more of the figures.

[0020] According to various embodiments, ocular aberration analysis systems and methods provide substantially real-time measurement and monitoring of a patient's ocular aberrations, reducing system and measurement errors typical of conventional systems. For example, a patient's eye naturally drifts when fixed during ocular aberration measurements or examination. This fixation-induced drift includes tip, tilt, and twist rotations of the eye as well as changes in x, y, and z positions. These alignment deviations contribute to errors in the calculation of the wavefront characterization aberrations of the eye. If the average of these alignment deviations is close to zero, averaging the wavefront aberration measurements from individual frames of the examination image stream may be sufficient to remove most of the errors due to misalignment. If it is known that the mean of the alignment mismatch for the test sequence is not near zero or if it is not possible to confirm that the alignment mismatch has a mean near zero, various strategies described herein can be used to reduce the measurement error or noise, including one or a combination of: averaging the wavefront sensor data when the alignment mismatch does not exceed a preset threshold (e.g., represented by a Zernike polynomial expansion), correcting for errors based on misalignment of the wavefront sensor data using complex analysis methods (described herein), determining an ocular fixation status (e.g., for each wavefront measurement) based on a cluster analysis applied to the series of wavefront measurements and / or based on eye tracker data. The estimated alignment mismatches and / or their effect on the wavefront measurements can be reduced relative to a metric of ocular alignment mismatch and can be provided to a user of the ocular aberration analysis system as a display view with corresponding graphics or can be used to cause the ocular aberration analysis system to ignore images from the test image sequence or to stop and / or restart the test. Thus, embodiments provide more reliable and accurate aberration analysis measurements in clinical and intraoperative ocular aberration analysis tests than conventional systems, including by reducing variability due to eye movement during test image sequences, while providing substantially real-time monitoring feedback.

[0021] In further embodiments of the present disclosure, ocular aberration analysis systems and methods provide platforms and techniques for accurately characterizing system aberrations and correcting wavefront sensor data that may be corrupted by alignment misalignments associated with a patient's eye. For example, the ocular aberration analysis systems described herein may employ one or more of two unique forms of calibration for accurate high order aberration (HOA) analysis, namely, a system characterization process and a complex analysis training process, to address errors that may be caused by system aberrations or occur during eye movements.

[0022] For the system characterization process, the ocular aberration analysis system may 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 substantially one type of variable aberration (e.g., defocus aberration) to the ocular aberration analysis system with substantially zero alignment misalignment. The reference interferograms may be used to characterize and / or quantify any system aberrations of a particular ocular aberration analysis system and may be used to correct, such as by removing, the wavefront sensor data provided by the wavefront sensor of that particular ocular aberration analysis system prior to subsequent analysis.

[0023] For the complex analysis training process, the ocular aberration analysis system may be used to capture a series of wavefront measurements generated for a model target configured to present a selection of different types and varying intensities of aberrations (e.g., up to sixth order Zernike expansion coefficients) to the ocular aberration analysis system with tip, tilt, and twist rotations of the aberration elements of the model target and variable misalignments in x, y, and z positions. The series of wavefront measurements may be used to train and / or improve a complex analysis engine executed by the ocular aberration analysis system as described herein and configured to generate substantially accurate estimated misalignments and / or corrected wavefront sensor data based on, for example, uncorrected wavefront sensor data or a combination of uncorrected wavefront sensor data and eye tracker data. Thus, the embodiments provide more reliable and accurate aberration analysis measurements than conventional systems, such as by increasing the accuracy of the aberration analysis measurements through reduction of errors resulting from system aberrations and aberration analysis measurements of an off-axis or distorted eye. Furthermore, embodiments provide a more robust (e.g., reliable and rapid) aberration analysis system by increasing the range of alignment misalignments that can be accurately compensated for or corrected for in a particular inspection and therefore included (e.g., present and / or detected within a given image of an inspection image sequence).

[0024] In further embodiments of the present disclosure, ocular aberration analysis systems and methods provide techniques for correcting wavefront sensor data that may be corrupted by image artifacts associated with a patient's eye. For example, to address errors that may result from imaging of vitreous gas bubbles and / or other image artifacts during the eye characterization process, the ocular aberration analysis systems described herein may decompose wavefront sensor data (e.g., a representation of an ocular interferogram) into a form that includes phase components (e.g., spatial phase components) that may be weighted to reduce or eliminate portions of the wavefront sensor data that contribute to errors.

[0025] In further embodiments of the present disclosure, ocular aberration analysis systems and methods provide techniques for monitoring tear film hydration (e.g., dry eye) during an aberration analysis examination to mitigate the undesirable effects of dehydration (e.g., relatively poor tear film hydration) on corresponding wavefront sensor data. For example, the ocular aberration analysis systems described herein may provide substantially real-time feedback to a clinician or surgeon, as described herein, to, for example, filter out wavefront sensor data corrupted by relatively poor tear film hydration, to prompt the patient to blink, to prompt the operator or surgeon to administer saline, and / or to provide other dry eye mitigation techniques.

[0026] 1 illustrates a block diagram of an ocular aberration analysis system 100 according to one embodiment of the present disclosure. In the embodiment illustrated in FIG. 1, ocular aberration analysis system 100 may be implemented to provide substantially real-time (e.g., 30 Hz updates) 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 variations, vibrations, and / or other characterization errors that degrade the ocular aberration analysis provided by ocular aberration analysis system 100.

[0027] 1, the ocular aberration analysis system 100 includes a beacon 110 that generates a probe beam 111 used to illuminate the optical target 102 of the wavefront sensor 120 and / or other elements of the ocular aberration analysis system 100. The ocular aberration analysis system 100 may also include various other sensor-specific beacons and / or light sources, such as a light emitting diode (LED) array 132 that is used to illuminate the optical target 102 of the eye tracker 130 and an OCT beacon 123 that is used to generate an OCT probe beam 124 for illuminating the optical target 102 of the OCT sensor 122. Beam splitters 112-116 are used to provide the probe beam 111 to the optical target 102 and to generate associated sensor beams 113, 115, 117 provided by the optical target 102 (e.g., the probe beam 111, the probe beam 124, and a portion of the light reflected by the optical target 102 generated by the LED array 132). The beacon 110, the OCT beacon 123, the LED array 132, and each sensor element of the ocular aberration analysis system 100 may be controlled by a control unit 140 (e.g., via communication links 141-144), which may also serve as an interface between the beacon 110, the OCT beacon 123, the LED array 132, and the sensor elements of the ocular aberration analysis system 100 and other elements of the ocular aberration analysis system 100, including a user interface 146, a server 150, a distributed server 154, and other modules 148 (e.g., accessed via any of the communication links 145, 149, and 155) as shown.

[0028] In a typical operation, the controller 140 activates one or more of the wavefront sensor 120, optional OCT sensor 122, and optional eye tracker 130, control beacon 110, OCT beacon 123, and / or LED array 132 to illuminate the optical target 102, and receives ocular aberration analysis output data (e.g., wavefront sensor data, eye tracker data, OCT sensor data) from various sensor elements of the ocular aberration analysis system 100. As described herein, the controller 140 may process the ocular aberration analysis output data itself (e.g., to detect or correct alignment misalignments and / or extract aberration analysis parameters from the wavefront sensor data) or may provide the ocular aberration analysis output data to the server 150 and / or distributed server system 154 (e.g., via the network 152) for processing. As described herein, controller 140 and / or server 150 may be configured to receive user input at user interface 146 (e.g., to control operation of ocular aberration analysis system 100) and / or generate user feedback, such as ocular aberration analysis output data and / or a display view of characteristics of the ocular aberration analysis output data, for display to a user via a display of user interface 146. 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 operation and / or characterization of ocular aberration analysis system 100, including, for example, machine learning and / or training of a complex analysis engine (e.g., a neural network based classification and / or regression engine) for characterizing system aberrations of ocular aberration analysis system 100, detecting alignment misalignments associated with optical target 102, and / or correcting wavefront sensor data and / or associated aberration classification coefficients. In various embodiments, the ocular aberration analysis system 100 may be configured to provide substantially real-time monitoring and user feedback (e.g., updates at a frequency of 30 Hz or greater) of the optical aberration analysis of the optical target 102 while continuously compensating for common characterization errors, as described herein.This makes the ocular aberration analysis system 100 particularly suitable for clinical and intra-operative examinations.

[0029] The beacon 110 may be implemented with a laser source (e.g., generating substantially coherent light) and / or a superluminescent diode (e.g., a “superluminescent diode (SLD)” generating relatively low coherence light) that may be controlled by the controller 140 to generate a probe beam 111 primarily for use with the wavefront sensor 120. The OCT beacon 123 may be implemented with a laser source and / or a superluminescent diode (e.g., generating relatively low coherence light that is particularly suitable for the OCT sensor 122) that may be controlled by the controller 140 to generate an OCT probe beam 124 primarily for use with the OCT sensor 122. In various embodiments, the OCT beacon may be integrated with the OCT sensor 122 as shown, for example, integrated with the beacon 110, and / or implemented as its own stand-alone beacon similar to the beacon 110 (e.g., using appropriately positioned beam splitters). The LED array 132 may be implemented by an array of shaped or patterned LEDs that may be controlled by the controller 140 to illuminate the target 102, primarily for use with the eye tracker 130. The beam splitters 112-116 may be implemented by any of a number of optical components (e.g., pellicle beam splitters, mirrors) configured to direct and / or pass the probe beam 111 to the optical target 102, and to deflect at least a portion of the probe beam 111 and / or source beam generated by the optical target 102 (e.g., the probe beam 111 or 124 and / or the reflected portion of the light emitted from the LED array 132) towards various sensor elements of the ocular aberration analysis system 100 to form sensor beams 113-117 (e.g., sensor beams). The optical target 102 may be, for example, a patient's eye, as described herein, or may be implemented by, for example, a single pass (probe beam 111 is turned off and optical target 102 generates its own illumination) or double pass (e.g., probe beam 111 is turned on in normal operation) model target.

[0030] The wavefront sensor 120 may 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 the sensor beam 117 caused by reflection of at least the probe beam 111 from the optical target 102, and the wavefront sensor 120 may be configured to provide associated wavefront sensor data. For example, the wavefront sensor 120 may be implemented as any one or combination of Shack-Hartmann wavefront sensors, phase-shifted Schlieren wavefront sensors, wavefront curvature sensors, pyramidal wavefront sensors, common-path interferometers, multi-faceted shearing interferometers, Ronchi testers, shearing interferometers, and / or other wavefront sensors that may be configured for use in ophthalmology. The wavefront sensor data provided by the wavefront sensor 120 may be represented in a variety of formats, including Zernike coefficients, e.g., Fourier transforms, cosine transforms, or Hartley transforms, or Taylor polynomials in cylindrical or Cartesian coordinates, or as an interferogram.

[0031] The OCT sensor 122 may be implemented with any one or combination of devices or device architectures configured to use relatively low coherence light and low coherence interferometry to capture micrometer and / or sub-micrometer resolution two-dimensional and three-dimensional images from within an optically scattering medium, such as the optical target 102, and provide associated OCT sensor data. For example, the OCT sensor 122 may be implemented as any one or combination of OCT sensor architectures that may be configured for use in ophthalmology. The eye tracker 130 may be implemented with any one or combination of devices or device architectures configured to track the orientation and / or position of the optical target 102 and / or features of the optical target 102 (e.g., retina, pupil, iris, cornea, lens), including a conventional eye tracker or a fundus camera, and configured to provide associated eye tracker data. In some embodiments, the eye tracker 130 may be configured to capture one or more types of images of the Purkinje reflection associated with the target 102.

[0032] Controller 140 may be implemented as any suitable logic device (e.g., a processing device, a microcontroller, a processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a memory storage device, a memory reader, or other device or combination of devices that may be adapted to execute, store, and / or receive suitable instructions, such as, for example, software instructions implementing control loops or processes for controlling various operations of ocular aberration analysis system 100 and / or elements of ocular aberration analysis system 100). Such software instructions may also implement methods for processing sensor signals, determining sensor information, providing user feedback (e.g., via user interface 146), querying a device for operating parameters, selecting operating parameters for a device, or performing any of the various operations described herein (e.g., operations performed by logic devices of the various devices of ocular aberration analysis system 100).

[0033] Additionally, a machine-readable medium may be provided for storing non-transitory instructions for loading into and executing by the controller 140. In these and other embodiments, the controller 140 may be implemented with other components, such as volatile memory, non-volatile memory, one or more interfaces, and / or various analog and / or digital components for interfacing with devices of the ocular aberration analysis system 100, as appropriate. For example, the controller 140 may be adapted to store, for example, sensor signals, sensor information, complex analysis parameters, calibration parameters, a series of calibration points, training data, reference data, and / or other operating parameters over time, and provide such stored data to other elements of the ocular aberration analysis system 100. In some embodiments, the controller 140 may be integrated with a user interface 146.

[0034] The user interface 146 may be implemented as one or more of a display, a touch screen, a keyboard, a mouse, a joystick, a knob, a virtual reality headset, and / or any other device capable of accepting user input and / or providing feedback to a user. In various embodiments, the user interface 146 may be adapted to provide user input to other devices of the ocular aberration analysis system 100, such as the control unit 140. The user interface 146 may also be implemented by one or more logic devices, which may be adapted to execute instructions, such as software instructions, that perform any of the various processes and / or methods described herein. For example, the user interface 146 may be adapted to form a communication link, 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.

[0035] In some embodiments, the user interface 146 may be adapted to accept user input, for example, to form a communication link (e.g., to the server 150 and / or the distributed server system 154), to select certain parameters for operation of the ocular aberration analysis system 100, to select a manner of processing the sensor data, to adjust the position and / or orientation of the articulated model target, and / or to otherwise facilitate operation of the ocular aberration analysis system 100 and devices therein. Once the user interface 146 accepts the 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 including graphs or maps of such data and / or parameters. In some embodiments, the user interface 146 may be adapted to accept the user input and, for example, to modify a control loop or process parameter of the controller 140 or a control loop or process parameter of any other element of the ocular aberration analysis system 100.

[0036] Other module 148 may include any one or combination of sensors and / or devices configured to facilitate operation of ocular aberration analysis system 100. For example, other module 148 may include a temperature sensor configured to measure one or more temperatures associated with operation of one or more elements of ocular aberration analysis system 100, a humidity sensor configured to measure ambient humidity around ocular aberration analysis system 100, a vibration sensor configured to measure amplitude and / or presence associated with operation of ocular aberration analysis system 100, a patient sensor configured to measure a posture, movement or other characteristic of a patient-supplied optical target 102, and / or other sensors that may provide sensor data useful for facilitating operation of ocular aberration analysis system 100 and / or correcting common system errors typical to operation of ocular aberration analysis system 100. In further embodiments, other module 148 may include additional illumination and camera systems configured to capture images of one or more types of Purkinje reflexes associated with target 102, similar to the combination of LED array 132 and eye tracker 130.

[0037] The server 150 may be implemented, for example, similarly to the controller 140 and may include various elements of a personal computer or server computer used to store, process, and / or otherwise manipulate relatively large data sets associated with one or more patients, including relatively large training data sets for training neural networks or performing other types of machine learning, as described herein. The distributed server system 154 may be implemented, for example, as a distributed combination of multiple embodiments of the controller 140 and / or the server 150 and may include networking and storage devices and capabilities configured to facilitate the storage, processing, and / or other manipulation of relatively large data sets, including relatively large training data sets for training neural networks or performing other types of machine learning, in a distributed setting, as described herein. The network 152 may be implemented as one or more of a wired and / or wireless network, a local area network, a wide area network, the Internet, a cellular network, and / or according to other network protocols and / or topologies.

[0038] 2A-B show block diagrams of aberration analysis characterization targets 202A-B of the ocular aberration analysis system 100 according to one embodiment of the present disclosure. In the embodiment shown in FIG. 2A, the aberration analysis characterization target 202A may be implemented as a single-pass model target configured to characterize optical aberrations associated with the ocular aberration analysis system 100. As shown in FIG. 2A, the aberration analysis characterization target / single-pass model target 202A includes a laser or SLD source 260 generating a source beam 211 that passes through a lens system 262 and is directed 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, the source 260 may be configured to generate, for example, a diverging spherical wavefront with controllable dispersion power, a converging spherical wavefront with controllable dispersion power, or a plane wave with zero power. Lens system 262 is coupled to a linear motion actuator 264 via mount 265, which allows lens system 262 to move along its optical axis to vary the defocus aberration of single pass model target 202A and generate a plurality of reference interferograms and corresponding wavefront sensor data (e.g., provided by wavefront sensor 120). Such reference interferograms and / or corresponding wavefront sensor data may be collected and stored, for example, as aberrometer model 360 of FIG. 3, and used to correct system aberrations associated with ocular aberration analysis system 100, as described herein.

[0039] In some embodiments, lens system 262 may be implemented as a lens traceable by the National Institute of Standards and Technology (NIST), and linear motion actuator 264 may be implemented as a relatively high precision actuator stage configured to position lens system 262 at a series of spaced positions from source 260 to generate source beam 211 having known and predefined defocus powers (e.g., defocus aberrations), such as, for example, −12 to 6 diopters or 0 to +−5 diopters, in 5.0D steps or higher resolution steps, according to various defocus aberrations commonly experienced by patients, as observed by ocular aberration analysis system 100. The resulting aberrometer model 360 may be used to compensate for various system aberrations, including those due to shot noise, thermal variations, and vibrations.

[0040] As shown in FIG. 2B, the aberration analysis characterization target / double-pass model target 202B includes an exchangeable ocular aberration model 270 removably 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 vary the position and / or orientation of the exchangeable ocular aberration model 270 to generate a plurality of selected (e.g., known) alignment misalignments (e.g., relative to an optical axis of the ocular aberration analysis system 100) and a corresponding plurality of wavefront sensor data sets. Such alignment misalignments and corresponding wavefront sensor data may be collected and stored, for example, as the eye model 370 of FIG. 3, and may be used to train a complex or compact analysis engine to detect alignment misalignments associated with the optical target 102 and correct the corresponding wavefront sensor data, as described herein.

[0041] In some embodiments, the interchangeable ocular aberration model 270 may form one element of a series of interchangeable ocular aberration models, each formed by an exact amount of predefined ocular aberration (e.g., represented by exact and predefined Zernike coefficient amplitudes, such as amplitudes expressed in microns for a sixth order Zernike expansion). In one embodiment, such an interchangeable ocular aberration model may be cut on a contact lens lathe using transparent poly(methyl methacrylate) (PMMA). More generally, such an interchangeable ocular aberration model may be measured by a third party profiler with traceability to NIST for ground truth comparison with measurements performed by the ocular aberration analysis system 100. In various embodiments, the 6DOF motion actuator 272 may be configured to provide micrometer resolution positioning of the interchangeable ocular aberration model 270 along the x-, y-, and z-axes and microradian orientation of the interchangeable ocular aberration model 270 in the θy (tip), θx (tilt), and θz (twist) directions, as shown by representative coordinate frames 280A-B.

[0042] In general operation, each interchangeable ocular aberration model 270 in a set (e.g., a set of 10 or more) may be further attached to a 6DOF motion actuator 272, and the controller 140 may be configured to control the 6DOF motion actuator 272 to position and / or orient the interchangeable ocular aberration model 270 in a series of relative positions and / or orientations (e.g., relative to the optical axis of the ocular aberration analysis system 100) within a range of misalignments typically experienced by patients as observed by the ocular aberration analysis system 100. In one embodiment, the series of misalignments may include approximately 40,000 different misalignments. In various embodiments, the combined series of misalignments and the corresponding series of wavefront sensor data (e.g., provided by the wavefront sensor 120) may form a supervised data set (e.g., eye model 370), which may be used to determine a complex analysis engine or a compact analysis engine, as described herein. Such an analysis engine may be used to compensate for misalignment of the optical target 102, which may also be measured by the eye tracker 130. More generally, the combination of the characterizations performed by the aberration analysis characterization targets 202A-B may be used to compensate or correct both system aberrations and errors in the wavefront sensor data due to misalignment of the optical target 102.

[0043] Fig. 3 illustrates a block diagram of an ocular aberration analysis system 300 according to one embodiment of the present disclosure. In the embodiment illustrated in Fig. 3, the ocular aberration analysis system 300 may be configured to use characterization data generated by the ocular aberration analysis system 100 via the aberration analysis characterization targets 202A-B to generate a complex analysis engine 350 and / or a compact analysis engine 340, which may be used during operation of the ocular aberration analysis system 100 to provide substantially real-time monitoring and user feedback (e.g., updates at a frequency of 30 Hz or greater) of the optical aberration analysis of the optical target 102 while continuously compensating for common characterization errors, as described herein.

[0044] 3, ocular aberration analysis system 300 is similar to ocular aberration analysis system 100, but with additional details regarding various data structures and executable program instructions used in the operation of ocular aberration analysis system 100 or ocular aberration analysis system 300. For example, controller 140 is shown implemented with compact analysis engine 340, and server 150 and distributed server system 154 are each shown implemented with or storing one or more of aberrometer model 360, eye model 370, training data 392, supervised learning engine 390, complex analysis / neural network engine 350, and compact analysis engine 340. Although dashed lines generally indicate optional storage and / or implementation of certain elements, in various embodiments, controller 140, server 150, and distributed server system 154 may each implement or store any of the identified elements and / or additional elements, as described herein.

[0045] 6, the aberrometer model 360 may be generated by collecting sensor data associated with use of the single pass model target 202A and characterizing the ocular aberration analysis system 100, the eye model 370 may be generated by collecting sensor data associated with use of the double pass model target 202B and characterizing a parameter space associated with the optical target 102, and the training data 392 may be generated by incorporating the aberrometer model 360 and / or the eye model 370 and / or by generating and collecting simulation training data sets. The supervised learning engine 350 may be implemented as a static learning engine and / or according to a procedurally generated learning engine (e.g., a learning engine updatable with a genetic algorithm) configured to generate the complex analysis engine 350 using the training data 392. The complex analysis engine 350 may be implemented using other complex analysis techniques, including, for example, a deep neural network and / or various other neural network architectures or complex analysis techniques, including dense K-Nearest Neighbor (k-NN) databases for classification and / or regression, as described herein. The compact analysis engine 340 may be implemented as a compact form of the complex analysis engine 350, such as a form more suitable for relatively low resource but high performance execution by the control unit 140, and thus may be implemented as a deep neural network and / or other complex analysis techniques. In certain embodiments, the compact analysis engine 340 may be implemented as a neural network having fewer hidden layers and / or neurons / layers than the complex analysis engine 350.

[0046] FIG. 4 illustrates a flow diagram of a process 400 for characterizing the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of the process 400 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 4. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, the inputs of the blocks, the outputs of the blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although the process 400 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-3, the process 400 may be performed by other systems that are different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of the mobile structures.

[0047] In block 402, an aberrometer model associated with the ocular aberration analysis system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to control a source 260 of a single-pass model target 202A positioned as an optical target 102 observed by the ocular aberration analysis system 100 to generate a source beam 211 that passes through a lens system 262 and illuminates the wavefront sensor 120 and / or other elements of the ocular aberration analysis system 100. The controller 140 may be configured to vary a defocus aberration of the single-pass model target 202A according to a number of selected defocus powers, for example, to generate a number of wavefront sensor data sets provided by the wavefront sensor 120. The controller 140, the server 150, and / or the distributed server system 154 may be configured to determine system aberrations associated with the ocular aberration analysis system 100 based at least in part on the number of wavefront sensor data sets provided by the wavefront sensor 120. The system aberrations and / or the associated wavefront sensor data set may be stored (eg, on server 150 and / or distributed server system 154) as an aberrometer model 360.

[0048] In block 404, an eye model associated with the ocular aberration analysis system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to control the beacon 110 of the ocular aberration analysis system 100 to generate the probe beam 111 to illuminate the double-pass model target 202B positioned as the optical target 102 observed by the ocular aberration analysis system 100, and further to illuminate (e.g., by reflection of the probe beam 111) one or more of the wavefront sensor 120, the eye tracker 130, the OCT sensor 122, and / or other elements of the ocular aberration analysis system 100. The controller 140 may be configured to, for example, vary the position and / or orientation of the exchangeable ocular aberration model 270 of the double-pass model target 202B relative to the optical axis 111 of the ocular aberration analysis system 100 according to a number of selected alignment misalignments to generate a number of wavefront sensor data sets provided by the corresponding wavefront sensors 120. The selected misalignments and / or the corresponding wavefront sensor data sets may be stored (e.g., in the server 150 and / or the distributed server system 154) as an eye model 370. Similar techniques may be used to incorporate the eye tracker data from the eye tracker 130 and the OCT sensor data from the OCT sensor 122 into the eye model 370.

[0049] At block 406, a complex analysis engine is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the complex analysis engine 350 based at least in part on the aberrometer model 360 generated at block 402 and / or the eye model 370 generated at block 404. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to form a deep neural network 600 including an input layer 620, an output layer 640, and at least one hidden layer 630-639 coupled between the input layer 620 and the output layer 640, each hidden layer including a plurality of neurons. The controller 140, server 150, and / or distributed server system 154 may be configured to train at least a trainable weight matrix W associated with each neuron of the input layer, output layer, and hidden layer of the neural network 600 via the supervised learning engine 390 using the alignment misalignments of the eye model 370 as ground truth output data and the corresponding wavefront sensor data set of the eye model 370 as training input data, as described herein. The resulting deep neural network may be stored and used as the complex analysis engine 350. In other embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to generate a plurality of corrected wavefront sensor data sets corresponding to a plurality of selected alignment misalignments of the eye model 370 based at least in part on system aberrations associated with an ocular aberration analysis system in the aberrometer model 360, before forming the neural network 600 and training one or more complex analysis parameters of the neural network 600 using the supervised learning engine 390 to determine the complex analysis engine 350, as described herein.

[0050] At block 408, a compact analysis engine is generated. For example, the controller 140, the server 150, and / or the 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 coupled between the input layer 620 and the output layer 640, and generate a weight matrix W associated with each neuron of the input layer, the output layer, and / or the hidden layer of the compact neural network 600 based at least in part on one or more complex analysis parameters associated with the multiple hidden layers 630-639 of the complex analysis engine 350. Once generated, the compact analysis engine 340 may be stored or integrated or implemented by the controller 140. The controller 140 may use the compact analysis engine 340 to generate user feedback (e.g., displaying a view including various graphics) and reliable and accurate monitoring of ocular alignment deviations, ocular aberrations, and / or other characteristics of the optical target 102 in substantially real time (e.g., 30 frames / second), as described herein.

[0051] FIG. 5 illustrates a flow diagram of a process 500 for operating the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of the process 500 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 5. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, the inputs of the blocks, the outputs of the blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although the process 500 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-3, the process 500 may be performed by other systems that are different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of the mobile structures.

[0052] At block 502, an output of the ocular aberration analysis is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive ocular aberration analysis output data that includes at least wavefront sensor data provided by wavefront sensor 120. More generally, the ocular aberration analysis output data may include any one or more of wavefront sensor data provided by wavefront sensor 120, OCT sensor data provided by OCT sensor 122, eye tracker data provided by eye tracker 130, and / or other output data provided by ocular aberration analysis system 100, as described herein.

[0053] In block 504, an estimated ocular alignment misalignment is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target 102 (e.g., the patient's eye) observed by the ocular aberration analysis system 100 based at least in part on the ocular aberration analysis output data received in block 502. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment misalignment based at least in part on the 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 the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the estimated ocular alignment misalignment by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment from a wavefront corresponding to the wavefront sensor measurement.

[0054] For example, in one embodiment, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment shift by: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target 102 (e.g., an estimated ocular alignment of the optical target 102); identifying one or more clusters of the estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-set or adaptive cluster thresholds; determining a fixed alignment based at least in part on the centroid of a largest cluster among the one or more identified clusters; and determining, for each wavefront sensor measurement, an estimated ocular alignment shift based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement (e.g., a difference between the fixed alignment and an estimated ocular alignment of the optical target 102 corresponding to a time series of wavefront sensor measurements). In a related embodiment, the ocular aberration analysis system 100, including the eye tracker 130, the controller 140, the server 150, and / or the distributed server system 154, may be configured to determine an estimated ocular alignment misalignment by determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and the eye tracker sensor data, and filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status before determining a corresponding estimated relative position and / or orientation of the optical target 102. For example, such an approach may filter out wavefront sensor measurements obtained while the optical target 102 is detected by the eye tracker 130 as not being fixed.

[0055] In block 506, the corrected ocular aberration analysis output data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment determined in block 504 and / or the wavefront sensor data received in block 502. In some embodiments in which the wavefront sensor data comprises a time series of wavefront sensor measurements, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with the 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 with an associated estimated ocular alignment misalignment less than or equal to a preset maximum allowable misalignment. Such a preset maximum allowable misalignment may be selected or set by a manufacturer and / or a user of the ocular aberration analysis system 100.

[0056] In other embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with the wavefront sensor measurement, and for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum allowable misalignment, determining a corrected wavefront sensor measurement based at least in part on the wavefront sensor measurement and / or the associated estimated ocular alignment misalignment, and generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected wavefront sensor measurement by applying the complex analysis engine 350 or the compact analysis engine 340 of the ocular aberration analysis system 100 to each wavefront sensor measurement and generating an estimated ocular alignment misalignment and / or a corrected wavefront sensor measurement from the corresponding wavefront, as described herein.

[0057] In block 508, user feedback is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback corresponding to the ocular aberration analysis output data received in block 502 based at least in part on the estimated ocular alignment misalignment determined in block 504. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback by determining a metric of the ocular alignment misalignment based at least in part on the estimated ocular alignment misalignment and reporting the metric of the ocular alignment misalignment via the user interface 146 of the ocular aberration analysis system 100. In other embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback based at least in part on the estimated ocular alignment misalignment determined in block 504 and / or the corrected ocular aberration analysis output data determined in block 506. In various embodiments, such user feedback may include a substantially real-time display view of an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment determined in block 504, as described herein, a substantially real-time display view of an ocular aberration map based at least in part on the corrected ocular aberration analysis output data determined in block 504 and / or an audible and / or visual alarm indicating that at least one of the estimated ocular alignment misalignments is greater than a pre-set maximum allowable misalignment.

[0058] Thus, as described herein, embodiments of the present disclosure can provide user feedback (e.g., displaying views including various graphics) and reliable and accurate monitoring in substantially real-time (e.g., 30 frames / second) of ocular alignment misalignments, ocular aberrations, and / or other characteristics of the optical target 102. Such embodiments can be used to assist in various types of clinical and intra-operative eye examinations and help improve surgical outcomes.

[0059] FIG. 6 illustrates a diagram of a multi-layer or “deep” neural network (DNN) 600 according to an embodiment of the present disclosure. In some embodiments, the neural network 600 may represent a neural network used to implement each of the one or more models and / or analytical engines described with respect to system 100 and / or system 300. The neural network 600 processes input data 610 using an input layer 620. In various embodiments, the input data 610 may correspond to aberration analysis output data and / or training data provided to one or more models and / or analytical engines to generate and / or train one or more models and / or analytical models, as described herein. In some embodiments, the input layer 620 may include multiple neurons or nodes used to condition the input data 610 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning the input data 610 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., if the input data 610 has already been appropriately scaled, biased, filtered, range-limited, and / or otherwise adjusted). Each neuron in the input layer 620 generates an output that is provided to a neuron / node in the hidden layer 630. The hidden layer 630 includes multiple neurons / nodes that process the output from the input layer 620. In some embodiments, each of the neurons in the hidden layer 630 generates an output that is then propagated through one or more additional hidden layers terminating in the hidden layer 639. The hidden layer 639 includes multiple neurons / nodes that process the output from the previous hidden layer. In the embodiment shown in FIG. 6, the output of the hidden layer 639 is provided to the output layer 640. In various embodiments, the output layer 640 includes one or more neurons / nodes that may be used to adjust the output from the hidden layer 639 by scaling, biasing, filtering, range-limiting, and / or otherwise adjust the output from the hidden layer 639 to form the output data 650.In alternative embodiments, neural network 600 may 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 various other neural network architectures including K-Nearest Neighbor (k-NN) databases or complex analysis techniques for classification and / or regression.

[0060] In some embodiments, the input layer 620, the hidden layers 630-639, and / or the output layer 640 each include one or more neurons. In one embodiment, the input layer 620, the hidden layers 630-639, and / or the output layer 640 each may include the same or different numbers of neurons. In a particular embodiment, the neural network 600 may include about 6 layers in total with up to 2000-4000 neurons in each layer. In various embodiments, each such constituent neuron may be configured to receive a combination of its inputs x (e.g., a weighted sum generated using a trainable weight matrix / vector W), to receive any trainable bias b, and to apply an activation function f to generate an output a, e.g., according to the formula a=f(Wx+b). The activation function f may be implemented, for example, as a normalized linear unit activation function, or according to any one or combination of activation functions with upper and / or lower bounds, a logistic sigmoid function, a hyperbolic tangent function, and / or other activation function forms. As described herein, 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. In certain embodiments, corresponding to regression applications, only the neurons in the output layer 640 may be configured to apply such linear activation functions to generate their respective outputs.

[0061] In various embodiments, the neural network 600 may be trained using supervised learning (e.g., implemented as a supervised learning engine 390) such as by systematically providing selected training data sets (e.g., training data 392) (each training data set including an input training data set and a corresponding set of ground truth (e.g., expected) output data (e.g., a combination of the aberrometer model 360 and the eye model 370) to the neural network 600, and then determining and / or otherwise comparing the difference (e.g., "training error") between the resulting output data 650 (e.g., the training output data provided by the neural network 600) and the ground truth output data. 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 analytical parameters of the neural network 600. In some embodiments, such training error may be provided as feedback to the neural network 600 using, for example, one or various backpropagation and / or other backpropagation methods including stochastic gradient descent. In one or more embodiments, a relatively large set of selected training datasets may be presented to neural network 600 multiple times until an overall loss function (e.g., mean squared error based on the difference between each training dataset) converges to or below a pre-set maximum tolerable loss threshold.

[0062] In further 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 methods to aid in training the complex analysis parameters of the neural network 600 (e.g., to generate the complex analysis engine 350), as described herein. For example, the supervised learning engine 390 may be configured to generate simulation training data sets, each set including simulation input training data and a corresponding simulation ground truth output data set, and to perform supervised learning based at least in part on the simulation training data sets. Each of the simulation input training data set and the simulation ground truth data set may be generated by modifying the input training data (e.g., to adjust aberration parameters and / or alignment misalignments associated with the simulation double-pass model target) and interpolating non-simulated ground truth data to generate the corresponding simulation ground truth data. In various embodiments, the supervised learning engine 390 may be configured to simulate millions of sets of simulated training data (each of which deviates at least slightly from the training data set corresponding to the eye model 370) and train the neural network 600 according to the millions of such sets of simulated training data.

[0063] In various embodiments, the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 may be configured as a phase-based ocular aberration analysis system. In such embodiments, one of the processing steps of such a phase-based ocular aberration analysis system generates a wrapped phase array as part of the representation of the wavefront sensor data provided by the wavefront sensor 120. Typically, wrapped phase arrays have true values ​​limited to the range [-π to π] or [0 to 2π], e.g., as a natural result of calculating phase from complex numbers using an arctangent function. These wrapped phase values ​​are then "unwrapped" to reveal the true range of phase values. The phase values ​​are generally proportional to the first wavefront aberration derivative and may be used to determine wavefront aberration values ​​for wavefront aberration characterization.

[0064] To generate an accurate final set of wavefront aberration values ​​(e.g., Zernike polynomial coefficients), the resulting unwrapped phase array should be nearly continuous (e.g., corresponds to a continuous function). However, for various reasons, such as image artifacts (in the initial interferogram captured by the wavefront sensor) due to vitreous air bubbles, a typical unwrapped phase array may contain various "singularities" or discontinuities that may not be continuous and lead to errors in the final calculated aberration fit. In general, such an aberration fit is usually a Zernike polynomial expansion, although other functional aberration characterizations are possible. Due to the manner in which the Zernike polynomial coefficients are fitted to the wavefront sensor data in the scaled and unwrapped phase array, local singularity errors (e.g., where the unwrapped phase is not continuous) may cause errors throughout the final aberration characterization results. Some kind of restoration may be applied in an attempt to recover the true phase, but such restoration processes are often unsatisfactory as the "restored" data points do not accurately reflect the true wavefront sensor data and may introduce further errors. In the embodiments disclosed herein, rather than attempting this type of restoration, a linear system of weighted normal equations is applied to the wavefront sensor data represented by the unwrapped phase array, as the Zernike polynomial coefficients are fitted to the data in a least squares error sense. The weights of the individual rows of the linear system of equations are selected to reduce or eliminate the effect of each singularity.

[0065] In one embodiment, the weights may be selected as follows: Let d be the shortest distance from a point in the unwrapped phase array to an identified singularity. The weight of this point is 0 if the distance d is less than or equal to a threshold D0, 1 if the distance d is greater than or equal to a threshold D1, and [(d-D0) / (D1-D0)]^p if the distance d is between D0 and D1. In other words, the weight of each point is zero for elements less than or equal to a threshold distance D0 from any identified singularity, 1 for elements more than or equal to a threshold distance D1 from all identified singularities, and a function of a scaling parameter p for all elements between the shortest distance (the threshold) and two threshold distances from the identified singularity. In formula form:

number

[0066] The hyper-parameters D0, D1, and p may be optimized depending on the application, but preferred values ​​are D0=P*0.05, D1=P*0.1, and p=2, where the value P is the diameter of the pupil to be characterized / reconstructed. In various embodiments, such a weighted fit, as described herein, reduces variability in the phase-based ocular aberration analysis test due to singularities in the wavefront sensor data, without the risk of introducing new errors, and further improves the precision and accuracy of the resulting ocular aberration analysis output data (e.g., characterization of ocular aberrations).

[0067] 7A illustrates a flow diagram of a process 700 for reducing errors caused by image artifacts in an ocular aberration analysis system according to one embodiment of the present disclosure. In the embodiment illustrated in FIG. 7A, initial wavefront sensor data in the form of an interferogram 710 of a human eye (e.g., including a plurality of approximately concentric fringe lines / patterns 712) is analyzed by filtering at least a wrapped phase component / array 724 (Φ i ) such that the wrapped phase wavefront sensor data 720 forms a discrete representation of the interferogram 710. i )), which may be similar to a discrete spatial Fourier transform of the interferogram 710. The wrapped phase wavefront sensor data 720 may be approximately 2π in magnitude (or a multiple thereof) and / or may include a wrapped phase array 724 (Φ i ) near the artificial numerical boundaries (e.g., +-π or 0 and 2π). i ) and identifies discontinuities (e.g., between adjacent elements) in the wrapped phase array 724 (Φ i ) by adding 2π to the elements of the wrapped phase array 724 (Φ i) to remove discontinuities and obtain the unwrapped phase array 734 (Φ i ') to form at least the unwrapped phase array 734 (Φ i The wavefront sensor data 730 may be converted (eg, step 704) into unwrapped phase wavefront sensor data 730 including the phase shift vector α, β ...

[0068] Once the unwrapped phase wavefront sensor data 730 has been determined, singularities or discontinuities due to image artifacts (e.g., associated with a vitreous air bubble 714 imaged as part of the interferogram 710) are filtered out from the unwrapped phase array 734 (Φ i Such a predetermined singularity threshold may be determined based on the various types of image noise in the interferogram 710 and / or the unwrapped phase array 734 (Φ ′), or by identifying any remaining discontinuities in the unwrapped phase wavefront sensor data 730. i ') to account for expected gradients between adjacent elements.

[0069] Such discontinuities (e.g., unwrap phase array 734 (Φ i Once identified as an element of the wavefront sensor data 730 of the unwrapped phase), the distance between any element of the wavefront sensor data 730 of the unwrapped phase and such a discontinuity can be determined, as described herein, and w iA fitting weight for each element of the unwrapped-phase wavefront sensor data 730 may be determined, such as by using a linear system of weighted normal equations in (1). In some embodiments, such weights may be calculated only for the nearest singularity (e.g., as a value of d), or may be calculated for each detected singularity, and the resulting set of weights combined (e.g., by multiplication) to determine the fitting weight for that element of the unwrapped-phase wavefront sensor data 730. The aberration characterization fit 740 (e.g., to the aberration analysis output data) may include fitting a fitting function (e.g., a selected aberration characterization function, such as a Zernike polynomial expansion of a selected order), the determined weights w i and unwrapped phase wavefront sensor data 730 (step 706), and an ocular aberration map 750 may be generated (step 708) to visualize the aberration characterization fit 740 and identify spatial regions 752-754 of the test eye that deviate from the ideal eye.

[0070] In various embodiments, interferogram 710 and / or ocular aberration map 750 may be rendered on a display of user interface 146 and form at least a part of a display view (e.g., display view 718 and / or display view 758) that is displayed to a user as user feedback regarding the operation of ocular aberration analysis system 100 and / or ocular aberration analysis system 300. In general operation, diameter 716 of interferogram 710 and diameter 756 of ocular aberration map 750 may be configured to approximately represent or correspond to the diameter of the pupil of the test eye.

[0071] 7B shows a flow diagram of a process 700B for reducing errors caused by image artifacts in an ocular aberration analysis system according to an embodiment of the present disclosure, similar to process 700 of FIG. 7A. In the embodiment shown in FIG. 7B, initial wavefront sensor data in the form of an interferogram / fringe pattern 710B is generated by at least a wrapped phase component / array (e.g., wrapped phase component / array 724 of FIG. 7A (Φ i)) to generate transformed wavefront sensor data 720B. The transformed wavefront sensor data 720B may include multiple spatial peaks with corresponding spatial structure neighborhoods 723B-1, 723B-2. In some embodiments, the spatial structure neighborhoods 723B-1, 723B-2 may be selected to be circular neighborhoods within the transformed wavefront sensor data 720B, with their radii selected to include non-overlapping spatial structures, e.g., with respect to the order of one or more selected aberration characterization functions (e.g., the order of the Zernike polynomial expansion), as shown. In some embodiments, each spatial structure neighborhood 723B-1, 723B-2 may be processed separately to determine a corresponding set of wrapped-phase wavefront sensor data 724B and a corresponding set of wrapped-phase wavefront sensor data 726B, as shown. In the particular embodiment shown in Figure 7B, the spatial structure neighborhood 723B-1 is processed into a first wrapped-phase dW / dx array and the spatial structure neighborhood 723B-1 is processed into a second wrapped-phase dW / dy array.

[0072] The wrapped phase wavefront sensor data 724B and the wrapped phase wavefront sensor data 726B are fed to at least an unwrapped phase array (e.g., wrapped phase component / array 734 (Φ iOnce the unwrapped phase wavefront sensor data 734B, 736B is determined, singularities or discontinuities 764 (e.g., due to image artifacts in the interferogram 710B) may be identified in the unwrapped phase wavefront sensor data 734B, 736B to generate processed unwrapped phase wavefront sensor data 744B, 746B. Once such discontinuities 764 are identified (e.g., as elements of a corresponding unwrapped phase array), a distance between any element of the unwrapped phase wavefront sensor data 734B, 736B and such discontinuities may be determined, as described herein, to generate processed unwrapped phase wavefront sensor data 744B, 746B. i A fitting weight for each element of the unwrapped-phase wavefront sensor data 734B, 736B may be determined, such as by equation (1) for (i) = (i,j,j,i,j,i,j,i) In particular, the processed unwrapped-phase wavefront sensor data 744B, 746B includes, for example, darker lines 764 indicating singularities in the processed unwrapped-phase wavefront sensor data 744B, 746B, and halos 766 around the discontinuities 764 indicating local weight variances from one.

[0073] The aberration characterization fit (e.g., aberration analysis output data) may be determined by fitting a fitting function (e.g., a selected aberration characterization function, such as a Zernike polynomial expansion of a selected order or range of orders), determined weights w iand processed unwrapped-phase wavefront sensor data 744B, 746B (e.g., processed forms of the first wrapped-phase dW / dx array and the second wrapped-phase dW / dy array), and an ocular aberration map 750B may be generated (step 706B / 708B) to visualize the aberration characterization fit and identify various spatial regions of the subject eye that deviate from the ideal eye (e.g., shown as gradients color-mapped according to a selected palette). An uncorrected ocular aberration map 790B (e.g., showing significantly different spatial gradients and aberration distributions) obtained directly from the unwrapped-phase wavefront sensor data 734B, 736B (step 709B) without weighting and according to equation (2) is provided in FIG. 7B for comparison.

[0074] In various embodiments, interferogram 710B and / or ocular aberration map 750B may be rendered on a display of user interface 146, as shown, together with or separately from any one or combination of intermediate step display views, such as display view 728B, display view 729B, display view 738B, display view 739B, and / or display view 798B, and form at least a portion of a display view (e.g., display view 718B and / or display view 758B) that is displayed to a user as user feedback regarding the operation of ocular aberration analysis system 100 and / or ocular aberration analysis system 300. In general operation, diameter 716B of interferogram 710B and diameter 756B of ocular aberration map 750B may be configured to approximately represent or correspond to a diameter of a pupil of the test eye.

[0075] FIG. 7C shows a flow diagram of a process 700C for reducing errors caused by image artifacts in an ocular aberration analysis system according to an embodiment of the present disclosure, which includes a portion of the process 700 of FIG. 7A and / or the process 700B of FIG. 7B. In particular, the process 700C shows a spatially exaggerated version of the approach used in steps 705B-1 and 705B-2 to determine and apply weights to the unwrapped-phase wavefront sensor data 734B, 736B, taking into account the discontinuity 764. For example, the unwrapped-phase wavefront sensor data 734C is shown, including the discontinuity contour 764C and the nearest discontinuity point 766C (e.g., the point used to fit the Zernike polynomial expansion) to the elements / points 744-1, 744-2, 744-3 of the unwrapped-phase wavefront sensor data 734C. Also shown in FIG. 7C is a circle 740C-0. The circle 740C-0 includes / distinguishes all points that are less than or equal to a threshold distance / radius D0 from the nearest discontinuity 766C of the discontinuity contour 764C, and the circle 740C-1 excludes / distinguishes all points that are more than or equal to a threshold distance D1 from the nearest discontinuity 766C of the discontinuity contour 764C. A line 746C may be perpendicular to the discontinuity contour 764C at the nearest discontinuity 766C and passes through elements / points 744-1, 744-2, 744-3 of the unwrapped phase wavefront sensor data 734C. Thus, according to equation (1) herein, the weight of element 744-1 is zero (e.g., corresponding to discontinuity 764), the weight of element 744-2 is between zero and one (e.g., corresponding to halo 766), and the weight of element 744-3 is one (e.g., remaining portion of the unwrapped phase wavefront sensor data 736B).

[0076] In various embodiments, process 700C, together with or separately from any one or combination of the other display views described herein, may be rendered on a display of user interface 146 to form at least a portion of a display view (e.g., display view 738C) that is displayed to a user as user feedback regarding the operation of ocular aberration analysis system 100 and / or ocular aberration analysis system 300. In general operation, diameter 716C may be configured to approximately represent or correspond to the diameter of the pupil of the test eye.

[0077] FIG. 8 illustrates a flow diagram of a process 800 for operating the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 to reduce errors caused by image artifacts, according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of the process 800 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 8. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, inputs of the blocks, outputs of the blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although the process 800 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-7, the process 800 may be performed by other systems different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of the mobile structures.

[0078] At block 802, an output of the ocular aberration analysis is received. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to receive ocular aberration analysis output data including at least wavefront sensor data provided by the wavefront sensor 120. More generally, the ocular aberration analysis output data may include any one or more of wavefront sensor data provided by the wavefront sensor 120, OCT sensor data provided by the OCT sensor 122, eye tracker data provided by the eye tracker 130, and / or other output data provided by the ocular aberration analysis system 100, as described herein. The wavefront sensor data provided by the wavefront sensor 120 may include, for example, an interferogram 710 of the test eye, and / or may include, for example, wrapped phase wavefront sensor data 720 or unwrapped phase wavefront sensor data 730, depending on the capabilities and / or configuration of the wavefront sensor 120.

[0079] In block 804, the ocular aberration analysis output data is decomposed. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to decompose the wavefront sensor data received in block 802 into at least wrapped phase components / arrays 724, such as by generating a discrete Fourier transform of the interferogram 710, as described herein. In embodiments in which the wavefront sensor 120 directly provides wrapped phase wavefront sensor data 720, for example, block 804 may be omitted or incorporated into block 802.

[0080] Singularities in the ocular aberration analysis output data are identified in block 806. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to identify singularities caused by image artifacts in the unwrapped phase wavefront sensor data corresponding to the ocular aberration analysis output data received in block 802. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the unwrapped phase array 734 and / or the unwrapped phase wavefront sensor data 730 based at least in part on the wrapped phase array 724 of the wrapped phase wavefront sensor data 720, and to identify discontinuities caused by image artifacts in the unwrapped phase array 734, as described herein. In various embodiments, the control unit 140, the server 150, and / or the distributed server system 154 may be configured to identify singularities, for example, by identifying discontinuities in the unwrapped phase component 734 of the wrapped phase wavefront sensor data 730 having a magnitude exceeding a predetermined singularity threshold, or by using other discontinuity detection techniques for discrete data, including, for example, spline fitting and elemental excursion methods and / or numerical derivative and thresholding methods.

[0081] At block 808, the corrected ocular aberration analysis output data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data based at least in part on the wrapped phase components / array 724 of the wrapped phase wavefront sensor data decomposed at block 804 and / or received at block 802 and / or singularities in the received ocular aberration analysis output data identified at block 806. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine fitting weights for each element of the unwrapped phase wavefront sensor data 730, as described herein, and determine the corrected ocular aberration analysis output data by determining an aberration analysis fit based at least in part on the fitting weights and the unwrapped phase wavefront sensor data 730.

[0082] In some embodiments, the controller 140, server 150, and / or distributed server system 154 may determine the fitting weights for each element of the unwrapped phase wavefront sensor data 730 as described herein, as w i The values ​​of the parameters used in equation (1) are based at least in part on a pupil diameter of the test eye, such that the fitting weights themselves are based at least in part on a pupil diameter. In a related embodiment, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data additionally using any of the methods described herein for compensating for system aberrations and / or ocular alignment misalignments, as described herein with respect to processes 400 and 500 and FIGS. 4-6.

[0083] In block 810, user feedback is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback corresponding to the ocular aberration analysis output data received in block 802, such as a display view of the interferogram 710, and / or may be configured to generate user feedback corresponding to the corrected ocular aberration analysis output data determined in block 808, such as a display view of the ocular aberration map 750. In various embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback using any of the processes described herein with respect to FIGS. 4-6. In various embodiments, such user feedback may include a substantially real-time display view of the wavefront sensor data received in block 802, the decomposed ocular aberration analysis output data determined in block 804, the singularities identified in block 806, the corrected ocular aberration analysis output data determined in block 808, and / or the corresponding ocular aberration map 750.

[0084] Thus, as described herein, embodiments of the present disclosure can provide user feedback (e.g., displaying views including various graphics) and reliable and accurate monitoring in substantially real-time (e.g., 30 frames / second) of ocular aberrations and / or other characteristics of the optical target 102. Such embodiments can be used to assist in various types of clinical and intra-operative eye examinations and help provide improved surgical outcomes.

[0085] Aberration analysis measurements may be progressively adversely affected in terms of accuracy and repeatability as tear film hydration declines over the course of an examination / surgery. This may occur due to a lack of basic saline applied by the surgeon during intraoperative measurements or a lack of regular blinking by the patient during clinical examination. In various embodiments, ocular aberration analysis system 100 and / or ocular aberration analysis system 300 may be configured to monitor at least one and typically two available image resources during the preview and acquisition phases of measurements, for example, to quantify hydration decline and provide various associated mitigating measures, as described herein.

[0086] The first such image resource may include any imaging system of the ocular aberration analysis system that may be used to generate a Purkinje image with a known and repeatable structure reflected from the surface of the subject's eye. For example, the eye tracker 130, a dedicated Purkinje imaging system (e.g., other module 148), and / or other imaging systems of the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 may be configured to generate and / or capture a first Purkinje reflection (P1) image of one or more LED reflections generated by the LED array 132 reflecting from an external surface of the target 102 (e.g., the external surface of the cornea of ​​a human eye). In some embodiments, such a first Purkinje reflection image of the LED reflection may be processed by thresholding into a binary morphological representation that provides, for example, an analysis of the pixel connectivity and / or equivalent diameter and / or area of ​​each reflected LED. Such reflection characterization parameters may be quantified and monitored over a series of images or video frames to determine the imaged tear film hydration state in near real time.

[0087] A second such image resource includes the wavefront sensor 120. The wavefront sensor 120 may be configured to generate wavefront sensor data associated with the subject eye. As described herein, such wavefront sensor data may include interferograms of fringe lines / patterns and Talbot moire fringe contrast associated with the interferograms that are typically degraded by loss of hydration. Such tear film hydration degradation may be quantified in the Fourier transform domain of the interferogram. For example, the amplitude and shape of a neighborhood around a major frequency peak in the Fourier domain (e.g., neighborhoods 723B-1, 723B-2 of the spatial structure in FIG. 7B) may represent the derivative of the wavefront. Such neighborhoods may be directly affected when the tear film dehydrates, and the associated tear film hydration state may be quantified and monitored using analysis methods of images of various amplitudes and sizes, as described herein.

[0088] In various embodiments, the focus and Talbot moiré cameras (e.g., eye tracker 130 and wavefront sensor 120) may be synchronized. Thus, image analysis of separate image / video streams may coincide. For example, the amplitude of the Fourier peaks may be monitored along with the quality of the LED reflection (e.g., reflection characterization parameters) to provide the physician / technologist with substantially real-time feedback on the state of the tear film hydration state. For example, during an intraoperative or clinical examination, the instantaneous tear film hydration state may be communicated to the clinician (e.g., via one or more display views). Upon completion of the examination, a more comprehensive tear film hydration map and / or graph may be generated showing the occurrence of relatively dry regions as a function of time.

[0089] FIG. 9 illustrates a procedural diagram of a process 900 for monitoring tear film hydration of an ocular aberration analysis system according to an embodiment of the present disclosure. In the embodiment illustrated in FIG. 9, wavefront sensor data in the form of a first interferogram 910 of a human eye (e.g., including a plurality of approximately concentric fringe lines / patterns 912) may be acquired / captured at a first time T1, including wavefront sensor data associated with one or more monitoring regions 914, including a particular monitoring region 915. In some embodiments, the shape, pattern, and / or distribution of the monitoring regions 914 may approximately match the reflection of individual LEDs of the LED array 132 from the target 102, as described herein. In other embodiments, the monitoring regions 914 may be selected to cover at least half or three-quarters or the entire surface of the target 102, and the monitoring regions may or may not overlap each other. After a predetermined time, wavefront sensor data in the form of a second interferogram 920 of the same eye may be acquired / captured at a second time T2. The fringe lines / pattern 922 of the interferogram 920 have relatively degraded contrast compared to the fringe lines / pattern 912 of the interferogram 910. For example, a Fourier transform of one or more of the monitored regions 914 may be determined (step 904) and plotted as a graph 935 to indicate the absolute and / or relative tear film hydration status based on the wavefront sensor data of the target 102.

[0090] For example, in one embodiment, graph 935 shows a Fourier transform of wavefront sensor data in the form of image data corresponding to monitored region 915, where transform contour 932 corresponds to a Fourier transform of image data captured at time T1 and transform contour 933 corresponds to a Fourier transform of image data captured at time T2. Various transform characteristics of the transform contours may indicate absolute or relative tear film hydration status of target 102, such as peak amplitude, full width at half maximum, area, and / or other transform characteristics of transform contours 932, 933, each of which may be monitored and / or stored. In an alternative embodiment, graph 935 may be a two-dimensional spatial plot, for example, similar to that shown in display view 728B of FIG. 7B, where the transform characteristics of the spatial peaks and the vicinity 723B-1, 723B-2 of the corresponding spatial structures may be monitored and stored.

[0091] Over approximately the same period of time, Purkinje image data (e.g., including one or more reflections of known structures) in the form of a first Purkinje reflection (P1) image 950 of the human eye, including Purkinje image data associated with one or more reflections 952 (e.g., including a particular reflection 955) of the LEDs of the LED array 132, may be acquired / captured at approximately a first time T1. In some embodiments, the shape, pattern, and / or distribution of the individual LEDs of the LED array 132 may be selected to cover at least half or three-quarters or the entire surface of the target 102 and / or to match the monitored area 914, and such monitored structure reflections may generally not overlap one another. After a predetermined period of time, Purkinje image data in the form of a second Purkinje (P1) image 960 of the human eye of the same eye may be acquired / captured at a second time T2. The reflection 962 in the Purkinje (P1) image 960 has relatively poor pixel connectivity and / or is relatively small or indistinguishable from the background when compared to the reflection 952 in the Purkinje (P1) image 950. For example, such reflectance characterization parameters corresponding to each of the reflections 952 and 962 (and / or mean, median, and / or other statistical combinations and / or measurements of the reflectance characterization parameters) may be determined and plotted as a graph 975 to indicate the absolute and / or relative tear film hydration status based on the Purkinje image data of the target 102.

[0092] For example, in one embodiment, graph 975 shows reflectance characterization parameters of Purkinje image data corresponding to reflection / monitoring region 955, where reflectance characterization parameter value 972 corresponds to an estimated diameter of reflection 955 captured at time T1 and reflectance characterization parameter value 973 corresponds to an estimated diameter of reflection 955 captured at time T2. Various reflectance characterization parameters may be indicative of absolute or relative tear film hydration status of target 102, such as pixel connectivity, estimated diameter, estimated area, and / or other reflectance characterization parameters of reflections 952, 962, each of which may be monitored and / or stored.

[0093] In various embodiments, the interferograms 910, 920, Purkinje (P1) images 950, 960, and / or graphs 935, 975 may be rendered on a display of the user interface 146 as shown and form at least a portion of display views (e.g., display views 918, 928, 938, 958, 968, and / or 778) that are displayed to a user as user feedback regarding the operation of the ocular aberration analysis system 100 and / or ocular aberration analysis system 300. Additionally, the process 900 of FIG. 9 may be used to generate a time series of images, graphs, tear film hydration states, and / or related display views having more than two time series of constituent images and / or states (including streams of such images and / or states). In general operation, the diameter 916 of the interferogram 910 and the diameter 956 of the Purkinje (P1) image 950 may be configured to approximately represent or correspond to a diameter of the pupil of the test eye.

[0094] FIG. 10 illustrates a display view 100 (e.g., including one or more of display views 1018 and / or 1028) for monitoring tear film hydration of an ocular aberration analysis system according to an embodiment of the present disclosure. In the embodiment illustrated in FIG. 10, the display view 1018 includes a tear film hydration map 1010 having one or more dehydration indicators 1012. In some embodiments, the tear film hydration map 1010 may be rendered according to a color palette and / or grayscale gradient configured to indicate absolute and / or relative tear film hydration of the test eye. For example, an absolute measurement may be based on an ideal ocular tear film hydration, such as a known tear film thickness typically present after a series of typical blinks. A relative measurement may be based on an initial tear film hydration measured, for example, at the beginning of the test (e.g., T1) or when the patient indicates that their eye feels adequately hydrated, and all subsequent tear film hydration measurements may be determined relative to this initial measurement. In various embodiments, the dehydration indicator 1012 may be a graphical overlay configured to identify portions of the target 102 and / or tear film hydration map 1010 where, for example, the tear film hydration is lower than an absolute measurement or lower than expected after a certain time (e.g., T2-T1) based on, for example, an idealized eye model having typical dehydration rates.

[0095] Also shown in the display view 1018 is a selector box 1014. In some embodiments, the user interface 146 may be configured to accept user input, for example, identifying the boundaries of the selector box 1014, and the ocular aberration analysis system 100 may be configured to generate a display view 1028 including a hydration over time graph 1024 configured to show the change in tear film hydration over time associated with the region of the tear film hydration map 1010 bounded by the selector box 1014. For example, in the embodiment shown in FIG. 10, the hydration over time graph 1024 includes a hydration change contour 1022 showing the absolute or relative hydration of the portion of the tear film hydration map 1010 bounded by the selector box 1014, with a point 1026 indicating the current time or the end of a particular series of tests. In embodiments without the selector box 1014, the hydration over time graph 1024 may correspond to an arithmetic mean, average, or other statistical measure of the entire tear film hydration map 1010. In various embodiments, the selector box 1014 may be, for example, a different shape or may correspond to a selection of one or more of the dehydrator indicators 1012, as described herein.

[0096] FIG. 11 illustrates a flow diagram of a process 1100 for operating ocular aberration analysis system 100 and / or ocular aberration analysis system 300 with monitoring of tear film hydration, according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of process 1100 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 11. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Additionally, inputs of blocks, outputs of blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although process 1100 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-10, process 1100 may be performed by other systems that are different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of mobile structures.

[0097] At block 1102, an output of the ocular aberration analysis is received. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to receive ocular aberration analysis output data including at least wavefront sensor data provided by the wavefront sensor 120 and / or Purkinje image data provided by an imaging system (e.g., the eye tracker 130, other modules 148) of the ocular aberration analysis system 100. More generally, the ocular aberration analysis output data may include any one or more of wavefront sensor data provided by the wavefront sensor 120, OCT sensor data provided by the OCT sensor 122, eye tracker data provided by the eye tracker 130, and / or other output data provided by the ocular aberration analysis system 100, as described herein. The wavefront sensor data provided by the wavefront sensor 120 may include, for example, an interferogram 910 of the test eye, and the Purkinje image data provided by the imaging system of the ocular aberration analysis system 100 may include, for example, a first Purkinje reflection (P1) image 950 of the test eye.

[0098] At block 1104, a tear film hydration state based on the Purkinje image data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine a tear film hydration state corresponding to the target 102 based at least in part on the Purkinje image data received at block 1102. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the tear film hydration state by determining a thresholded binary first Purkinje reflection (P1) image (e.g., a binary morphological representation of the P1 image where reflective pixels=1 and non-reflective pixels=0) based at least in part on the captured P1 image 950, and then determining one or more reflectance characterization parameters (e.g., reflectance pixel connectivity, estimated diameter, estimated area) corresponding to the tear film hydration state of the target 102 based at least in part on the thresholded binary P1 image. Determining such a threshold-based binary P1 image may include, for each pixel of the P1 image 950, determining a pixel intensity or pixel brightness, and assigning a pixel value of one to pixels having an intensity or brightness equal to or greater than the threshold intensity or brightness, and assigning a pixel value of zero to pixels having an intensity or brightness below the threshold intensity or brightness. In some embodiments, the threshold may be, for example, predetermined (e.g., by user selection) or adaptive (e.g., based on an arithmetic mean, median, or statistical measure of the intensities and / or brightnesses of pixels in the P1 image 950).

[0099] At block 1106, a tear film hydration state based on the wavefront sensor data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine a tear film hydration state corresponding to the target 102 based at least in part on the wavefront sensor data received at block 1102. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the tear film hydration state by determining a Fourier transform of the wavefront sensor data associated with a monitored region of the wavefront sensor data and determining one or more transform characteristics based at least in part on the Fourier transform of the wavefront sensor data. In various embodiments, the transform characteristics may include one or more fringe contrast metrics, such as, for example, peak amplitude, neighborhood diameter and / or area, and / or one or more contour characteristics, such as peak amplitude, full width at half maximum, area, and / or other transformed contour characteristics, as described herein.

[0100] At block 1108, a synchronized tear film hydration state is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine a synchronized tear film hydration state corresponding to the target 102 based at least in part on the tear film hydration state determined at blocks 1104 and 1106. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the synchronized tear film hydration state by controlling the wavefront sensor 120 and the eye tracker 130 (e.g., or another imaging sensor of the system 100) to image the target 102 at approximately the same time (e.g., T1, T2) and / or according to approximately the same speed (e.g., captures / second). In various embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine a synchronized tear film hydration state by combining the tear film hydration state based on the Purkinje image data and the tear film hydration state based on the wavefront sensor data by determining an arithmetic mean, median, and / or other statistical combination of the tear film hydration state based on the Purkinje image data and the tear film hydration state based on the wavefront sensor data.

[0101] At block 1110, user feedback is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback corresponding to the tear film hydration state determined at blocks 1104, 1106, and / or 1108 based at least in part on any one or combination of the determined tear film hydration state and / or the ocular aberration analysis output data received at block 1102, such as a display view of the interferogram 910, the first Purkinje reflection (P1) image 950, and / or a graph or map of a time course or series of the determined tear film hydration state, such as a display view of the tear film hydration map 1010 and / or the tear film hydration graph 1024. In various embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate the user feedback using any of the processes described herein with respect to FIGS. 4-8.

[0102] In further embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to perform various operations based at least in part on, for example, the tear film hydration state determined in blocks 1104, 1106, and / or 1008 and / or the user feedback generated in block 1110 or a combination thereof. For example, the controller 140, server 150, and / or distributed server system 154 may be configured to detect that one or more of the tear film hydration state determined in blocks 1104, 1106, and / or 1008, the dehydration indicator 1012, the tear film hydration map 1010, and / or the hydration over time graph 1024, for example, indicate a tear film hydration level below a minimum aberration analysis hydration threshold and to exclude wavefront sensor data and / or aberration analysis measurements for a series of tests associated with a tear film hydration level below the minimum aberration analysis hydration threshold. In other embodiments, aberration analysis system 100 and / or aberration analysis system 300 may be configured to prompt (e.g., via visual or audible user feedback) the patient to blink or the operator to provide saline, for example, when any portion of target 102 is detected to be below a minimum aberration analysis hydration threshold or when a preset percentage (e.g., 10%) of target 102 is detected to be below a minimum aberration analysis hydration threshold. In further embodiments, aberration analysis system 100 and / or aberration analysis system 300 may be configured to project air (e.g., to prompt the patient to blink) or saline (to wet target 102) onto target 102 when any portion of target 102 or a preset percentage of target 102 is detected to be below a minimum aberration analysis hydration threshold.

[0103] Thus, as described herein, embodiments of the present disclosure can provide user feedback (e.g., displaying views including various graphics) and reliable and accurate monitoring in substantially real-time (e.g., 30 frames / second) of ocular aberrations and / or other properties of the optical target 102 that affect the characterization of such ocular aberrations. Such embodiments can be used to assist in various types of clinical and intra-operative eye examinations and help improve surgical outcomes.

[0104] Where appropriate, the various embodiments provided by the present disclosure may be implemented using hardware, software, or a combination of hardware and software. Also, where appropriate, the various hardware and / or software components described herein may be combined into composite components including software, hardware, and / or both without departing from the spirit of the present disclosure. Where appropriate, the various hardware and / or software components described herein may be separated into subcomponents including software, hardware, or both without departing from the spirit of the present disclosure. Furthermore, where appropriate, it is contemplated that software components may be implemented as hardware components and vice versa.

[0105] Software according to the present 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 contemplated that the software specified herein may be implemented using one or more general-purpose or special-purpose computers and / or computer systems, networked and / or not. Where appropriate, the order of the various steps described herein may be changed, combined into composite steps, and / or separated into sub-steps to provide the features described herein.

[0106] The above-described embodiments illustrate but do not limit the present invention. It should also be understood that numerous modifications and variations are possible in accordance with the principles of the present invention. Therefore, the scope of the present invention is defined only by the following claims.

Claims

1. 1. An ocular aberration analysis system, comprising: a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; a logic device configured to communicate with the wavefront sensor, the logic device comprising: receiving ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor; determining a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state; Determining when a portion of the optical target or a preset percentage of the optical target is detected to be below a predetermined minimum aberration analysis hydration threshold. It is configured as follows: A logical device; a launcher that launches saline onto the optical target when a portion of the optical target or a preset percentage of the optical target is detected to be below the predetermined minimum aberration analysis hydration threshold; An ocular aberration analysis system comprising:

2. The tear film hydration state includes a tear film hydration state based on wavefront sensor data, and determining the tear film hydration state includes: determining a Fourier transform of the received wavefront sensor data; determining a tear film hydration status based on the wavefront sensor data based at least in part on one or more transform characteristics corresponding to the determined Fourier transform of the received wavefront sensor data; Including, the one or more transform characteristics include at least one of a peak amplitude, a full width at half maximum, an area, a spatial peak amplitude, and / or an area near a spatial structure; The ocular aberration analysis system according to claim 1 .

3. the ocular aberration analysis output data includes Purkinje image data provided by an imaging system of the ocular aberration analysis system, and the tear film hydration state includes a tear film hydration state based on the Purkinje image data, and determining the tear film hydration state comprises: determining a thresholded binary first Purkinje reflection (P1) image based at least in part on the received Purkinje image data; determining one or more reflectance characterization parameters based at least in part on the thresholded binary P1 image; and Including, The ocular aberration analysis system according to claim 1 .

4. the imaging system of the ocular aberration analysis system; the imaging system includes an eye tracker; the one or more reflectance characterization parameters include at least one of a pixel connectivity, an estimated diameter, and / or an estimated area of ​​at least one reflectance in the received Purkinje image data. The ocular aberration analysis system according to claim 3 .

5. The tear film hydration state includes a synchronous tear film hydration state, and determining the tear film hydration state includes: controlling the wavefront sensor and the imaging sensor of the ocular aberration analysis system to image the optical target substantially simultaneously and / or according to the same speed; combining a Purkinje image data-based tear film hydration state determined based at least in part on Purkinje image data provided by the imaging system and a wavefront sensor data-based tear film hydration state determined at least in part on the received wavefront sensor data by determining an arithmetic mean, median, and / or other statistical combination of the Purkinje image data-based tear film hydration state and the wavefront sensor data-based tear film hydration state; Including, 5. The ocular aberration analysis system according to claim 4.

6. 6. The ocular aberration analysis system of claim 5, wherein the received Purkinje image data includes one or more reflections corresponding to light emitting diodes (LEDs) in an LED array configured to illuminate the optical target.

7. The generating of the user feedback corresponding to the determined tear film hydration status includes: generating a display view including a tear film hydration map corresponding to said determined tear film hydration state and including at least one dehydration indicator and / or at least one hydration change contour. Including, The ocular aberration analysis system according to claim 1 .

8. The logic device is 8. The ocular aberration analysis system of claim 7, configured to receive a user input selecting a boundary of a selector box indicating a selected region of the tear film hydration map, and wherein the at least one hydration change contour corresponds to a time change in tear film hydration associated with the selected region of the tear film hydration map.

9. The logic device is detecting that the determined tear film hydration state indicates a tear film hydration level below a minimum aberration analysis hydration threshold; filtering out wavefront sensor data of the received wavefront sensor data associated with the detected tear film hydration level below the minimum aberration analysis hydration threshold. The ocular aberration analysis system according to claim 1 ,

10. The logic device is detecting that the determined tear film hydration state indicates a tear film hydration level below a minimum aberration analysis hydration threshold. prompting a patient of the ocular aberration analysis system to blink or an operator of the ocular aberration analysis system to provide saline to the optical target; or projecting air or saline onto the optical target; The ocular aberration analysis system according to claim 1 ,

11. 1. A method for operating an ocular aberration analysis system by a computer, the method comprising: receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; determining a tear film hydration state associated with the optical target based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration analysis output data and / or the determined tear film hydration state; detecting, by the computer, that the determined tear film hydration state indicates a tear film hydration level below a minimum aberration analysis hydration threshold; the computer firing air or saline onto the optical target; A method comprising:

12. The tear film hydration state includes a tear film hydration state based on wavefront sensor data, and determining the tear film hydration state includes: determining a Fourier transform of the received wavefront sensor data by the computer; determining a tear film hydration state based on the wavefront sensor data based at least in part on one or more transform characteristics corresponding to the determined Fourier transform of the received wavefront sensor data; Including, the one or more transform characteristics include at least one of a peak amplitude, a full width at half maximum, an area, a spatial peak amplitude, and / or an area near a spatial structure; The method of claim 11.

13. the ocular aberration analysis output data includes Purkinje image data provided by an imaging system of the ocular aberration analysis system, and the tear film hydration state includes a tear film hydration state based on the Purkinje image data, and determining the tear film hydration state comprises: determining, by the computer, a thresholded binary first Purkinje reflection (P1) image based at least in part on the received Purkinje image data; determining, by the computer, one or more reflectance characterization parameters based at least in part on the thresholded binary P1 image; Including, The method of claim 11.

14. the imaging system of the ocular aberration analysis system; the imaging system includes an eye tracker; the one or more reflectance characterization parameters include at least one of a pixel connectivity, an estimated diameter, and / or an estimated area of ​​at least one reflectance in the received Purkinje image data. The method of claim 13.

15. The tear film hydration state includes a synchronous tear film hydration state, and determining the tear film hydration state includes: said computer controlling said wavefront sensor and said imaging sensor of said ocular aberration analysis system to image said optical target substantially simultaneously and / or according to the same speed; said computer combining a Purkinje image data-based tear film hydration state determined based at least in part on Purkinje image data provided by the imaging system and a wavefront sensor data-based tear film hydration state determined at least in part on the received wavefront sensor data by determining an arithmetic mean, median, and / or other statistical combination of the Purkinje image data-based tear film hydration state and the wavefront sensor data-based tear film hydration state; Including, The method of claim 14.

16. 16. The method of claim 15, wherein the received Purkinje image data includes one or more reflections corresponding to light emitting diodes (LEDs) in an LED array configured to illuminate the optical target.

17. The generating of the user feedback corresponding to the determined tear film hydration status includes: generating a display view including a tear film hydration map corresponding to the determined tear film hydration state and including at least one dehydration indicator and / or at least one hydration change contour. Including, The method of claim 11.

18. 20. The method of claim 17, further comprising receiving a user input selecting a boundary of a selector box indicating a selected region of the tear film hydration map, wherein the at least one hydration change contour corresponds to a time change in tear film hydration associated with the selected region of the tear film hydration map.

19. detecting, by the computer, that the determined tear film hydration state indicates a tear film hydration level below a minimum aberration analysis hydration threshold; The computer filters out wavefront sensor data of the received wavefront sensor data associated with the detected tear film hydration level below the minimum aberration analysis hydration threshold. The method of claim 11 , further comprising:

20. The computer prompts the patient of the ocular aberration analysis system to blink or the operator of the ocular aberration analysis system to provide saline to the optical target. The method of claim 11 further comprising:

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