System and method for providing a graphical user interface for intraocular lens insertion planning - Patents.com

Abstract

A system and method are presented for providing a graphical user interface (GUI) to assist eye care professionals in planning intraocular lens insertion in a two-stage process. During a first stage, first biometric data of a patient's eye is acquired. A first lens product set comprising a first lens product is identified based on the first biometric data. A first grid is provided via the GUI to present the first lens product set. During a second stage, second biometric data of the eye is acquired after an existing lens of the patient's eye is removed. A second lens product set comprising a second lens product is identified based on the second biometric data. A second grid is provided via the GUI to present the second lens product set and differences between the first and second lens products.

Description

[Technical field]

[0001] The present disclosure relates to a graphical user interface for assisting eye care professionals in intraocular lens insertion planning according to various embodiments of the disclosure. [Background technology]

[0002] Intraocular lenses have been developed to replace the crystalline lens for patients with ocular conditions, such as cataracts. Patients with such ocular conditions may undergo surgery whereby the crystalline lens of the patient's eye is removed and an artificial lens (e.g., an intraocular lens) is inserted into the eye to replace the crystalline lens. Considering that different patients may have different eye shapes (e.g., requiring different lens prescriptions), lenses having different powers may be selected and used for different patients to achieve a targeted result visual acuity (e.g., a target refractive value).

[0003] However, selecting the appropriate intraocular lens for a patient may not be an easy task. For example, intraocular lenses are manufactured with individual powers (e.g., 24.0D, 24.5D, etc.), so eye care professionals (ECPs) often have to compromise and select a lens that may not provide a similar but precise target result visual acuity for the patient. Furthermore, factors such as the patient's different lifestyle choices, the patient's ocular surgery history, non-dominant eye preferences, etc. may also affect the ECP's decision in selecting a lens. Thus, there is a need in the art to provide a user interface to assist an ECP in selecting an intraocular lens for a patient. Summary of the Invention [Means for solving the problem]

[0004] According to some embodiments, the method includes obtaining, by one or more hardware processors, first biometric data associated with the patient's eye; identifying, by the one or more hardware processors, a first lens configuration set from a plurality of lens configurations based on the first biometric data and a target refractive value, each lens configuration in the plurality of lens configurations corresponding to a particular spherical power and a particular toric power, the first lens configuration set comprising a first lens configuration having an associated result refractive value for the eye that is closest to the target refractive value based on the first biometric data; and after a lens of the eye is removed, identifying, by the one or more hardware processors, a first lens configuration set associated with the patient's eye. The method includes acquiring second biometric data; identifying, by one or more hardware processors, a second lens configuration set from the plurality of lens configurations based on the second biometric data and the target refractive value, the second lens configuration set comprising a second lens configuration having an associated result refractive value for the eye that is closest to the target refractive value based on the second biometric data; presenting, by the one or more hardware processors, a grid representing the second lens configuration set on a graphical user interface (GUI) of the device; and indicating, by the one or more hardware processors, a difference between the first lens configuration and the second lens configuration on the grid.

[0005] According to some embodiments, the system includes a non-transitory memory and one or more hardware processors, the non-transitory memory configured to read instructions to cause the system to: obtain first biometric data associated with the patient's eye; identify a first lens product set from a plurality of lens products based on the first biometric data, each lens product in the plurality of lens products corresponding to a particular spherical power and a particular toric power, the first lens product set comprising a first lens product having an associated result refractive value for the eye that is closest to a target refractive value based on the first biometric data; and identify an existing refractive value of the eye. The device further comprises one or more hardware processors configured to perform operations including: acquiring second biometric data associated with the patient's eye after the lens is removed; identifying a second set of lens products from the plurality of lens products based on the second biometric data, the second set of lens products comprising a second lens product having an associated result refractive value for the eye that is closest to the target refractive value based on the second biometric data; and presenting a grid representing at least the first lens product and the second lens product on a graphical user interface (GUI) of the device.

[0006] According to some embodiments, a non-transitory machine-readable medium having machine-readable instructions stored thereon is executable to cause a machine to perform operations including acquiring first biometric data associated with a patient's eye, identifying a first lens product from a plurality of lens products having an associated resultant refractive value for the eye that is closest to the target refractive value based on the first biometric data, acquiring second biometric data associated with the patient's eye after an existing lens for the eye is removed, identifying a second lens product from the plurality of lens products having an associated resultant refractive value for the eye that is closest to the target refractive value based on the second biometric data, and presenting a grid representing at least the first lens product and the second lens product on a graphical user interface (GUI) of the device.

[0007] For a more complete understanding of the present technology, its features, and advantages, reference is made to the following description taken in conjunction with the accompanying drawings, in which: [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram of a system for lens selection, according to some embodiments. [Diagram 2] FIG. 2 illustrates a process for providing a graphical user interface for presenting lens products for a patient, according to some embodiments. [Diagram 3] FIG. 3 illustrates an example grid for presenting a lens product set, according to some embodiments. [Figure 4] FIG. 4 illustrates a modified grid for presenting a lens product set, according to some embodiments. [Diagram 5] FIG. 5 illustrates another example grid for presenting a set of lens products, according to some embodiments. [Figure 6A] FIG. 6A is a diagram of a processing system according to some embodiments. [Figure 6B] FIG. 6B is a diagram of a processing system according to some embodiments. [Figure 7] FIG. 7 is a diagram of a multi-layer neural network, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] In the drawings, elements with the same reference numbers have the same or similar functions.

[0010] This description and the accompanying drawings illustrating aspects, embodiments, implementations, or modules of the invention should not be construed as limiting, and the claims define the protected invention. Various mechanical, configurational, structural, electrical, and operational changes may be made without departing from the spirit and scope of this description and claims. In some cases, well-known circuits, structures, or techniques have not been shown or described in detail so as not to obscure the invention. Similar numbers in two or more figures represent the same or similar elements.

[0011] In this description, certain details are provided to describe some embodiments consistent with the present disclosure. Numerous specific details are provided to provide a thorough understanding of the embodiments. However, it will be apparent to one skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are intended to be illustrative, not limiting. One skilled in the art may realize other elements not specifically described herein that are within the scope and spirit of this disclosure. In addition, to avoid unnecessary repetition, one or more features illustrated and described in connection with one embodiment may be incorporated into other embodiments, unless specifically described otherwise or unless one or more features render the embodiment non-functional.

[0012] The technology described below relates to systems and methods that provide a graphical user interface (GUI) to assist an eye care professional (ECP) (e.g., an ophthalmologist) in planning an intraocular lens insertion in a two-step process. In some embodiments, a lens selection system may be provided to assist the ECP in selecting an intraocular lens for a patient. The lens selection system may present the GUI on a device associated with the ECP. During a first stage of the two-step process, through the GUI presented on the device, the lens selection system may obtain first biometric data related to the patient's eye. For example, the ECP may use a diagnostic device to measure eye geometry, such as axial length, corneal refractive power, anterior chamber depth, lens thickness, white-white corneal diameter, and possibly other measurements, prior to performing surgery on the eye. The ECP may then provide such eye geometry to the lens selection system via the GUI. In some embodiments, the diagnostic device may automatically provide the measured eye geometry to the GUI (e.g., automatically populate data fields on the GUI). In some embodiments, the ECP may also provide the desired post-operative outcome, such as the target visual acuity (eg, target refractive value) for the patient's eye to the lens selection system via the GUI.

[0013] Based on the first biometric data and the target refractive value, the lens selection system may select a first lens product for the patient from a plurality of lens products using one or more lens selection algorithms described in more detail below. In some embodiments, each lens product may have an individual lens configuration that specifies one or more corresponding powers of the lens product (e.g., a corresponding spherical power and a corresponding toric power, etc.). The lens selection system may select the first lens product by identifying the first lens product from among the plurality of lens products available for the ECP that produces an estimated result visual acuity (e.g., an estimated result refractive value) for the patient's eye that is closest to the target visual acuity (e.g., a target refractive value) based on the first biometric data. As discussed above, the lens products may be manufactured with individual powers, and not all lens powers are available for the ECP. For example, a family of lens products may include lenses with spherical powers (e.g., 24.0D, 24.5D, 25.0D, etc.) separated by 0.5 spherical power steps. Thus, lens products having spherical powers between steps (eg, 24.3D) may not be available to ECPs.

[0014] In some embodiments, the lens selection system may use one or more algorithms (e.g., Barrett Universal, Haigis, Hoffer Q, Holladay, SRK, etc.) to predict the lens power required for the patient's eye to achieve the target visual acuity based on the patient's first biometric data. For example, the lens selection system may use the first biometric data including the axial length, corneal refractive power, anterior chamber depth, lens thickness, and white-white corneal diameter of the patient's eye measured by the ECP as input for the Barrett Universal formula, and may identify the desired refractive power (e.g., desired spherical power, desired toric power, etc.) of the patient's eye based on the Barrett Universal formula. However, since the lens products available for the ECP may not include a lens product having the exact desired power, the lens selection system may select a lens product having a lens configuration closest to the desired power from the lens products available for the ECP as the first lens product. For example, if the calculated desired power for a patient's eye includes a sphere of 24.1D and a toric power of T3, the lens selection system may select a lens product having a lens configuration specifying a sphere of 24.0D and a toric power of T3 as the first product for the patient's eye.

[0015] In addition to the first lens product, the lens selection system may also identify a first lens product group based on the first lens product for the patient. The lens selection system may identify the first lens product group by two different approaches. By the first approach, the lens selection system may identify a first lens product group having a lens configuration that is closest to the first lens configuration of the first lens product (or closest to the desired power) from the different lens products available for the ECP. For example, if the first lens product has a first lens configuration that specifies a sphere power of 24.0D and a toric power of T3, the lens selection system may select the lens product having the sphere power and / or toric power that is most similar (e.g., closest) to the first lens configuration of the first product as part of the first lens product group. Using the example discussed above, for a first lens product having a first lens configuration specifying a 24.0D spherical power and a T3 toric power, the lens selection system may select as part of the first lens product group lens products having lens configurations that specify the same T3 toric power (as the first lens configuration) but vary the spherical power (e.g., 23.5D, 24.5D, etc.). Similarly, the lens selection system may select as part of the first lens product group lens products having lens configurations that specify the same 24.0D spherical power (as the first lens configuration) but vary the toric power (e.g., T2, T4, etc.). In addition, the lens selection system may select lens products having lens configurations that specify a similar (but not the same) spherical power (e.g., 23.5D, 24.5D) and a similar (but not the same) toric power (e.g., T2, T4, etc.).

[0016] According to the second approach, the lens selection system may identify, from the different lens products available to the ECP, a first lens product group that produces an estimated result visual acuity (e.g., estimated result refractive value) that is closest to the target visual acuity (e.g., target refractive value) based on a formula. The two approaches usually, but not always, produce the same lens product group based on the first lens product. Therefore, it is beneficial for the lens selection system to be able to select (or for the ECP to enable) one of the two approaches when selecting the first lens product group. The lens selection system may select any number (e.g., 4, 8, 10, etc.) of lens products for the first lens product group using either approach.

[0017] In some embodiments, the lens selection system may present a first lens product and a first lens product group selected for the patient's eye via a GUI. In some embodiments, the lens selection system may present the first lens product and the first lens product group in a grid (e.g., a first grid) having two dimensions, where the first dimension corresponds to the spherical power and the second dimension corresponds to the toric power. The lens selection system may present the first lens product (e.g., a lens product that will produce an estimated result visual acuity closest to the target visual acuity, or a lens product having a lens configuration that specifies a spherical power and a toric power closest to the desired power based on the first biometric data) in the center of the first grid. The lens selection system may then present the lens products in the first lens product group around the first lens product. For example, the lens selection system may present lens products in a first lens product group that have the same toric power (variable spherical power) along a first dimension of a first grid (e.g., to the left and right of the first lens product) and may present lens products in the first lens product group that have the same spherical power (variable toric power) along a second dimension of the first grid (e.g., above and below the first lens product). The lens selection system may then present the remaining lens products within the first group (e.g., lens products that vary in both spherical and toric power from the first lens product) on the first grid along the first dimension with other lens products that have the same toric power and along the second dimension with other lens products that have the same spherical power. Thus, lens products having a lens configuration that is similar to the first lens product are presented closer to the first lens product on the first grid, and lens products having a lens configuration that is less similar (e.g., more different) to the first lens product are presented farther away from the first lens product on the first grid. In other words, the distance between one lens product and the first lens product on the first grid indicates how similar the lens configuration is between the lens product and the first lens product.

[0018] The grid presentation of lens products based on two dimensions (e.g., a spherical power dimension and a toric power dimension) enhances the way in which the ECP presents the lens products selected for the patient. As discussed above, the ECP may not always select the lens product that provides the closest estimated result visual acuity to the target visual acuity of the patient's eye. For example, the ECP may select a different lens product (other than the first lens product) for the patient's eye due to factors such as the patient's lifestyle choices, the patient's eye's surgical history, the patient's non-dominant eye's preferences, etc. Furthermore, the calculation of the desired power in this first stage of the two-stage process may not be accurate because the eye shape measurements that constitute the first biometric data are estimated measurements and may not be accurate. Thus, the lens products that the ECP may select for insertion into the patient's eye during surgery may differ. The grid presentation allows the ECP to select multiple lens products (e.g., lens products similar to the first lens product) in preparation for surgery so that the lens product ultimately selected by the ECP for insertion into the patient's eye can be prepared during surgery.

[0019] In some embodiments, the lens selection system may acquire second biometric data of the patient's eye during a second stage of the two-stage process. During the second stage of the two-stage process, the ECP removes an existing lens (e.g., crystalline lens) from the patient's eye, for example, during eye surgery. The patient's eye is referred to as "aphakic" after the existing lens is removed. After removing the existing lens from the patient's eye, the ECP may again measure the eye shape to acquire second biometric data. Because the second biometric data is acquired after the lens is removed, the measurements are generally more accurate than the first biometric data acquired before the lens is removed. In some embodiments, the second biometric data may include a refractive value (e.g., a wavefront measurement obtained by measuring the refractive power of an aphakic patient, etc.). Thus, the lens selection system may acquire the second biometric data from the ECP via a GUI.

[0020] Based on the second biometric data and the target refraction value, the lens selection system may select a second lens product for the patient from a plurality of lens products. The second lens product may have a second lens configuration that specifies a spherical power and a toric power. In some embodiments, the lens selection system may predict the lens power required for the patient's eye using one or more aphakic-based intraocular lens prediction formulas to achieve the target visual acuity based on the patient's second biometric data. For example, the lens selection system may use the second biometric data including the refraction value of the patient's eye measured by the ECP after the crystalline lens of the patient's eye is removed as an input to the aphakic-based formula, and may identify the desired power (e.g., desired spherical power, desired toric power, etc.) for the patient's eye based on the aphakic-based formula. However, because the lens products available for the ECP may not include a lens product having the exact desired power, the lens selection system may select a lens product having a lens configuration closest to the desired power from the lens products available for the ECP as the second lens product. For example, if the calculated desired power for the patient's eye includes a sphere of 24.4D and a toric power of T2, the lens selection system may select a lens product having a lens configuration specifying a sphere of 24.5D and a toric power of T2 as the second product for the patient's eye.

[0021] The second lens product may have a second lens configuration similar to the first lens configuration of the first lens product, and thus the second lens product may be included in the first lens product group. In some embodiments, the lens selection system may identify the second lens product in the first grid and may modify the first grid by highlighting the second lens product in the first grid. For example, the lens selection system may insert a marker in the area where the second lens product is presented. If the second lens product is presented in a box in the first grid, the lens selection system may increase the edge thickness of the box. In some embodiments, the lens selection system may change the background color / pattern of the presentation of the second lens product. The lens selection system may then present the modified first grid via the GUI for the ECP. The highlighting of the second lens product in the modified first grid provides an indication of the distance between the first lens product and the second lens product in the modified first grid, thereby indicating a deviation between the first lens product and the second lens product.

[0022] In some embodiments, in addition to identifying the second lens product, the lens selection system may also identify a second lens product group based on the second lens product (and the second biometric data) for the patient. Similar to selecting the first lens product group, the lens selection system may identify the second lens product group by two different approaches. By the first approach, the lens selection system may identify the second lens product group having a lens configuration that is closest to the second lens configuration of the second lens product (or closest to the desired power) from the different lens products available for the ECP. For example, if the second lens product has a second lens configuration that specifies a sphere power of 24.5D and a toric power of T2, the lens selection system may select the lens product having the sphere power and / or toric power that is most similar (e.g., closest) to the second lens configuration of the second product as part of the second lens product group. Using the example discussed above, for a second lens product having a second lens configuration specifying a 24.5D spherical power and a T2 toric power, the lens selection system may select as part of the second lens product group lens products having lens configurations that specify the same T2 toric power (as the second lens configuration) but vary the spherical power (e.g., 24.0D, 25.0D, etc.). Similarly, the lens selection system may select as part of the first lens product group lens products having lens configurations that specify the same 24.0D spherical power (as the second lens configuration) but vary the toric power (e.g., T2, T4, etc.). In addition, the lens selection system may select lens products having lens configurations that specify a similar (but not the same) spherical power (e.g., 24.0D, 25.0D) and a similar (but not the same) toric power (e.g., T2, T4, etc.).

[0023] With the second approach, the lens selection system may identify a second lens product group from the different lens products available for the ECP that produces an estimated result visual acuity (e.g., estimated result refractive value) for the patient's eye that is closest to the target visual acuity (e.g., target refractive value) based on an aphakic-based formula. Similar to identifying the first lens product group, the lens selection system of some embodiments may select any number (e.g., 4, 8, 10, etc.) of lens products for the second lens product group using either approach. The number of lens products selected for the second group may be the same or different from the number of lens products selected for the first group.

[0024] In some embodiments, in addition to or instead of presenting the modified first grid, the lens selection system may generate a second grid to present a second lens product and a second lens product group selected for the patient's eye. Similar to the first grid, the second grid also has two dimensions, where the first dimension corresponds to the spherical power and the second dimension corresponds to the toric power. The lens selection system may present a second lens product (e.g., a lens product that will produce an estimated result visual acuity closest to the target visual acuity, or a lens product having a lens configuration that specifies a spherical power and a toric power closest to the desired power based on the second biometric data) in the center of the second grid. The lens selection system may then present lens products in the second lens product group around the second lens product. For example, the lens selection system may present lens products in a second lens product group that have the same toric power (variable spherical power) along a first dimension of a first grid (e.g., to the left and right of the second lens product) and may present lens products in the second lens product group that have the same spherical power (variable toric power) along a second dimension of a second grid (e.g., above and below the second lens product). The lens selection system may then present the remaining lens products within the second group (e.g., lens products that vary in both spherical and toric power from the second lens product) on a second grid along with other lens products with the same toric power along the first dimension and along with other lens products with the same spherical power along the second dimension. Thus, lens products having a lens configuration that is similar to the second lens product are presented closer to the second lens product on the second grid, and lens products having a lens configuration that is less similar (e.g., more different) to the second lens product are presented farther from the second lens product on the second grid. In other words, the distance between one lens product and the second lens product on the second grid indicates how similar the lens configuration is between the lens product and the second lens product. The lens selection system may then present the second grid to the ECP via the GUI.

[0025] As discussed above, the first lens product may have a first lens configuration similar to the second lens configuration of the second lens product, and thus the first lens product may be included in the second lens product group. In some embodiments, the lens selection system may identify the first lens product in the second grid and may modify the second grid by highlighting the first lens product in the second grid. For example, the lens selection system may insert a marker in the area where the first lens product is presented on the second grid. If the first lens product is presented in a box in the second grid, the lens selection system may increase the edge thickness of the box. In some embodiments, the lens selection system may change the background color / pattern of the presentation of the first lens product. The lens selection system may then present the modified second grid via the GUI for the ECP. Similar to the modified first grid, the highlighting of the first lens product in the modified second grid provides an indication of the distance between the first lens product and the second lens product in the modified second grid, thereby indicating the deviation between the first lens product and the second lens product.

[0026] An indication of the difference between the first lens product and the second lens product (e.g., via a modified first grid, a modified second grid, etc.) may enable the ECP to select a lens product and take any necessary corrective action before inserting the lens product into the patient's eye. For example, if the difference is greater than a predetermined threshold, the ECP may perform the measurement of the ocular parameter again. Thus, if the difference between the first lens product and the second lens product is identified as being greater than a predetermined threshold, the lens selection system may also perform the operation of providing a warning to the ECP, for example, by providing a notification on the GUI indicating the significant difference between the first lens product and the second lens product.

[0027] FIG. 1 illustrates a system 100 in which a lens selection system as discussed herein may be implemented, according to some embodiments. System 100 includes a lens selection platform 102 that is coupled to one or more ECP devices, such as ECP devices 130, 140, and 150, via a network 115. Each of the ECP devices may also be communicatively coupled to a diagnostic device. For example, ECP device 130 is communicatively coupled to diagnostic device 160 (e.g., via an internal network, a wired or wireless connection, etc.). In some implementations, network 115 may include one or more switching devices, routers, a local area network (e.g., Ethernet), a wide area network (e.g., the Internet), etc. Although in this figure, lens selection platform 102 is shown separate from the ECP devices, in some embodiments, some or all of the lens selection platform may be implemented within each of ECP devices 130-150 and / or diagnostic device 160 such that the functionality of the lens selection system may be performed locally at the ECP office.

[0028] Each of the ECP devices (e.g., ECP devices 130, 140, and 150) may include a user interface (UI) application and an ECP identifier. For example, ECP device 130 includes UI application 132 and ECP identifier 134. UI application 132 may be used by a corresponding ECP (e.g., ECP 170) to interact with lens selection platform 102. For example, UI application 132 may be a web browser or a client application (e.g., a mobile application). ECP 170 may access a graphical user interface (GUI), such as a web page generated and / or hosted by lens selection platform 102, via UI application 132. ECP identifier 134 is an identifier that uniquely identifies ECP 170 among multiple ECPs serviced by lens selection platform 102.

[0029] The lens selection platform 102 includes a database 104, a lens selection engine 106, and an interface server 108. The database 104 may store information associated with lens products (e.g., different families of lens products including lens configurations available for each family of lens products, etc.). For example, based on an ECP identifier (e.g., ECP identifier 134), the lens selection platform 102 may identify which families of lens products are available to the ECP by querying the database 104. The interface server 108, in some embodiments, is configured to provide user interfaces (e.g., graphical user interfaces (GUIs), etc.) on the ECP devices 130, 140, and 150 through which an ECP, such as ECP 170, may interact with the lens selection platform 102. For example, the interface server 108 in some embodiments may include a web server that hosts a website associated with the lens selection platform 102. The interface server 108 may generate and / or store one or more interactive web pages that may be presented on the ECP devices via a UI application (e.g., UI application 132). In another embodiment, the interface server 108 may include an application server that interacts with a client application (eg, the UI application 132) via a protocol (eg, the REST protocol, etc.).

[0030] In some embodiments, an ECP (e.g., ECP 170) may acquire biometric data of the patient's eye (e.g., before and / or after removal of an existing lens on the patient's eye). For example, ECP 170 may measure the ocular geometry of the patient's eye using diagnostic device 160. Upon acquiring the biometric data, ECP 170 may provide the biometric data of the patient's eye (e.g., before and / or after removal of an existing lens on the patient's eye) via a UI application (e.g., UI application 132) and a user interface provided by interface server 108. In some embodiments, ECP device 130 may establish a connection with diagnostic device 160 and automatically acquire the biometric data from diagnostic device 160, and may automatically use the acquired biometric data to populate data fields on the user interface such that ECP 170 does not have to manually provide the biometric data to the user interface. Based on the biometric data received from the ECP device, lens selection engine 106 may generate a presentation of one or more lens products available to the ECP and selected for the patient's eye based on the biometric data. In some embodiments, the interface server may present one or more lens products in a grid. The grid may indicate an optimal lens product (e.g., a first lens product) for the patient's eye selected based on first biometric data acquired by the ECP prior to removal of an existing lens on the patient's eye, an optimal lens product (e.g., a second lens product) for the patient's eye selected based on second biometric data acquired by the ECP after removal of the existing lens on the patient's eye, and a deviation (e.g., power difference) between the first lens product and the second lens product.

[0031] FIG. 2 illustrates a process 200 for providing an ECP with a user interface for selecting a lens product for a patient's eye using a two-step process, according to an embodiment of the disclosure. In some embodiments, process 200 may be performed by lens selection platform 102, ECP devices 130-150, and / or diagnostic device 160. Process 200 begins by obtaining (at step 205) first biometric data associated with the patient's eye. For example, during a first step of the two-step process, an ECP (e.g., ECP 170) may measure the eye geometry of the patient's eye prior to performing surgery on the eye. In some embodiments, ECP 170 may measure the eye geometry of the patient's eye using diagnostic device 160. The eye geometry may include axial length, corneal refractive power, anterior chamber depth, lens thickness, white-white corneal diameter, and possibly other measurements. ECP 170 may then provide such eye geometry to the lens selection system. As discussed above, in some embodiments, the interface server 108 of the lens selection platform 102 may provide a GUI on the ECP device 130 of the ECP 170. For example, the ECP may use the UI application 132 to access a GUI (e.g., a web page) associated with the lens selection platform 102. The GUI may include a mechanism (e.g., an input field) configured to receive the eye shape (e.g., the first biometric data) from the ECP 170. The ECP 170 may manually input the first biometric data on the GUI before sending the first biometric data to the lens selection platform 102. However, if the diagnostic device 160 is connected to the ECP device 130, the diagnostic device may automatically transmit the first biometric data to the ECP device and automatically use the first biometric data to populate data fields on the GUI. In some embodiments, the ECP 170 may also provide the desired post-operative outcome, such as a target visual acuity (e.g., a target refractive value) for the patient's eye, to the lens selection platform 102 via the GUI.In some embodiments, the GUI may be configured to transmit the first biometric data, the target refractive value, and the ECP identifier 134 provided by the ECP 170 to the lens selection platform 102 (e.g., in response to the ECP selecting a “Submit” button on the GUI).

[0032] The process 200 then identifies (at step 210) a first lens product for the eye based on the first biometric data and the target refractive value. Upon obtaining the first biometric data and the ECP identifier 134 from the ECP device 130, the lens selection engine 106 may first query the database 104 to retrieve a set of lens products (e.g., one or more families of lens products) available to the ECP 170 based on the ECP identifier 134. In some embodiments, each lens product may have an individual lens configuration that specifies one or more corresponding powers of the lens product (e.g., a corresponding spherical power and a corresponding toric power, etc.). The lens selection system may select the first lens product by identifying, from among the multiple lens products available to the ECP 170, that the first lens product produces an estimated result visual acuity (e.g., an estimated result refractive value) for the patient's eye that is closest to the target visual acuity (e.g., a target refractive value). As discussed above, the lens products may be manufactured with individual powers, and not all lens powers are available to the ECP 170. For example, a family of lens products available to ECP 170 may include lenses having spherical powers separated by 0.5 spherical power steps (e.g., 24.0D, 24.5D, 25.0D, etc.) Thus, lens products having spherical powers between the steps (e.g., 24.3D) may not be available to ECP 170.

[0033] In some embodiments, the lens selection engine 106 may use one or more algorithms (e.g., Barrett Universal, Haigis, Hoffer Q, Holladay, SRK, etc.) to predict the lens power required for the patient's eye to achieve the target visual acuity based on the patient's first biometric data. For example, the lens selection engine 106 may use the first biometric data including the axial length, corneal refractive power, anterior chamber depth, lens thickness, and white-white corneal diameter of the patient's eye measured by the ECP 170 as input for the Barrett Universal formula, and may identify the desired refractive power (e.g., desired spherical power, desired toric power, etc.) of the patient's eye based on the Barrett Universal formula. However, since the lens products available for the ECP 170 may not include a lens product having the exact desired power, the lens selection engine 106 may select a lens product having a lens configuration closest to the desired power from the lens products available for the ECP 170 as the first lens product. For example, if the calculated desired power for the patient's eye includes a sphere of 24.1D and a toric power of T3, the lens selection engine 106 may select a lens product having a lens configuration specifying a sphere of 24.0D and a toric power of T3 as the first product for the patient's eye.

[0034] The process 200 then generates (at step 215) a first grid to present the first lens product and the first lens product group selected for the patient's eye. In some embodiments, in addition to the first lens product, the lens selection engine 106 may also identify the first lens product group based on the first lens product for the patient. The lens selection engine 106 may identify the first lens product group by two different approaches. By the first approach, the lens selection engine 106 may identify the first lens product group having a lens configuration that is closest to the first lens configuration of the first lens product (or closest to the desired power) from the different lens products available to the ECP. For example, if the first lens product has a first lens configuration that specifies a sphere power of 24.0D and a toric power of T3, the lens selection engine 106 may select the lens product that has the most similar (e.g., closest) sphere power and / or toric power to the first lens configuration of the first product as part of the first lens product group. Using the example discussed above, for a first lens product having a first lens configuration specifying a 24.0D spherical power and a T3 toric power, the lens selection engine 106 may select as part of the first lens product group lens products having lens configurations that specify the same T3 toric power (as the first lens configuration) but vary the spherical power (e.g., 23.5D, 24.5D, etc.). Similarly, the lens selection engine 106 may select as part of the first lens product group lens products having lens configurations that specify the same 24.0D spherical power (as the first lens configuration) but vary the toric power (e.g., T2, T4, etc.). In addition, the lens selection engine 106 may select lens products having lens configurations that specify a similar (but not the same) spherical power (e.g., 23.5D, 24.5D) and a similar (but not the same) toric power (e.g., T2, T4, etc.).

[0035] According to the second approach, the lens selection engine 106 may identify, from the different lens products available to the ECP 170, a first lens product group that produces an estimated result visual acuity (e.g., estimated result refractive value) that is closest to the target visual acuity (e.g., target refractive value) based on a formula. The two approaches usually, but not always, produce the same lens product group based on the first lens product. Therefore, it is beneficial for the lens selection engine 106 to be able to select (or allow the ECP 170 to select via a GUI) one of the two approaches when selecting the first lens product group. The lens selection system may select any number (e.g., 4, 8, 10, etc.) of lens products for the first lens product group using either approach.

[0036] In one embodiment, the lens selection engine 106 may select ten lens products having the following lens configurations as a first lens product group: (a) 24.0D sphere and T4 toric, (b) 24.0D sphere and T5 toric, (c) 24.0D sphere and T2 toric, (d) 24.0D sphere and T1 toric, and (e) 24.0D sphere and T2 toric. (e) the spherical power of 23.5D and the toric power of T3, (f) the spherical power of 24.5D and the toric power of T3, (g) the spherical power of 23.5D and the toric power of T4, (h) the spherical power of 23.5D and the toric power of T2, (i) the spherical power of 24.5D and the toric power of T4, and (j) the spherical power of 24.5D and the toric power of T2.

[0037] In some embodiments, the lens selection engine 106 may use the interface server 108 to present the first lens product and the first lens product group selected for the patient's eye via a GUI. In some embodiments, the interface server 108 may present the first lens product and the first lens product group in a grid (e.g., a first grid). FIG. 3 illustrates an example GUI 300 provided to the ECP device 130 by the interface server 108. As shown in FIG. 3, the GUI 300 may show the patient name 302 (e.g., John Doe), a formula 304 (e.g., Barrett) used to select the lens product for the patient, and a target visual acuity (e.g., target refractive value) 306 (e.g., 0.0). In some embodiments, the lens selection platform 102 may allow the ECP 170 via the GUI 300 to select a different formula (e.g., Haigis formula, Hoffer Q formula, etc.) for calculating the desired power for the patient.

[0038] The GUI 300 may also present information 308 associated with the planned surgery for the patient's eye, such as primary incision information, secondary incision information, etc. In some embodiments, the lens selection platform 102 may also provide, via the GUI 300, a presentation of a first lens product and a first lens product group selected for the patient's eye based on the first biometric data acquired via the GUI 300 from the ECP 170. As shown, the interface server 108 provides a grid 310 on the GUI 300 for presenting the first lens product and the first lens product group selected for the patient's eye. The grid 310 has two dimensions, where a first dimension (e.g., a horizontal dimension) corresponds to a spherical power and a second dimension (e.g., a vertical dimension) corresponds to a toric power.

[0039] In some embodiments, the lens selection platform 102 may present a first lens product (e.g., a lens product that would result in an estimated result visual acuity closest to the target visual acuity or a lens product having a lens configuration that specifies a sphere and toric power closest to the desired power based on the first biometric data) in the center of the grid 310. As shown, the first lens product having a first lens configuration (a sphere of 24.0D and a toric power of T3) is presented in a box 312 in the center of the grid. In addition, the box 312 is highlighted (e.g., a patterned background) to indicate to the ECP 170 that the lens product presented in the box 312 is a lens product having an estimated result visual acuity closest to the target visual acuity or a lens product having a lens configuration that specifies a sphere and toric power closest to the desired power based on the first biometric data.

[0040] The lens selection platform 102 may also present lens products in a first lens product group around the first lens product in the grid 310. For example, the lens selection platform 102 may present lens products in a first lens product group having the same toric power (variable spherical power) along a first dimension of the first grid (e.g., to the left and right of the box 312 presenting the first lens product). In this example, lens products in the first lens product group having the same toric power, such as a lens product having a 23.5D spherical power and a T3 toric power, and a lens product having a 24.5D spherical power and a T3 toric power, are presented to the left and right of the box 312, respectively. The lens selection platform 102 may present lens products in a first lens product group having the same spherical power (variable toric power) along a second dimension of the first grid (e.g., above and below the box 312 presenting the first lens product). In this example, a lens product having a spherical power of 24.0D and a toric power of T4 and a lens product having a spherical power of 24.0D and a toric power of T5 are presented in the upper part of box 312, and a lens product having a spherical power of 24.0D and a toric power of T2 and a lens product having a spherical power of 24.0D and a toric power of T1 are presented in the lower part of box 312.

[0041] The lens selection platform 102 may also present the remaining lens products within the first group (e.g., lens products that vary in both sphere and toric power from the first lens product) on the grid 310 along with other lens products having the same toric power along the first dimension and along with other lens products having the same sphere power along the second dimension. For example, a lens product having a 23.5D sphere and a T4 toric power and a lens product having a 24.5D sphere and a T4 toric power are presented in the same row (along the first dimension) on the grid 310 as a lens product having a 24.0D sphere and a T4 toric power. Similarly, a lens product having a 23.5D sphere and a T2 toric power is presented in the same column on the grid 310 as a lens product having a 23.5D sphere and a T3 toric power.

[0042] Thus, lens products in the first group that have lens configurations that are more similar to the first lens product are presented closer to the first lens product (e.g., box 312) on the grid 310, and lens products in the first group that have lens configurations that are less similar (e.g., more different) to the first lens product are presented farther away from the first lens product (e.g., box 312) on the grid 310. In other words, the distance between one lens product and the first lens product on the grid 310 indicates how similar the lens configurations are between the lens products and the first lens product.

[0043] The grid presentation of lens products based on two dimensions (e.g., a spherical power dimension and a toric power dimension) enhances the manner in which the ECP 170 presents the lens products selected for the patient. As discussed above, the ECP 170 may not always select a lens product that provides an estimated result visual acuity closest to the target visual acuity of the patient's eye. For example, the ECP 170 may select a different lens product (other than the first lens product) for the patient's eye due to factors such as the patient's lifestyle choices, the patient's eye's surgical history, the patient's non-dominant eye's preferences, etc. Furthermore, the calculation of the desired power in this first stage of the two-stage process may not be accurate because the eye shape measurements that constitute the first biometric data are estimated measurements and may not be accurate. Thus, the lens products that the ECP 170 may ultimately select for insertion into the patient's eye during surgery may differ. The grid 310 allows the ECP 170 to select multiple lens products (e.g., lens products similar to the first lens product) in preparation for surgery so that the lens product ultimately selected by the ECP 170 for insertion into the patient's eye can be prepared during surgery.

[0044] Referring back to FIG. 2, after generating the first grid to present the first lens product and the first lens product group, the process 200 acquires (at step 220) second biometric data associated with the eye after the existing lens has been removed from the eye. In some embodiments, the lens selection platform 102 may acquire second biometric data of the patient's eye during a second stage of the two-stage process. During the second stage of the two-stage process, the ECP 170 may be removing the existing lens (e.g., crystalline lens) from the patient's eye. After removing the lens from the patient's eye, the ECP 170 may measure the eye shape again to acquire the second biometric data. Because the second biometric data is acquired after the lens has been removed, the measurements are theoretically more accurate than the first biometric data acquired before the lens was removed. In some embodiments, the second biometric data may include a refractive value (e.g., a wavefront measurement, etc., obtained by measuring the refractive power of an aphakic patient). Thus, the lens selection engine 106 may obtain the second biometric data from the ECP 170 via the GUI.

[0045] The process 200 then identifies (at step 225) a second lens product for the eye based on the second biometric data and the target refractive value. For example, based on the second biometric data and the target refractive value, the lens selection engine 106 may select a second lens product for the patient's eye from a plurality of lens products available to the ECP 170. The second lens product may have a second lens configuration that specifies a spherical power and a toric power. In some embodiments, the lens selection engine 106 may predict the lens power required for the patient's eye using one or more aphakic-based intraocular lens prediction equations to achieve the target visual acuity based on the patient's second biometric data. For example, the lens selection engine 106 may use the second biometric data, including the refractive value of the patient's eye measured by the ECP 170 after the crystalline lens of the patient's eye is removed, as input to the aphakic-based equations, and may identify the desired power (e.g., desired spherical power, desired toric power, etc.) for the patient's eye based on the aphakic-based equations. However, because the lens products available for the ECP 170 may not include a lens product with the exact desired power, the lens selection engine 106 may select a lens product with a lens configuration that is closest to the desired power from the lens products available for the ECP as the second lens product. For example, if the calculated desired power for the patient's eye includes a sphere of 24.4D and a toric power of T2, the lens selection system may select a lens product with a lens configuration that specifies a sphere of 24.5D and a toric power of T2 as the second product for the patient's eye.

[0046] The second lens product may have a second lens configuration similar to the first lens configuration of the first lens product, and therefore the second lens product may be included in the first lens product group. In some embodiments, the lens selection engine 106 may identify the second lens product in the grid 310 and may modify the grid 310 by highlighting the second lens product in the grid 310. For example, the interface server 108 may insert a marker in the area where the second lens product is presented. If the second lens product is presented in a box in the grid 310, the lens selection system may increase the thickness of the edge of the box. In some embodiments, the interface server 108 may change the background color / pattern of the presentation of the second lens product on the grid 310. The lens selection system may then present the modified grid via a GUI for the ECP 170.

[0047] 4 illustrates a GUI 400 presented to the ECP 170 during surgery on a patient's eye. As shown, the GUI 400 includes the patient's name, an image 402 of the patient's eye after an existing lens has been removed, and measurements (e.g., second biometric data) 404 of the patient's eye taken after the existing lens of the eye has been removed. The second biometric data may include ocular geometric measurements (e.g., 24.5 mm) and ocular refractive values. Additionally, the GUI 400 includes a grid 410 that is based on modifying the grid 310 according to the techniques described herein.

[0048] As shown, the grid 410 is substantially identical to the grid 310 presenting the first lens product and the first lens product group, except that the box 412 presenting the second lens product is highlighted. In this example, the box 412 includes a marker 414 indicating that the lens product presented in the box 414 is the second lens product. In some embodiments, the box 312 (presenting the first lens product) is highlighted differently than the box 412 to distinguish between the first lens product and the second lens product. The highlighting of the second lens product in the modified first grid provides an indication of the distance between the first lens product and the second lens product in the modified first grid, thereby indicating to the ECP 170 the deviation (e.g., difference) between the first lens product and the second lens product.

[0049] In some embodiments, in addition to identifying the second lens product, the lens selection system may also identify a second lens product group based on the second lens product (and the second biometric data) for the patient and generate a second grid for presenting the second lens product and the second lens product group. Thus, in step 230, the process 200 generates a second grid to present the second lens and the second lens product group. Similar to selecting the first lens product group, the lens selection engine 106 may identify the second lens product group based on the second lens product (and the second biometric data) by two different approaches as discussed herein. By the first approach, the lens selection engine 106 may identify the second lens product group having a lens configuration that is closest to the second lens configuration of the second lens product (or closest to the desired power) from the different lens products available to the ECP 170. For example, if the second lens product has a second lens configuration specifying a 24.5D sphere and a T2 toricity, the lens selection system may select as part of the second lens product group a lens product having a sphere and / or toricity that is most similar (e.g., closest) to the second lens configuration of the second product. Using the example discussed above, for a second lens product having a second lens configuration specifying a 24.5D sphere and a T2 toricity, the lens selection engine 106 may select as part of the second lens product group a lens product having a lens configuration that specifies the same T2 toricity (as the second lens configuration) but changes the sphere (e.g., 24.0D, 25.0D, etc.). Similarly, the lens selection engine 106 may select lens products having lens configurations that specify the same 24.0D spherical power (as the second lens configuration) but vary in toric power (e.g., T2, T4, etc.) as part of the first lens product group.In addition, the lens selection engine 106 may select lens products having lens configurations that specify similar (but not the same) spherical powers (e.g., 24.0D, 25.0D) and similar (but not the same) toric powers (e.g., T2, T4, etc.).

[0050] With the second approach, the lens selection engine 106 may identify a second lens product group from the different lens products available to the ECP 170 that produces an estimated result visual acuity (e.g., estimated result refractive value) for the patient's eye that is closest to the target visual acuity (e.g., target refractive value) based on an aphakic-based formula. Similar to identifying the first lens product group, the lens selection engine 106 of some embodiments may select any number (e.g., 4, 8, 10, etc.) of lens products for the second lens product group using either approach. The number of lens products selected for the second group may be the same or different from the number of lens products selected for the first group.

[0051] In one embodiment, the lens selection engine 106 may select ten lens products having the following lens configurations as a second lens product group: (a) 24.5D sphere and T3 toric, (b) 24.5D sphere and T4 toric, (c) 24.5D sphere and T1 toric, (d) 24.5D sphere and T0 toric. (e) the toric power of 24.0D and T2, (f) the toric power of 25.0D and T2, (g) the toric power of 24.0D and T3, (h) the toric power of 24.0D and T1, (i) the toric power of 25.0D and T3, and (j) the toric power of 25.0D and T1.

[0052] In some embodiments, in addition to or instead of modifying the first grid (e.g., grid 310), the lens selection engine 106 may generate a second grid to present a second lens product and a second lens product group selected for the patient's eye. FIG. 5 illustrates a GUI 400 displaying a grid 510 (e.g., a second grid) to present a second lens product and a second lens product group. Like the first grid (e.g., grid 310), the grid 510 also has two dimensions, where the first dimension corresponds to a spherical power and the second dimension corresponds to a toric power (the dimensions can correspond to different powers, thus in some embodiments, the second grid may have a first dimension corresponding to a toric power and a second dimension corresponding to a spherical power). The lens selection engine 106 may present a second lens product (e.g., a lens product that would produce an estimated result visual acuity closest to the target visual acuity or a lens product having a lens configuration that specifies a spherical power and a toric power closest to the desired power based on the second biometric data) at the center of the grid 510, for example, in a box 512 at the center of the grid 510. The lens selection engine 106 may then present lens products in a second lens product group around the second lens product (e.g., box 512) on the grid 510. For example, the lens selection engine 106 may present lens products in a second lens product group that have the same toric power (varying spherical power) along a first dimension of the first grid (e.g., to the left and right of the second lens product). In this example, lens products in a second lens product group having the same toric power as the second lens product, such as a lens product having a 24.0D spherical power and a T2 toric power, and a lens product having a 25.0D spherical power and a T2 toric power, are presented to the left and right of box 512, respectively. The lens selection engine 106 may present lens products in the second lens product group having the same spherical power (varying toric power) along a second dimension of the second grid (e.g., above and below the second lens product).In this example, a lens product having a spherical power of 24.5D and a toric power of T3 and a lens product having a spherical power of 24.5D and a toric power of T4 are presented in the upper part of box 512, and a lens product having a spherical power of 24.5D and a toric power of T1 and a lens product having a spherical power of 24.5D and a toric power of T0 are presented in the lower part of box 512.

[0053] The lens selection engine 106 may then present the remaining lens products within the second group (e.g., lens products that vary in both sphere and toric power from the second lens product) on a second grid along with other lens products having the same toric power along the first dimension, and along with other lens products having the same sphere power along the second dimension. For example, a lens product having a 24.0D sphere and a T3 toric power and a lens product having a 25.0D sphere and a T3 toric power are presented in the same row (along the first dimension) on the grid 510 as a lens product having a 24.5D sphere and a T3 toric power. Similarly, a lens product having a 24.0D sphere and a T1 toric power is presented in the same column on the grid 510 as a lens product having a 24.0D sphere and a T2 toric power.

[0054] Thus, lens products having a lens configuration that is more similar to the second lens product are presented closer to the second lens product (e.g., box 512) on the grid 510, and lens products having a lens configuration that is less similar (e.g., more different) to the second lens product are presented farther away from the second lens product (e.g., box 512) on the grid 510. In other words, the distance between one lens product and the second lens product on the second grid indicates how similar the lens configuration is between the lens product and the second lens product.

[0055] The process 200 then presents (at step 235) an indication of the difference between the first lens product and the second lens product on the second grid. As discussed above, the first lens product may have a first lens configuration similar to the second lens configuration of the second lens product, and thus the first lens product may be included in the second lens product group. In some embodiments, the lens selection engine 106 may identify the first lens product in the second grid (e.g., grid 510) and may modify the second grid by highlighting the first lens product in the second grid. For example, the lens selection engine 106 may insert a marker in the area where the first lens product is presented on the second grid. If the first lens product is presented in a box in the second grid, the lens selection engine 106 may increase the thickness of the edge of the box. In some embodiments, the lens selection system may change the background color / pattern of the presentation of the first lens product. 5, the lens selection engine 106 modifies the grid 510 by highlighting an area (e.g., box 514) that represents a first lens product. In this example, the lens selection engine 106 highlighted the box 514 by placing markers 516 at the corners of the box 514. However, other embodiments of the lens selection engine 106 may choose to highlight the area of ​​the first lens product on the grid 510 differently, as discussed herein.

[0056] Similar to the modified first grid, the highlighting of the first lens product in the modified second grid provides an indication of the distance between the first lens product and the second lens product in the modified second grid, thereby indicating a deviation (e.g., difference) between the first lens product and the second lens product. An indication of the difference between the first lens product and the second lens product (e.g., via the modified first grid, the modified second grid, etc.) may enable the ECP 170 to select a lens product and take necessary corrective action before inserting the lens product into the patient's eye. For example, if the difference is greater than a predetermined threshold, the ECP 170 may perform a measurement of the eye parameter again, since a large difference may indicate a problem with the previous measurement. Thus, if the difference between the first lens product and the second lens product is identified as being greater than a predetermined threshold, the lens selection engine may also perform an operation of providing a warning to the ECP 170, for example, by providing a notification on the GUI 300 indicating the large difference between the first lens product and the second lens product. In some embodiments, the difference between the first lens product and the second lens product is large enough that the first lens product is not included in the second lens product group (and / or the second lens product is not included in the first lens product group). Thus, the first lens product (or the second lens product) may not be presented in the modified first grid or the second grid. Thus, the highlighting of the area presenting the first lens product and / or the second lens product may not provide an indication of the difference. Thus, the lens selection engine 106 may present a notification, such as a pop-up window or a flashing icon on the GUI 300, indicating the difference in sphere and toricity between the first lens product and the second lens product. In some embodiments, the lens selection engine 106 may also send a notification to another device (e.g., a mobile device) of the ECP 170 when the difference between the first lens product and the second lens product is greater than a predetermined threshold.

[0057] In some embodiments, the selection of the first and second lens products and / or the selection of the first and second lens product groups may be performed with the aid of a machine learning model. For example, the machine learning model may be trained using historical data associated with the ECP's selection of lens products based on biometric data of the patient's eye. As discussed above, it is not uncommon for an ECP to select a lens product for a patient's eye that does not provide optimal outcome vision based on a variety of factors. Thus, by learning which lens products the ECP has selected for a patient in the past, the machine learning model may better predict the lens product that the ECP will ultimately use for the patient's eye. For example, after performing an IOL surgery, the ECP device 130 may obtain post-operative data (e.g., eye measurements, quality of vision, etc.) from, for example, the diagnostic device 160 or through a survey administered to the patient. The post-operative data may be used in combination with the lenses selected for the patient as training data to train the machine learning model. Details of the machine learning model are further described below with reference to FIG. 7.

[0058] Figures 6A and 6B are diagrams of a processing system according to some embodiments. Although two embodiments are shown in Figures 6A and 46B, one of ordinary skill in the art will readily appreciate that other system embodiments are also possible. According to some embodiments, the processing system of Figures 6A and / or 6B is representative of a computing system that may be included in one or more of the lens selection platform 102, ECP devices 130, 140, and 150, etc.

[0059] 6A illustrates a computing system 600, whose components are in electrical communication with one another using a bus 605. The system 600 includes a processor 610 and a system bus 605 that couples various system components to the processor 610, including memory in the form of read-only memory (ROM) 620, random access memory (RAM) 625, and the like (e.g., PROM, EPROM, FLASH-EPROM, and / or other memory chips or cartridges). The system 600 may further include a cache 612 of high-speed memory directly connected to, in close proximity to, or integrated as part of the processor 610. The system 600 may access data stored in the ROM 620, the RAM 625, and / or one or more storage devices 630 via the cache 612 for fast access by the processor 610. In some embodiments, the cache 612 may provide a performance boost to avoid delays when the processor 610 accesses data from the memory 615, the ROM 620, the RAM 625, and / or the one or more storage devices 630 that were previously stored in the cache 612. In some embodiments, the one or more storage devices 630 store one or more software modules (e.g., software modules 632, 634, 636, etc.). The software modules 462, 634, and / or 636 may control and / or be configured to control the processor 610 to perform various actions, such as the processes of the methods 300 and / or 500. Also, while the system 600 shows only one processor 610, it will be understood that the processor 610 may represent one or more central processing units (CPUs), multi-core processors, microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs), tensor processing units (TPUs), etc. In some embodiments, system 400 may be implemented as a stand-alone subsystem and / or as an added board to a computing device or as a virtual machine.

[0060] To enable a user to interact with system 600, system 600 includes one or more communication interfaces 640 and / or one or more input / output (I / O) devices 645. In some embodiments, one or more communication interfaces 640 may include one or more network interfaces, network interface cards, etc. to provide communications according to one or more network and / or communication bus standards. In some embodiments, one or more communication interfaces 440 may include an interface for communicating with system 600 over a network, such as network 115. In some embodiments, one or more I / O devices 645 may include one or more user interface devices (e.g., a keyboard, a pointing / selection device (e.g., a mouse, a touchpad, a scroll wheel, a trackball, a touch screen, etc.)), audio devices (e.g., a microphone and / or a speaker), sensors, actuators, display devices, etc.

[0061] Each of the one or more storage devices 630 may include non-transitory and non-volatile storage such as that provided by a hard disk, optical media, solid-state drive, etc. In some embodiments, each of the one or more storage devices 630 may be co-located with system 600 (e.g., local storage) and / or located remotely from system 600 (e.g., cloud storage).

[0062] 6B illustrates a computing system 650 based on a chipset architecture that may be used in performing any of the methods described herein (e.g., methods 300 and / or 510). The system 650 may include a processor 655 representing any number of physically and / or logically distinct resources that may perform software, firmware, and / or other computations, such as one or more CPUs, multi-core processors, microprocessors, microcontrollers, DSPs, FPGAs, ASICs, GPUs, TPUs, etc. As shown, the processor 655 is assisted by one or more chipsets 660, which may also include one or more CPUs, multi-core processors, microprocessors, microcontrollers, DSPs, FPGAs, ASICs, GPUs, TPUs, co-processors, coder-decoders (CODECs), etc. As shown, the one or more chipsets 660 interface with the processor 655 along with one or more of one or more I / O devices 665, one or more storage devices 670, memory 675, a bridge 680, and / or one or more communication interfaces 690. In some embodiments, one or more of I / O devices 665, one or more of storage devices 670, memory, and / or one or more of communication interfaces 690 may correspond to similarly named counterparts in FIG. 6A and system 600.

[0063] In some examples, bridge 680 may provide additional interfaces to provide access to one or more user interface (UI) components for system 650, such as one or more keyboards, pointing / selection devices (e.g., a mouse, touchpad, scroll wheel, trackball, touch screen, etc.), audio devices (e.g., a microphone and / or speaker), display devices, etc. According to some embodiments, systems 600 and / or 650 may provide a graphical user interface (GUI) suitable for assisting a user (e.g., a surgeon and / or other medical personnel) in performing the processes of method 200.

[0064] Methods according to the above-described embodiments may be implemented as executable instructions stored on a non-transitory, tangible, machine-readable medium. The executable instructions, when executed by one or more processors (e.g., processor 610 and / or processor 655), may cause the one or more processors to perform one or more of the processes of methods 200 and / or 210. Some common forms of machine-readable media that may contain the processes of methods 200 and / or 210 are, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tapes, any other physical media with patterns of holes, RAM, PROMs, EPROMs, FLASH-EPROMs, any other memory chips or cartridges, and / or any other media adapted to be read by a processor or computer.

[0065] An apparatus implementing methods according to these disclosures may include hardware, firmware, and / or software and may take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, etc. Some of the functionality described herein may also be embodied in peripherals and / or add-in cards. Such functionality may also be implemented on a circuit board among different chips or different processes executed in a single device, as further examples.

[0066] FIG. 7 is a diagram of a multi-layer neural network 700, according to some embodiments. In some embodiments, the neural network 700 may represent a neural network used to implement a machine learning model for predicting the first and second lens products and / or the first and second lens product groups, as discussed herein. The neural network 700 processes input data 710 using an input layer 720. In some examples, the input data 710 may correspond to input data provided to one or more models and / or training data provided to one or more models upon updating during a process used to train the one or more models. The input layer 720 includes a number of neurons that are used to condition the input data 710 by scaling, range limiting, etc. Each neuron in the input layer 720 generates an output that is fed to an input of a hidden layer 731. The hidden layer 731 includes a number of neurons that process the output from the input layer 720. In some examples, each of the neurons in the hidden layer 731 generates an output that is then propagated through one or more additional hidden layers, terminating in a hidden layer 739. Hidden layer 739 includes multiple neurons that process the output from the previous hidden layer. The output of hidden layer 739 is provided to output layer 740. Output layer 740 includes one or more neurons that are used to adjust the output from hidden layer 739 by scaling, range limiting, etc. It should be understood that the architecture of neural network 700 is merely representative and that other architectures are possible, including neural networks with only one hidden layer, neural networks without input and / or output layers, neural networks with recurrent layers, etc.

[0067] In some embodiments, the input layer 720, the hidden layers 731-739, and / or the output layer 740 each include one or more neurons. In some embodiments, the input layer 720, the hidden layers 731-739, and / or the output layer 740 each may include the same or different numbers of neurons. In some embodiments, each of the neurons takes a combination (e.g., a weighted sum using a trainable weight matrix W) of its inputs x, adds an optional trainable bias b, and applies an activation function f to generate an output a, as shown in Equation 1. In some embodiments, the activation function f may be a linear activation function, an activation function with upper and / or lower bounds, a log-sigmoid function, a hyperbolic tangent function, a rectified linear unit function, etc. In some embodiments, each of the neurons may have the same or different activation functions. a=f(Wx+b) Equation (1)

[0068] In some embodiments, the neural network 700 may be trained using supervised learning, which is a combination of training data (e.g., a patient's biometric data, etc.) that includes a combination of input data and ground truth (e.g., expected) output data (e.g., lens products selected by an ECP for a patient in the past, etc.). The difference between the output of the neural network 700 generated using the input data for the input data 710 and the output data 750 generated by the neural network 700 compared to the ground truth output data. The difference between the generated output data 750 and the ground truth output data may then be fed back to the neural network 700 to correct various trainable weights and biases. In some embodiments, the difference may be fed back using a backpropagation technique using a stochastic gradient descent algorithm or the like. In some embodiments, multiple sets of combinations of training data may be presented to the neural network 700 multiple times until an overall loss function (e.g., mean squared error based on the difference between each training combination) converges to an acceptable level.

[0069] Although exemplary embodiments have been shown and described, the foregoing disclosure contemplates a wide range of modifications, changes, and substitutions, and in some cases, some features of the embodiments may be utilized without the corresponding use of other features. Those skilled in the art will recognize many variations, alternatives, and modifications. Accordingly, the scope of the present invention should be limited only by the scope of the claims that follow, and it is appropriate that the claims be interpreted broadly in a manner consistent with the scope of the embodiments disclosed herein. According to aspect (1), there is provided a method, acquiring, by one or more hardware processors, first biometric data associated with the patient's eye; identifying, by the one or more hardware processors, from a plurality of lens configurations, a first set of lens configurations based on the first biometric data and a target refractive value, each lens configuration in the plurality of lens configurations corresponding to a particular spherical power and a particular toric power, the first set of lens configurations comprising a first lens configuration having an associated resultant refractive value for the eye that is closest to the target refractive value based on the first biometric data; acquiring, by the one or more hardware processors, second biometric data associated with the patient's eye after the lens of the eye has been removed; and identifying, by the one or more hardware processors, from the plurality of lens configurations, a second lens configuration set based on the second biometric data and the target refractive value, the second lens configuration set comprising a second lens configuration having an associated resultant refractive value for the eye that is closest to the target refractive value based on the second biometric data; presenting, by the one or more hardware processors, a grid representing the second set of lens configurations on a graphical user interface (GUI) of the device; and indicating, by the one or more hardware processors, on the grid, a difference between the first lens configuration and the second lens configuration; The method includes: According to aspect (2), the second lens configuration is presented at the center of the grid and the first lens configuration is presented at a small distance from the center of the grid based on the difference. According to aspect (3), the first lens configuration is presented at a center of the grid and the second lens configuration is presented at a small distance from the center of the grid based on the difference. According to aspect (4), the operation further includes: determining that the difference between the first lens configuration and the second lens configuration is greater than a threshold; and in response to determining that the difference is greater than the threshold, providing a notification on the GUI of the device; Includes. According to aspect (5), the method further includes removing the lens of the patient's eye before acquiring the second biometric data. According to aspect (6), the grid is a first grid, and the method further comprises: presenting a second grid representing the first set of lens configurations prior to presenting the first grid on the GUI of the device. Includes. According to aspect (7), the first lens configuration is presented at the center of the second grid. According to an eighth aspect, there is provided a system comprising: A non-transient memory; one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to provide the system with: Obtaining first biometric data associated with an eye of a patient; identifying a first lens product set from a plurality of lens products based on the first biometric data, each lens product in the plurality of lens products corresponding to a particular spherical power and a particular toric power, the first lens product set comprising a first lens product having an associated resultant refractive value for the eye that is closest to a target refractive value based on the first biometric data; obtaining second biometric data associated with the patient's eye after an existing lens of the eye has been removed; and identifying a second lens product set from the plurality of lens products based on the second biometric data, the second lens product set comprising a second lens product having an associated resultant refractive value for the eye closest to the target refractive value based on the second biometric data; presenting a grid on a graphical user interface (GUI) of a device, the grid representing at least the first lens product and the second lens product; and one or more hardware processors configured to perform operations including: According to aspect (9), the grid represents the second lens product set, the grid having a first dimension corresponding to a spherical power and a second dimension corresponding to a toric power. According to aspect (10), lens products from the second lens product set having the same toric power are arranged along the first dimension of the grid, and lens products from the second lens product set having the same spherical power are arranged along the second dimension of the grid. According to aspect (11), the second lens product is presented at the center of the grid. According to aspect (12), each particular lens product of the second set of lens products is positioned on the grid at a distance from the center of the grid based on one or more differences in sphere and / or toricity between the particular lens product and the second lens product. According to aspect (13), the operation further includes: identifying an area on the grid that represents the first lens product; highlighting the area of ​​the grid; and Includes. According to aspect (14), the grid represents the first set of lens products, the first lens product being presented at a center of the grid, and the operation further comprises: identifying an area on the grid that represents the second lens product; highlighting the area of ​​the grid; and Includes. According to aspect (15), there is provided a non-transitory machine-readable medium having machine-readable instructions stored thereon, the machine-readable instructions including: Obtaining first biometric data associated with an eye of a patient; identifying a first lens product from a plurality of lens products having an associated resultant refractive value for the eye closest to a target refractive value based on the first biometric data; obtaining second biometric data associated with the patient's eye after an existing lens of the eye has been removed; and identifying a second lens product from the plurality of lens products having an associated resultant refractive value for the eye closest to the target refractive value based on the second biometric data; presenting a grid on a graphical user interface (GUI) of a device, the grid representing at least the first lens product and the second lens product;

[0033] It is a non-transitory machine-readable medium. According to aspect (16), the operation further includes: identifying a lens product set from the plurality of lens products based on the second biometric data, the grid representing the lens product set comprising the first lens product and the second lens product. According to aspect (17), the second lens product is presented at the center of the grid. According to an aspect (18), the operation further includes: identifying an area on the grid that represents the first lens product; highlighting the area of ​​the grid; and Includes. According to aspect (19), the operation further includes: identifying a lens product set from the plurality of lens products based on the first biometric data, the grid representing the lens product set comprising the first lens product and the second lens product. According to aspect (20), the first lens product is presented at a center of the grid, and the operation further comprises: identifying an area on the grid that represents the second lens product; highlighting the area of ​​the grid; and Includes.

Claims

1. 1. A system comprising: A non-transient memory; one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to provide the system with: Obtaining first biometric data associated with an eye of a patient; identifying a first lens product set from a plurality of lens products based on the first biometric data, each lens product in the plurality of lens products corresponding to a particular spherical power and a particular toric power, the first lens product set comprising a first lens product having an associated resultant refractive value for the eye that is closest to a target refractive value based on the first biometric data; obtaining second biometric data associated with the patient's eye after an existing lens of the eye has been removed; and identifying a second set of lens products from the plurality of lens products based on the second biometric data, the second set of lens products comprising a second lens product having an associated resultant refractive value for the eye closest to the target refractive value based on the second biometric data; presenting a grid on a graphical user interface (GUI) of a device, the grid representing at least the first lens product and the second lens product; and one or more hardware processors configured to cause the processor to perform operations including: The system determines whether a difference between a first lens configuration based on the first biometric data and a second lens configuration based on the second biometric data is greater than a threshold, and in response to determining that the difference is greater than the threshold, displays a warning on the GUI of the device.

2. 10. The system of claim 1, wherein the grid represents the second lens product set, the grid comprising a first dimension corresponding to a spherical power and a second dimension corresponding to a toric power.

3. 3. The system of claim 2, wherein lens products from the second lens product set having the same toric power are arranged along the first dimension of the grid and lens products from the second lens product set having the same spherical power are arranged along the second dimension of the grid.

4. The system of claim 2 , wherein the second lens product is presented at the center of the grid.

5. 5. The system of claim 4, wherein each particular lens product of the second set of lens products is positioned on the grid at a distance from the center of the grid based on one or more differences in sphere and / or toricity between the particular lens product and the second lens product.

6. The operation further comprises: identifying an area on the grid that represents the first lens product; highlighting the area of ​​the grid; and The system of claim 1 , comprising:

7. the grid represents the first set of lens products, the first lens product being presented at a center of the grid, and the operation further comprising: identifying an area on the grid that represents the second lens product; highlighting the area of ​​the grid; and The system of claim 1 , comprising:

8. A non-transitory machine-readable medium having machine-readable instructions stored thereon, the machine-readable instructions including instructions for causing a machine to: Obtaining first biometric data associated with an eye of a patient; identifying a first lens product from a plurality of lens products having an associated resultant refractive value for the eye closest to a target refractive value based on the first biometric data; obtaining second biometric data associated with the patient's eye after an existing lens of the eye has been removed; and identifying a second lens product from the plurality of lens products having an associated resultant refractive value for the eye closest to the target refractive value based on the second biometric data; presenting a grid on a graphical user interface (GUI) of a device, the grid representing at least the first lens product and the second lens product; determining whether a difference between a first lens configuration based on the first biometric data and a second lens configuration based on the second biometric data is greater than a threshold, and in response to determining that the difference is greater than the threshold, displaying a warning on the GUI of the device; The machine-readable instructions are executable to cause a system to perform operations including: Non-transitory machine-readable media.

9. The operation further comprises: identifying a second set of lens products from the plurality of lens products based on the second biometric data, the grid representing the first lens products and the second set of lens products. The non-transitory machine-readable medium of claim 8.

10. The non-transitory machine-readable medium of claim 9 , wherein the second lens product is presented at the center of the grid.

11. The operation further comprises: identifying an area on the grid that represents the first lens product; highlighting the area of ​​the grid; and 11. The non-transitory machine-readable medium of claim 10, comprising:

12. The operation further comprises: identifying a first set of lens products from the plurality of lens products based on the first biometric data, the grid representing the first set of lens products and the second lens products. The non-transitory machine-readable medium of claim 8.

13. The first lens product is presented at a center of the grid, and the operation further comprises: identifying an area on the grid that represents the second lens product; highlighting the area of ​​the grid; and 13. The non-transitory machine-readable medium of claim 12, comprising:

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