OCT-guided treatment

Abstract

A system for managing retinal disease treatment, predicting retinal disease progression, and classifying retinal disease uses remote-based OCT imaging of a subject's retina. The system for managing retinal disease treatment includes a communication unit, at least one processor, and a tangible storage device. The communication unit is configured to receive optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina. The tangible storage device stores non-transitory instructions executable by the at least one processor to process the OCT image data of the retina to determine a series of measured retinal fluid extent values ​​and generate a treatment recommendation for the subject via execution of a treatment algorithm using input based on the series of measured retinal fluid extent values.

Description

[Technical field]

[0001]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 330,666, filed April 13, 2022, the entire contents of which are incorporated herein by reference in their entirety for all purposes. [Background technology]

[0002]

[0002] Macular degeneration is the leading cause of vision loss in the United States. In macular degeneration, the central retina (also known as the macula) deteriorates. When healthy, the macula collects highly detailed images and sends them to the brain via the optic nerve. In the early stages, macular degeneration typically does not significantly affect vision. As macular degeneration progresses beyond the early stages, vision may become wavy and / or blurred. As macular degeneration continues to progress to the advanced stages, central vision may be lost.

[0003]

[0003] Macular degeneration is currently considered incurable, but there are treatments that can slow the progression of the disease to prevent severe vision loss. Treatment options include injections of antiangiogenic drugs into the eye, laser therapy to destroy actively growing abnormal blood vessels, and photodynamic laser therapy, which uses photosensitive drugs to damage abnormal blood vessels. Early detection of macular degeneration is paramount to prevent further progression of macular degeneration prior to treatment to inhibit disease progression.

[0004]

[0004] Early detection of macular degeneration can be achieved using a suitable retinal imaging system. For example, optical coherence tomography (OCT) is a non-invasive imaging technique that relies on low coherence interferometry, which can be used to generate cross-sectional images of the macula. The cross-sectional images of the macula indicate whether the macular layers are distorted, and can be used to evaluate the effect of treatment of macular degeneration by monitoring whether the distortion of the macular layers increases or decreases compared to previous cross-sectional images. Summary of the Invention

[0005]

[0005] The following presents a simplified summary of some embodiments of the invention in order to provide a basic understanding of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key / critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some embodiments of the invention in a simplified form as a prelude to the more detailed description that is presented later.

[0006]

[0006] In many embodiments, short-interval monitoring of a subject's retinal disease status, for example on a daily basis, using optical coherence tomography (OCT) imaging of the subject's retina is used to provide valuable information to treating physicians. In many embodiments, the retinal OCT image data is generated by an affordable OCT-based ophthalmic imaging device that the subject can use at home on a short-interval basis to monitor the subject's retinal disease status. Short-interval monitoring allows for more accurate tracking of the subject's retinal disease status and development of treatment approaches based on day-to-day changes in the subject's retinal disease status, as opposed to hit-and-miss treatment approaches that may be adopted when the subject's retinal disease status is checked at typical current intervals (e.g., once a month, once every five weeks, once every six weeks, etc.). In many embodiments, short-interval monitoring allows for improved scheduling of the application of treatments (e.g., injections of therapeutic compounds into the subject's eye) for the subject's retinal disease. In some embodiments, short-interval monitoring can be used to develop a customized treatment plan for the subject based on the observed progression of the subject's retinal disease and / or the observed response of the subject's retinal disease to one or more previous treatment applications.

[0007]

[0007] Thus, in one aspect, a system for tracking a retinal disease status of a subject's eye includes a communication unit, at least one processor, and a tangible storage device storing non-transitory instructions. The communication unit is configured to receive optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina having a suitable imaging frequency (e.g., at least once every two weeks, at least once a week, at least once every three days, at least once every two days, at least once a day). The non-transitory instructions are executable by the at least one processor to cause the at least one processor to process the retinal OCT image data to determine a series of measured extent values. Each of the series of measured extent values ​​is indicative of a respective extent of the retinal disease. The instructions further cause the processor to generate an output indicative of the series of measured extent values.

[0008]

[0008] In many embodiments of the system, a series of OCT imaging sessions of the retina is performed over a treatment interval of the retinal disease. For example, in some retinal diseases, the treatment interval (e.g., the time span between injections of a therapeutic compound into the subject's eye) can be about one month or more (e.g., 4 weeks, 5 weeks, 6 weeks, 7 weeks, or more). By performing a series of OCT imaging sessions over the treatment interval, visibility is obtained regarding the extent of the retinal disease at the time point between treatments. As a result, the extent of the retinal disease between treatment applications can be measured and tracked, thereby providing the treating medical professional with feedback regarding the regression and / or progression of the extent of the retinal disease between treatment applications. The series of OCT imaging sessions can be performed over any suitable time span and at any suitable frequency. For example, the series of OCT imaging sessions can be performed over at least one month or more to cover at least one time span between treatment applications. The series of OCT imaging sessions can have an imaging frequency of at least once every two weeks, at least once a week, at least once every three days, or at least once a day.

[0009]

[0009] In some embodiments of the system, the measured extent values ​​are indicative of an amount of fluid in the retina. For example, at least one of the series of measured extent values ​​may be indicative of a length of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​may be indicative of a depth of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​may be indicative of a volume of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​may be indicative of a length of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​may be indicative of a depth of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​may be indicative of a volume of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina.

[0010]

[0010] In many embodiments, the system is configured to generate and transmit a notification to the subject's designated treating professional in response to progression of the subject's retinal disease to or beyond a selected threshold to enable scheduling of application of treatment for the subject's retinal disease based on the observed progression of the subject's retinal disease. For example, in some embodiments, the non-transitory instructions further cause the at least one processor to compare at least one of the series of measured extent values ​​to a respective threshold extent value, and in response to at least one of the series of measured extent values ​​equaling or exceeding a respective threshold extent value, transmit a communication to the treating professional when at least one of the series of measured extent values ​​exceeds a respective threshold extent value. In some embodiments, the non-transitory instructions further cause the at least one processor to compare at least one of the series of measured extent values ​​to a respective threshold extent value, and in response to at least one of the series of measured extent values ​​equaling or exceeding a respective threshold extent value, trigger remote treatment of the retinal disease via operation of an implanted pump to infuse a therapeutic compound into the eye.

[0011] In many embodiments of the system, the non-transitory instructions further cause the at least one processor to transmit at least one of the series of one or more measured extent values ​​to the treating professional to enable the treating professional to track the progression of the retinal disease. In some embodiments, the non-transitory instructions further cause the at least one processor to transmit a graph of at least one of the series of measured extent values ​​to the treating professional. In some embodiments, the non-transitory instructions further cause the at least one processor to display at least one of the series of measured extent values ​​to the treating professional.

[0012]

[0012] In some embodiments, the system is configured to determine a parameter describing the extent of the subject's retinal disease between treatment applications. For example, in some embodiments, the system is configured to measure the extent of intraretinal fluid in the retina. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, the second treatment for the retinal disease being subsequent to the first treatment for the retinal disease and consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate at least one fluid-present interval within the treatment interval, during which a volume of intraretinal fluid is detected in each of a series of OCT imaging sessions of the retina achieved within the fluid-present interval. The non-transitory instructions further cause the at least one processor to calculate a fluid-absent interval within the treatment interval, during which a volume of intraretinal fluid is not detected via each of a series of OCT imaging sessions of the retina achieved within the treatment interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate a fluid regression interval within the treatment interval, where a volume of intraretinal fluid detected in a series of OCT imaging sessions of the retina is decreasing in volume during the fluid regression interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate a fluid increase interval within the treatment interval, where a volume of intraretinal fluid detected in a series of OCT imaging sessions of the retina increases in volume during the fluid increase interval.In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for a retinal disease, (b) store a second treatment date for a second treatment for a retinal disease, the second treatment for a retinal disease being subsequent to the first treatment for a retinal disease and contiguous with the first treatment for a retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) determine a maximum thickness of intraretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for a retinal disease, (b) store a second treatment date for a second treatment for a retinal disease, the second treatment for a retinal disease being subsequent to the first treatment for a retinal disease and contiguous with the first treatment for a retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) determine a maximum volume of intraretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval.

[0013]

[0013] In some embodiments, the system is configured to measure an extent of subretinal fluid in the retina. For example, in some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, the second treatment for the retinal disease being subsequent to the first treatment for the retinal disease and consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate at least one fluid-present interval within the treatment interval, during which a volume of subretinal fluid is detected in each of a series of OCT imaging sessions of the retina achieved within the fluid-present interval. In some embodiments, the non-transitory instructions further cause the at least one processor to calculate a fluid-absent interval within the treatment interval, during which no volume of subretinal fluid is detected via each of a series of OCT imaging sessions of the retina achieved within the treatment interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate a fluid regression interval within the treatment interval, where a volume of subretinal fluid detected in the series of OCT imaging sessions of the retina is decreasing in volume during the fluid regression interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for the retinal disease, (b) store a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) calculate a fluid increase interval within the treatment interval, where a volume of subretinal fluid detected in the series of OCT imaging sessions of the retina increases in volume during the fluid increase interval.In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for a retinal disease, (b) store a second treatment date for a second treatment for a retinal disease, the second treatment for a retinal disease being subsequent to the first treatment for a retinal disease and contiguous with the first treatment for a retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) determine a maximum thickness of subretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval. In some embodiments, the non-transitory instructions further cause the at least one processor to (a) store a first treatment date for a first treatment for a retinal disease, (b) store a second treatment date for a second treatment for a retinal disease, the second treatment for a retinal disease being subsequent to the first treatment for a retinal disease and contiguous with the first treatment for a retinal disease, the treatment interval spanning from the first treatment date to the second treatment date, and (c) determine a maximum volume of subretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval.

[0014] In some embodiments, the system is configured to monitor the subject's compliance with a specified schedule for imaging the subject's retina. For example, in some embodiments of the system, the OCT imaging data includes imaging date data indicating an occurrence date of each of the series of retinal OCT imaging sessions, and the non-transitory instructions further cause the at least one processor to (a) process the imaging date data to monitor non-compliance by the subject with the specified schedule for performing the series of retinal OCT imaging sessions, and (b) in response to detecting non-compliance by the subject with the specified schedule for performing the series of retinal OCT imaging sessions, send a reminder to the subject to follow the specified schedule for performing the series of retinal OCT imaging sessions.

[0015] In some embodiments, the system is configured to assess a severity of a retinal disease in the subject. For example, in some embodiments, the non-transitory instructions further cause the at least one processor to generate a severity score indicative of a severity of the retinal disease based on the OCT imaging data.

[0016]

[0016] In some embodiments, the system is configured to generate a recommendation for a treatment of a retinal disease for the subject. For example, in some embodiments, the non-transitory instructions further cause the at least one processor to generate a recommendation for a treatment of a retinal disease based on the OCT imaging data. The treatment recommendation can include a recommended date for injecting the therapeutic compound into the eye. The treatment recommendation can include a recommended amount of the therapeutic compound and / or a recommended composition of the therapeutic compound for injection into the eye.

[0017]

[0017] The system can be configured to track the progression of any suitable retinal disease. For example, in some embodiments of the system, retinal diseases that can be tracked may include pigment epithelial detachment, drusen, chorioretinal eye disease such as AMD, ocular histoplasmosis, myopia, central serous retinopathy, central serous choroidopathy, glaucoma, diabetic retinopathy, retinitis pigmentosa, optic neuritis, epiretinal membrane, vascular abnormalities and / or obstructions, choroidal dystrophies, retinal dystrophies, macular holes, or choroidal or retinal degenerations.

[0018]

[0018] In another aspect, a method for tracking progression of a retinal disease in a subject includes receiving, by a computing system, optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina having a suitable imaging frequency (e.g., at least once every two weeks, at least once a week, at least once every three days, at least once every two days, at least once a day). The retinal OCT image data is processed by the computer system to determine a series of measured extent values, each of the series of measured extent values ​​indicative of a respective extent of the retinal disease. An output indicative of the series of measured extent values ​​is output by the computer system.

[0019]

[0019] In many embodiments of the method, a series of OCT imaging sessions of the retina is performed over a treatment interval of the retinal disease. For example, in some retinal diseases, the treatment interval (e.g., the time span between injections of a therapeutic compound into the subject's eye) can be about one month or more (e.g., 4 weeks, 5 weeks, 6 weeks, 7 weeks, or more). By performing a series of OCT imaging sessions over the treatment interval, visibility is obtained regarding the extent of the retinal disease at the time point between treatments. As a result, the response of the retinal disease between treatment applications can be measured and tracked, thereby providing the treating medical professional with feedback regarding the regression and / or progression of the extent of the retinal disease between treatment applications. The series of OCT imaging sessions can be performed over any suitable time span and at any suitable frequency. For example, the series of OCT imaging sessions can be performed over at least one month or more to cover at least one time span between treatment applications. The series of OCT imaging sessions can have an imaging frequency of at least once every two weeks, at least once a week, at least once every three days, or at least once a day.

[0020]

[0020] In some embodiments of the method, the measured extent values ​​indicate an amount of fluid in the retina. For example, at least one of the series of measured extent values ​​can indicate a length of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​can indicate a depth of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​can indicate a volume of an intraretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​can indicate a length of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​can indicate a depth of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina. At least one of the series of measured extent values ​​can indicate a volume of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina.

[0021]

[0021] In many embodiments, the method includes generating and sending a notification to the subject's designated treating professional in response to progression of the subject's retinal disease to or beyond a selected threshold to enable scheduling of application of a treatment for the subject's retinal disease based on the observed progression of the subject's retinal disease. For example, in some embodiments, the method includes comparing, by the computer system, at least one of the series of measured severity values ​​to a respective threshold severity value, and in response to at least one of the series of measured severity values ​​equaling or exceeding the respective threshold severity value, sending, by the computer system, a communication to the treating professional when at least one of the series of measured severity values ​​exceeds the respective threshold severity value. In some embodiments, the method further includes comparing, by the computer system, at least one of the series of measured severity values ​​to a respective threshold severity value, and in response to at least one of the series of measured severity values ​​equaling or exceeding the respective threshold severity value, triggering, by the computer system, remote treatment of the retinal disease via operation of an implanted pump to infuse the eye with a therapeutic compound.

[0022]

[0022] In many embodiments, the method includes transmitting, by the computer system, at least one of the series of one or more measured extent values ​​to the treating professional to enable the treating professional to track the progression of the retinal disease. In some embodiments, the method includes transmitting, by the computer system, a graph of at least one of the series of measured extent values ​​to the treating professional. In some embodiments, the method includes displaying, by the computer system, at least one of the series of measured extent values ​​to the treating professional.

[0023]

[0023] In some embodiments, the method includes determining, by the computer system, a parameter describing the extent of the subject's retinal disease between treatment applications. For example, in some embodiments, the method includes measuring and tracking the extent of intraretinal fluid in the retina. For example, in some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, the second treatment for the retinal disease being subsequent to the first treatment for the retinal disease and consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) calculating, by the computer system, at least one intraretinal fluid presence interval during which, within the treatment interval, an amount of intraretinal fluid is detected in each of a series of OCT imaging sessions of the retina achieved within the intraretinal fluid presence interval. The method may include calculating, by the computer system, a fluid absence interval during which, within the treatment interval, an amount of intraretinal fluid is not detected in each of a series of OCT imaging sessions of the retina achieved within the fluid absence interval. In some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, and where the treatment interval spans from the first treatment date to the second treatment date; and (c) calculating, by the computer system, an intraretinal fluid regression interval, where the amount of intraretinal fluid detected in a series of OCT imaging sessions of the retina within the treatment interval decreases in volume during the intraretinal fluid regression interval.In some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, and where the treatment interval spans from the first treatment date to the second treatment date; and (c) calculating, by the computer system, an intraretinal fluid increase interval, where within the treatment interval, the amount of intraretinal fluid detected in a series of OCT imaging sessions of the retina increases in volume during the intraretinal fluid increase interval. In some embodiments, the method includes: (a) storing, by the computer system, a first treatment date for a first treatment for a retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for a retinal disease, the second treatment for the retinal disease following the first treatment for the retinal disease and contiguous with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) determining, by the computer system, a maximum thickness of intraretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval. In some embodiments, the method includes: (a) storing, by the computer system, a first treatment date for a first treatment for a retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, the second treatment for the retinal disease following the first treatment for the retinal disease and contiguous with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) determining, by the computer system, a maximum volume of intraretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval.

[0024]

[0024] In some embodiments, the method includes measuring the extent of subretinal fluid in the retina. For example, in some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, the second treatment for the retinal disease following the first treatment for the retinal disease and consecutive with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) calculating, by the computer system, at least one subretinal fluid presence interval during which a volume of subretinal fluid is detected in each of a series of OCT imaging sessions of the retina achieved within the subretinal fluid presence interval within the treatment interval. In some embodiments, the method includes calculating, by the computer system, a subretinal fluid absence interval during which a volume of subretinal fluid is not detected in each of a series of OCT imaging sessions of the retina achieved within the subretinal fluid absence interval within the treatment interval. In some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, and where the treatment interval spans from the first treatment date to the second treatment date; and (c) calculating, by the computer system, a subretinal fluid regression interval, where the amount of subretinal fluid detected in a series of OCT imaging sessions of the retina within the treatment interval decreases in volume during the subretinal fluid regression interval.In some embodiments, the method includes (a) storing, by the computer system, a first treatment date for a first treatment for the retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for the retinal disease, where the second treatment for the retinal disease follows the first treatment for the retinal disease and is consecutive with the first treatment for the retinal disease, and where the treatment interval spans from the first treatment date to the second treatment date; and (c) calculating, by the computer system, a subretinal fluid increase interval, where within the treatment interval, the amount of subretinal fluid detected in a series of OCT imaging sessions of the retina increases in volume during the subretinal fluid increase interval. In some embodiments, the method includes: (a) storing, by the computer system, a first treatment date for a first treatment for a retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for a retinal disease, the second treatment for the retinal disease following the first treatment for the retinal disease and contiguous with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) determining, by the computer system, a maximum thickness of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval. In some embodiments, the method includes: (a) storing, by the computer system, a first treatment date for a first treatment for a retinal disease; (b) storing, by the computer system, a second treatment date for a second treatment for a retinal disease, the second treatment for the retinal disease following the first treatment for the retinal disease and contiguous with the first treatment for the retinal disease, the treatment interval spanning from the first treatment date to the second treatment date; and (c) determining, by the computer system, a maximum volume of a subretinal fluid volume detected via a series of OCT imaging sessions of the retina during the treatment interval.

[0025]

[0025] In some embodiments, the method includes monitoring, by a computer system, the subject's compliance with a specified schedule for imaging the subject's retina. For example, in some embodiments of the method, the OCT imaging data includes imaging date data indicating an occurrence date of each of the series of retinal OCT imaging sessions, and the method includes (a) processing, by the computer system, the imaging date data to monitor non-compliance by the subject with the specified schedule for performing the series of retinal OCT imaging sessions, and (b) in response to detecting non-compliance by the subject with the specified schedule for performing the series of retinal OCT imaging sessions, sending, by the computer system, a reminder to the subject to follow the specified schedule for performing the series of retinal OCT imaging sessions.

[0026] In some embodiments, the method includes evaluating, by a computer system, a severity of a retinal disease in a subject. For example, in some embodiments, the method includes generating, by the computer system, a severity score indicative of a severity of the retinal disease based on the OCT imaging data.

[0027]

[0027] In some embodiments, the method includes generating, by a computer system, a recommendation for a treatment of a retinal disease for the subject. For example, in some embodiments, the method includes generating, by a computer system, a recommendation for a treatment of a retinal disease based on the OCT imaging data. The treatment recommendation can include a recommended date for injecting a therapeutic compound into the eye. The treatment recommendation can include a recommended amount of the therapeutic compound for injection into the eye and / or a recommended composition of the therapeutic compound for injection into the eye.

[0028]

[0028] The method may include tracking, by the computer system, the status of any suitable retinal disease. For example, retinal diseases that may be tracked via the method include pigment epithelial detachment, drusen, chorioretinal eye disease such as AMD, ocular histoplasmosis, myopia, central serous retinopathy, central serous choroidopathy, glaucoma, diabetic retinopathy, retinitis pigmentosa, optic neuritis, epiretinal membrane, vascular abnormalities and / or obstructions, choroidal dystrophies, retinal dystrophies, macular holes, or choroidal or retinal degenerations.

[0029] In another aspect, a system for managing treatment of a retinal disease of an eye of a subject includes a communications unit, at least one processor, and a tangible storage device. The communications unit is configured to receive optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina. The tangible storage device stores non-transitory instructions executable by the at least one processor to (a) process the OCT image data of the retina to determine a series of measured retinal fluid extent values, and (b) generate a treatment recommendation for the subject via execution of a treatment algorithm using input based on the series of measured retinal fluid extent values.

[0030]

[0030] The treatment algorithm may be formulated using any suitable technique. For example, the treatment algorithm may be a machine learning algorithm trained using training data that includes a plurality of sets of retinal fluid level values. In some embodiments, the training data further includes treatment application data associated with the plurality of sets of retinal fluid level values.

[0031]

[0031] The treatment recommendation may be any suitable treatment recommendation for a retinal eye disease. For example, the treatment recommendation may include a recommended date for a therapeutic injection into the subject's eye, a recommended compound for the therapeutic injection, and / or a recommended amount of the recommended compound for the therapeutic injection.

[0032] In another aspect, a system for predicting retinal disease progression in an eye of a subject includes a communications unit, at least one processor, and a tangible storage device. The communications unit is configured to receive optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina. The tangible storage device stores non-transitory instructions executable by the at least one processor to (a) process the OCT image data of the retina to determine a series of measured retinal fluid extent values, and (b) generate a prediction of retinal disease progression by execution of a predictive algorithm using input based on the series of measured retinal fluid extent values.

[0033]

[0033] The predictive algorithm may be formulated in any suitable manner. For example, the predictive algorithm may be a machine learning algorithm trained using training data comprising a plurality of series of retinal fluid level values. In some embodiments, the training data further comprises treatment application data associated with the plurality of series of retinal fluid level values.

[0034]

[0034] The prediction algorithm can be configured to generate any suitable prediction of retinal ocular disease progression. For example, the prediction of retinal disease progression can include a predicted therapeutic response of the subject to a treatment for the retinal disease. In some embodiments, the treatment for the retinal disease includes an injection of a therapeutic compound into the subject's eye. In some embodiments, the prediction of retinal disease progression includes a predicted progression of an increase in a series of retinal fluid level values.

[0035]

[0035] In another aspect, a system for classifying a retinal disease of an eye of a subject includes a communication unit, at least one processor, and a tangible storage device. The communication unit is configured to receive optical coherence tomography (OCT) image data of the subject's retina for each of a series of OCT imaging sessions of the retina. The tangible storage device stores non-transitory instructions executable by the at least one processor to (a) process the OCT image data of the retina to determine a series of measured retinal fluid extent values, and (b) determine a classification of a retinal disease of the subject's eye by execution of a classification algorithm using an input based on the series of measured retinal fluid extent values. In many embodiments, the classification is selected from a plurality of predefined classifications.

[0036]

[0036] The classification algorithm may be formulated in any suitable manner. For example, the classification algorithm may be a machine learning algorithm trained using training data that includes a plurality of sets of retinal fluid level values. In some embodiments, the training data further includes treatment application data associated with the plurality of sets of retinal fluid level values.

[0037]

[0037] The classification algorithm can be configured to generate any suitable classification of the retinal disease of the subject's eye. For example, in some embodiments, the classification of the retinal disease of the subject's eye indicates the severity of the retinal disease of the subject's eye. In some embodiments, the classification of the retinal disease of the subject's eye indicates the efficacy of a treatment for the retinal disease of the subject's eye.

[0038] For a more complete understanding of the nature and advantages of the present invention, reference should be made to the following detailed description and the accompanying drawings. [Brief description of the drawings]

[0039] [Figure 1] 1 is a simplified schematic diagram of a method for monitoring retinal disease in a subject, according to an embodiment. [Diagram 2] 2 is a simplified schematic diagram of an approach for achieving the method of FIG. 1, according to an embodiment. [Diagram 3] 1 is an OCT-generated image of a retina with intraretinal fluid, the image being generated via a remote OCT imaging device according to an embodiment. [Figure 4] 1 is an OCT-generated image of a retina with subretinal fluid, the image being generated via a remote OCT imaging device according to an embodiment. [Diagram 5] FIG. 3 illustrates an exemplary report that can be generated by the technique of FIG. 2 for use by a treating medical professional. [Figure 6] FIG. 3 illustrates another exemplary report that can be generated by the technique of FIG. 2 for use by a treating medical professional. [Figure 7] FIG. 3 illustrates another exemplary report that can be generated by the technique of FIG. 2 for use by a treating medical professional. [Figure 8] 1 is a graph of intraretinal and subretinal fluid volume over days between treatments, according to an embodiment. [Figure 9] FIG. 3 is a graph of several different rates of progression of retinal fluid that can be tracked and identified via the technique of FIG. 2. [Figure 10] FIG. 3 is a graph of several different types of responders to therapeutic applications that can be tracked and identified via the approach of FIG. 2. [Figure 11] FIG. 3 is a graph showing non-responders to therapeutic applications that can be tracked and identified via the approach of FIG. 2. [Figure 12A] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory in relation to pre-scheduled retreatment intervals; [Figure 12B] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory associated with pre-scheduled retreatment intervals. [Figure 13A] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals; [Figure 13B] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals; [Figure 13C]FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals; [Figure 13D] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals; [Figure 13E] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals; [Figure 13F] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory and associated customized retreatment intervals. [Figure 14] FIG. 1 illustrates an approach for predictive modeling of treatment outcome using periodic OCT-generated images of the retina, according to an embodiment. [Figure 15] 1 is a flowchart of a method of an approach for generating treatment-related parameters using periodic OCT-generated images of the retina, according to an embodiment. [Figure 16] FIG. 13 illustrates exemplary retinal fluid volume trajectory segments that are automatically identified in an embodiment. [Figure 17A] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory with automatically identified segments; [Figure 17B] FIG. 1 is a plot of an exemplary retinal fluid volume trajectory with automatically identified intervals. [Figure 18A] FIG. 2 is a plot of an exemplary segmented retinal fluid volume trajectory including a first response interval and a second response interval. [Figure 18B] FIG. 18B is a fluid volume map of an OCT retinal image acquired during a first response interval of the retinal fluid volume trajectory of FIG. 18A. [Figure 18C] FIG. 18B is a fluid volume map of an OCT retinal image acquired during a second response interval of the retinal fluid volume trajectory of FIG. 18A. [Figure 19A] 1 is a plot of an exemplary retinal fluid volume trajectory of a portion of a patient's retina, the trajectory including a first activation interval, a first response interval, a second activation interval, and a second response interval. [Figure 19B] FIG. 19B is a fluid volume map of an OCT retinal image acquired during the first activation interval of FIG. 19A. [Figure 19C]FIG. 19B is a fluid volume map of an OCT retinal image obtained during the second activation interval of FIG. 19A. [Figure 20A] FIG. 13 is a plot of an exemplary retinal fluid volume trajectory for a portion of the retina of another patient. [Figure 20B] FIG. 20B is a fluid volume map of an OCT retinal image acquired during the first activation interval of FIG. 20A. [Figure 20C] FIG. 20B is a fluid volume map of an OCT retinal image acquired during the first response interval of FIG. 20A. [Figure 20D] FIG. 20B is a fluid volume map of an OCT retinal image obtained during the second activation interval of FIG. 20A. [Figure 20E] FIG. 20B is a fluid volume map of an OCT retinal image obtained during the second response interval of FIG. 20A. [Figure 20F] FIG. 20B is a treatment response fluid loss percentage map for the first response interval of FIG. 20A. [Figure 20G] FIG. 20B is a therapeutic response fluid loss percentage map for the second response interval of FIG. 20A. [Figure 20H] FIG. 20B is a first half-life fluid volume map of the retinal fluid volume trajectory of FIG. 20A. [Figure 20I] FIG. 20B is a second half-life fluid volume map of the retinal fluid volume trajectory of FIG. 20A. [Figure 21A] FIG. 13 is a plot of an exemplary retinal fluid volume trajectory for a portion of the retina of another patient. [Figure 21B] FIG. 21B is a fluid volume map of an OCT retinal image acquired during the first activation interval of FIG. 21A. [Figure 21C] FIG. 21B is a fluid volume map of an OCT retinal image acquired during the first response interval of FIG. 21A. [Figure 21D] FIG. 21B is a fluid volume map of an OCT retinal image obtained during the second activation interval of FIG. 21A. [Figure 21E] FIG. 21B is a fluid volume map of an OCT retinal image acquired during the second response interval of FIG. 21A. [Figure 21F] FIG. 21B is a therapeutic response fluid loss percentage map for the first response interval of FIG. 21A. [Figure 21G] FIG. 21B is a therapeutic response fluid loss percentage map for the second response interval of FIG. 21A. [Fig. 21H] FIG. 21B is a first half-life fluid volume map of the retinal fluid volume trajectory of FIG. 21A. [Figure 21I] FIG. 21B is a second half-life fluid volume map of the retinal fluid volume trajectory of FIG. 21A. [Figure 22A] FIG. 13 is a plot of an exemplary retinal fluid volume trajectory for a portion of the retina of another patient. [Figure 22B] FIG. 22B is a final fluid volume map of the OCT retinal image acquired during the first response interval of FIG. 22A. [Figure 22C] FIG. 22B is a therapeutic response fluid loss percentage map for the first response interval of FIG. 22A. [Figure 22D] FIG. 22B is a final fluid volume map of the OCT retinal image acquired during the second response interval of FIG. 22A. [Figure 22E] FIG. 22B is a therapeutic response fluid reduction percentage map for the second response interval of FIG. 22A. [Figure 22F] FIG. 22B is a final fluid volume map of the OCT retinal image acquired during the third response interval of FIG. 22A. [Figure 22G] FIG. 22B is a therapeutic response fluid loss percent map for the third response interval of FIG. 22A. [Figure 23A] 1 is a fluid volume map of an activation interval of a patient; [Figure 23B] 1 is a fluid volume map of an activation interval of a patient; [Figure 23C] 1 is a fluid volume map of an activation zone of a patient. [Figure 24A] FIG. 1 shows activation at different locations in a patient's retina; [Figure 24B] FIG. 1 shows activation at different locations in a patient's retina; [Figure 24C] FIG. 1 shows activation at different locations in a patient's retina; [Figure 24D] FIG. 1 shows activation at different locations in a patient's retina; [Figure 24E] FIG. 1 shows activation at different locations in a patient's retina. [Diagram 25]FIG. 1 illustrates an exemplary graphical user interface of a system for use with home OCT retinal imaging to manage retinal disease in a patient, according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040]

[0090] Various embodiments of the present invention are described below. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the present invention can be practiced without the specific details. Furthermore, well-known features may be omitted or simplified so as not to obscure the embodiments being described.

[0041]

[0091] Introduction

[0042]

[0092] Many subjects with retinal disease are treated with intraocular injections according to general guidelines based on the average subject. The progression of retinal disease may progress differently in any particular subject than the average subject. Furthermore, a particular subject may respond to treatment differently than the average subject. Thus, there is a strong clinical need to monitor the progression of retinal disease in some subjects on a short interval basis so that the subject can receive treatment based on their disease progression. Ophthalmic imaging devices using optical coherence tomography (OCT) imaging are often used in ophthalmology clinics to image the subject's retina to assess the subject's retinal health and / or evaluate the subject's retinal disease status. However, the need to go to an ophthalmology clinic may prevent the achievement of repeated imaging of the subject's retina at sufficiently short intervals suitable for adequately monitoring the subject's retinal disease status. Retinal diseases that may be amenable to management via repeated OCT imaging on a short interval basis include, but are not limited to, chorioretinal ocular diseases such as AMD, ocular histoplasmosis, myopia, central serous retinopathy, central serous choroidopathy, glaucoma, diabetic retinopathy, retinitis pigmentosa, optic neuritis, epiretinal membranes, vascular abnormalities and / or obstructions, choroidal dystrophies, retinal dystrophies, macular holes, or choroidal or retinal degenerations.

[0043]

[0093] In many embodiments described herein, an affordable OCT-based ophthalmic imaging device is used by a subject during a series of OCT imaging sessions performed on a short interval basis to generate a corresponding series of OCT images of the subject's retina to monitor the subject's retinal disease status. As used herein, "short interval basis" refers to any suitable interval between OCT imaging sessions of a series of OCT imaging sessions to generate one or more OCT images of the subject during a period between treatments of the subject's retinal disease (e.g., injections of a therapeutic compound into the subject's eye), which are often spaced at least four weeks apart. Each subject may have a particular maximum interval between OCT image generation of the subject's retina that is sufficient to adequately monitor the subject's retinal disease status. The maximum interval between OCT image generation of the subject's retina for a particular subject may be selected by the medical professional treating the subject. The maximum interval for generating OCT images of the subject's retina for a particular subject may include, but is not limited to, at least once a day, at least once every two days, at least once every three days, at least once a week, and at least once every two weeks. Short-interval-based OCT imaging of a subject's retina may allow for improved monitoring of a subject's retinal disease status and development of customized treatment plans for the subject. In some embodiments, short-interval-based OCT imaging of a subject's retina is used to determine when to trigger remote treatment by injecting a therapeutic compound into the subject's eye via a remote pump fluidically coupled to the subject's eye.

[0044]

[0094] Any suitable parameter or combination of suitable parameters can be used to track the progression of retinal disease in a subject.For example, in some embodiments described herein, the fluid volume (e.g., intraretinal fluid volume, subretinal fluid volume) in a subject's retina is measured through short-interval-based OCT imaging of the subject's retina using an OCT-based ophthalmic imaging device that is used remotely (e.g., at home) by the subject.

[0045]

[0095] In some embodiments, the amount of fluid in the subject's retina is plotted to graphically show how the amount of fluid in the subject's retina changes on a selected periodic basis (e.g., daily). The resulting plot can be used to explain the effect of applying a suitable therapeutic compound and / or treatment on the subject's retinal disease state over time. The resulting plot can also be used to explain the difference in the effect of applying a suitable therapeutic compound and / or treatment on different subject's retinal disease states over time.

[0046]

[0096] Any suitable biomarker that indicates the subject's retinal disease state, as well as the progression and remission of the extent of the biomarker, can be evaluated using the systems and / or techniques described herein. Suitable biomarkers include, but are not limited to, intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), drusen, and macular holes.

[0047]

[0097] In some embodiments, the dataset indicating the degree of suitable biomarkers is evaluated by an algorithm that generates a recommendation regarding the next application of a treatment to treat the subject's retinal disease. The generated recommendation can include, for example, a recommended date for the next clinic visit to evaluate and / or treat the subject's retinal disease, a recommended date for the next injection of a therapeutic compound into the subject's eye to treat the subject's retinal disease, and / or a recommended type of therapeutic compound (e.g., injection volume, drug combination).

[0048]

[0098] Remote retinal imaging OCT system based retinal disease tracking

[0049]

[0099] Referring now to the drawings, where like reference numbers represent like parts throughout the several views, FIG. 1 shows a simplified schematic diagram of a method 10 for monitoring retinal disease in a subject, according to an embodiment. Method 10 relates to generating an output to a subject's treating professional for use in managing treatment for the subject's retinal disease based on short-interval-based OCT imaging of the subject's retina. The short-interval-based OCT imaging of the subject's retina generates a series of OCT images of the subject's retina. Method 10 can be used to remotely monitor the status of a subject's retinal disease over any suitable period of time, such as between clinic visits and / or between administrations of treatment for the subject's retinal disease.

[0050]

[0100] In operation 12, a first OCT image (OCT image(1)) of the subject's retina is generated. The first OCT image can be generated at a suitable interval (e.g., an interval described herein) after the start of a monitoring period, such as after administration of a treatment (e.g., injection of a therapeutic compound into the subject's eye) or after clinically-based OCT imaging of the subject's retina.

[0051]

[0101] In operation 14, the first OCT image is processed to measure one or more biomarkers indicative of the subject's retinal disease status. Any suitable number and / or type of biomarkers may be measured, including but not limited to those described herein.

[0052]

[0102] In operation 16, the biomarkers measured in the first OCT image are compared to selected limits for the biomarkers. If the biomarkers measured in the first OCT image exceed the selected limits for the biomarkers, an alert is generated and output to notify the occurrence of the exceedance. In many embodiments, the alert is output to a medical professional treating the subject's retinal disease.

[0053]

[0103] In act 18, a counter is set to 2 for use in generating and processing a second OCT image of the subject's retina in method 10. In act 30, the counter is incremented for generating and processing subsequent OCT images of the subject's retina in method 10. Acts 20-26 are repeated for each value of the counter.

[0054]

[0104] In operation 20, an OCT image (counter) of the subject's retina is generated. The OCT images (counter) may be generated at suitable intervals (e.g., intervals described herein) following generation of the OCT image (counter-1).

[0055]

[0105] In operation 22, the OCT images (counters) are processed to measure one or more biomarkers indicative of a retinal disease state of the subject. In many embodiments, the biomarkers measured are the same as those measured in each OCT image of the series of OCT images measured in method 10.

[0056]

[0106] In operation 24, the biomarkers measured in the OCT images (counter) are compared to selected limits for the biomarkers. If the biomarkers measured in the OCT images (counter) exceed the selected limits for the biomarkers, an alarm is generated and output to notify the occurrence of the exceedance. In many embodiments, the alarm is output to a medical professional treating the subject's retinal disease.

[0057]

[0107] In operation 26, one or more parameters indicative of a change in the magnitude of the biomarker from OCT image (counter-1) to OCT image (counter) are calculated. The calculated parameters reflect whether the subject's retinal disease condition has improved (e.g., as indicated by a decrease in the magnitude of the biomarker) from OCT image (counter-1) to OCT image (counter) or whether the subject's retinal disease condition has worsened (e.g., as indicated by an increase in the magnitude of the biomarker) from OCT image (counter-1) to OCT image (counter). The one or more calculated parameters may include any suitable parameters calculated from the measured biomarkers, including but not limited to those described herein.

[0058]

[0108] In act 28, if the OCT image (counter) is the last in the specified series of interim OCT images of the target retina, method 10 proceeds to act 32. If the OCT image (counter) is not the last in the specified series of interim OCT images of the target retina, method 10 proceeds to act 30, where a counter is incremented for generating and processing the next OCT image by repeating the performance of acts 20-28 for the next OCT image in the series of OCT images of the target retina. Acts 20-28 are repeated until the last OCT image in the series of OCT images has been generated and processed. When the last OCT image in the series of OCT images has been generated and processed, method 10 proceeds to act 32.

[0059]

[0109] The one or more biomarker values ​​and / or calculated parameters indicative of changes in the biomarkers between successive pairs of OCT images are output in operation 32. The one or more biomarker values ​​and / or calculated parameters may be output to any appropriate recipient, including, but not limited to, a medical professional engaged in the management and / or treatment of the subject's retinal disease.

[0060]

[0110] In operation 34, a recommendation for treatment of the subject's retinal disease is formulated based on the values ​​of the one or more biomarkers and / or the calculated parameters. The recommendation may include, but is not limited to, (a) a recommended date for injecting the therapeutic compound into the eye, (b) a recommended amount of the therapeutic compound for injection into the eye, and / or (c) a recommended composition of the therapeutic compound for injection into the eye.

[0061]

[0111] 2 shows a simplified schematic diagram of a technique 40 for accomplishing method 10, according to an embodiment. Technique 40 includes short-interval based repeated generation 42 of OCT image data of a subject's retina by an OCT-based ophthalmic imaging device 44. In many embodiments, the generation 42 of OCT image data can be repeated at any suitable interval as described herein. Any suitable OCT-based ophthalmic imaging device can be used as imaging device 44, including, but not limited to, affordable subject-operable OCT imaging devices that can be used remotely (e.g., at home) by the subject.

[0062]

[0112] The OCT image data for each imaging session of the subject's retina is transmitted (46) to a retinal disease management system 48. In many embodiments, the OCT image data is transmitted separately to the retinal disease management system 48 for each imaging session of the subject's retina. For example, in some cases, the subject is instructed to use the imaging device daily, and the OCT image data obtained for the subject's retina is transmitted to the retinal disease management system 48 for each daily imaging session.

[0063]

[0113] In many embodiments, the retinal disease management system 48 is web-based and configured to manage multiple aspects of tracking the status of a retinal disease in each of multiple subjects. Aspects of tracking the status of a retinal disease in each of a plurality of subjects that may be managed via the retinal disease management system 48 include, but are not limited to, 1) providing an instance of the imaging device 44 to each of one or more subjects monitored via the retinal disease management system 48; 2) obtaining and storing identification and treatment-related data for each of the subjects monitored via the retinal disease management system 48; 3) providing subject education regarding use of the imaging device 44 and / or treatment of the subject's retinal disease; 4) monitoring compliance with periodic imaging interval requirements (e.g., daily imaging requirements) by each of the subjects monitored by the retinal disease management system 48; 5) providing assistance to users (e.g., subjects, treating physicians) of the retinal disease management system 48 via online assistance and / or call-in telephone assistance; 6) obtaining and storing identification and treatment-related data for each of the treating physicians of the subjects monitored via the retinal disease management system 48; 7) providing education to the treating physicians regarding the treatment of the subject's retinal disease; 8) storing, and obtaining from each treating physician, parameters underlying monitoring reports and subject alerts generated by the retinal disease management system 48 and transmitted to the treating professional for each of the subjects monitored via the retinal disease management system 48; and / or 9) generating and transmitting recommended treatment parameters. In some embodiments, the recommended treatment parameters include any suitable combination of a recommended date for the next clinic visit for evaluation and / or treatment of the subject's retinal disease, a recommended date for the next injection of a therapeutic compound into the subject's eye to treat the subject's retinal disease, and a recommended type of therapeutic compound (e.g., injection volume, drug combination).

[0064]

[0114] In many embodiments, the retinal disease management system 48 monitors each subject's compliance with the subject's imaging schedule. The imaging schedule (e.g., calls for routine use of the imaging device 44 by the subject) can be selected by the subject's treating physician. The retinal disease management system 48 can monitor compliance by the subject by comparing receipt of the subject's OCT imaging data to the imaging schedule. If OCT imaging data is not received from the subject according to the subject's imaging schedule, the retinal disease management system 48 can generate and send a compliance reminder 50 to the subject to remind the subject of the need to use the imaging device 44 according to the subject's imaging schedule.

[0065]

[0115] In some embodiments, the retinal disease management system 48 processes the OCT imaging data generated for each imaging session to determine how much intraretinal and / or subretinal fluid is trapped within the retina of each subject. Figure 3 shows an exemplary OCT-generated image of a retina with intraretinal fluid 52. Figure 4 shows an exemplary OCT-generated image of a retina with subretinal fluid 54.

[0066]

[0116] In many embodiments, the retinal disease management system 48 processes the OCT image data for each of the imaging sessions for each respective subject and generates and transmits monitoring and / or alert reports 56 based on the OCT image data received for the subject to the subject's treating physician. Figures 5, 6 and 7 each show exemplary alert reports (56a, 56b, 56c) that may be generated by the retinal disease management system 48 and transmitted to the respective treating physician. Each of the alarm reports (56a, 56b, 56c) includes an indication of the presence of intraretinal fluid output (58a, 58b, 58c), an indication of the presence of subretinal fluid output (60a, 60b, 60c), a subject name output (62a, 62b, 62c), a subject birth date output (64a, 64b, 64c), an imaging date output (66a, 66b, 66c), an indication of the imaged eye output (68a, 68b, 68c), cross-sectional OCT images of the retina (70a, 70b, 70c), images ordered by cross-sectional area of ​​the fluid detected. 5 includes planar intraretinal fluid maps (70b, 70c) of intraretinal fluid within the imaged retina (72a, 72b, 72c), planar subretinal fluid maps (74a, 74b, 74c) of subretinal fluid within the imaged retina, alarm criteria (76a, 76b, 76c) for triggering generation of an alarm report (56a, 56b, 56c), and plots (78a, 78b, 78c) illustrating the variation of one or more tracked biomarkers indicative of the extent of retinal disease in the subject. The plots (78a, 78b, 78c) may illustrate any suitable tracked biomarkers indicative of the extent of retinal disease in the subject, or any suitable combination of two or more suitable tracked biomarkers indicative of the extent of retinal disease in the subject. In FIG. 5, plot 78a illustrates the average fluid thickness for both intraretinal and subretinal fluid within the imaged retina. In alert report 56a, plot 78a shows a decrease in thickness of both intraretinal and subretinal fluid in the imaged retina after injection of a therapeutic compound into the eye, followed by a period of relatively low intraretinal and subretinal fluid thickness, followed by an increase in thickness of both intraretinal and subretinal fluid. In FIG. 6, plot 78b shows the average fluid volume for both intraretinal and subretinal fluid in the imaged retina. In alert report 56b, plot 78b shows a period of relatively low intraretinal and subretinal fluid volume, followed by an increase in intraretinal fluid volume.7, plot 78c shows the average fluid volume for both intraretinal and subretinal fluid in the imaged retina. In alert report 56c, plot 78c shows that the subretinal fluid volume is relatively constant and the intraretinal fluid volume is negligible.

[0067]

[0117] 8 illustrates a plot 80 of intraretinal fluid volume values ​​82 and subretinal fluid volume values ​​84 over a number of days between treatments, according to an embodiment. The retinal disease management system 48 can process OCT image data from each imaging session of the subject's retina using techniques described herein to determine each of the intraretinal fluid volume values ​​82 and each of the subretinal fluid volume values ​​84. The retinal disease management system 48 can be configured to process the intraretinal fluid volume values ​​82 and / or subretinal fluid volume values ​​84 to determine a fluid regression interval (FRI) 86, a fluid present interval (FPI) 88, a fluid absent interval (FAI) 90, and a fluid increase interval (FII) 92. The fluid regression interval (FRI) 86 is the interval of time from treatment application (e.g., injection of a therapeutic compound into the subject's eye) during which retinal fluid (e.g., intraretinal fluid, subretinal fluid) is present but has decreased in volume to below a measurable level or a selected minimum reference volume. Fluid Present Interval (FPI) 88 is the time interval from pre-treatment application to post-treatment application during which retinal fluid is present in a measurable amount or above a selected minimum reference volume. Fluid Absent Interval (FAI) 90 is the time interval between two consecutive treatment applications during which retinal fluid is not present in a measurable amount or is below a selected minimum reference volume. Fluid Increase Interval (FII) 92 is the time interval before treatment application during which retinal fluid is above a measurable level or above a selected minimum reference volume and increases in volume. In some embodiments, one or more of FRI 86, FPI 88, FAI 90, and / or FII 92 are used alone and / or in combination as input parameters to an algorithm used to formulate a treatment recommendation for the subject, and / or output to a treating professional to further quantify the subject's retinal disease state and / or how the subject responds to one or more treatment applications.

[0068]

[0118] 9 illustrates a graph of several different exemplary progression rates of retinal fluid accumulation that may be tracked and identified via method 10. The illustrated retinal fluid progression rates show the progression of retinal fluid from 0.0 nanometers to 50.0 nanometers and include an exemplary lower progression curve 94, an exemplary intermediate progression curve 96, and a higher progression curve 98. In the lower progression curve 94, it takes approximately 30 days for the detected retinal fluid volume to increase from 0.0 nanometers to 30.0 nanoliters, and approximately another 10 days for the retinal fluid volume to increase from 30.0 nanoliters to 50.0 nanoliters. In the intermediate progression curve 96, it takes approximately 22 days for the detected retinal fluid volume to increase from 0.0 nanometers to 30.0 nanoliters, and approximately another 7.5 days for the retinal fluid volume to increase from 30.0 nanoliters to 50.0 nanoliters. At the higher progression curve 98, it takes approximately 15 days for the amount of detected retinal fluid to increase from 0.0 nanoliters to 30.0 nanoliters, and approximately another 5 days for the amount of retinal fluid to increase from 30.0 nanoliters to 50.0 nanoliters.

[0069]

[0119] The different exemplary retinal fluid progression rates shown in Figure 9 have different associated treatment windows resulting from the different retinal fluid progression rates. For example, if the criteria for generating an alarm report 56 is set to a retinal fluid volume reaching 30 nanoliters, the number of additional days until the retinal fluid volume reaches 50 nanoliters will vary with the different exemplary progression rates as described above (i.e., only 5 days for the higher progression curve 98 as opposed to 10 days for the lower progression curve 94). To administer treatment before the retinal fluid level exceeds the selected criteria level (e.g., 50 nanoliters) and avoid damage induced by excessive retinal fluid volume, the next scheduled treatment application date may be prioritized for the higher proposed retinal fluid progression rate over the lower proposed retinal fluid progression rate.

[0070]

[0120] In some embodiments, the criteria for generating and sending the alert report 56 to the treating physician account for differences in progression rates. For example, the criteria for triggering the generation and sending of the alert report 56 to the treating physician can account for the proposed daily retinal fluid progression rates between the most recent imaging of the retina and the previous day's imaging of the retina, with the triggering retinal fluid volume for higher retinal fluid volume progression rates being relatively low. For example, if a 10-day treatment window is desired, a look-up table can be configured and accessed such that different proposed retinal fluid volume progression rates each trigger the generation and sending of the alert report 56 to the treating physician, leaving an expected 10-day treatment window before the predicted retinal fluid volume exceeds a selected maximum pre-treatment retinal fluid volume. Assuming a maximum pre-treatment retinal fluid volume of 50 nanometers and a desired 10 day treatment window, the increase in retinal fluid volume from day 29 to day 30 (approximately 1.72 nanometers / day) of the lower retinal fluid volume progression curve 94 can be associated with an alarm limit of 30.0 nanometers, which is met on day 30, thereby triggering the generation and transmission of an alarm report 56 on day 30, thereby leaving 10 days before the retinal fluid volume is predicted to reach the selected maximum pre-treatment retinal fluid volume of 50 nanometers. For the intermediate retinal fluid volume progression curve 96, the increase in retinal fluid volume from day 18 to day 19 (approximately 2.02 nanometers / day) can be associated with an alarm limit of 23.4 nanometers, which is met on day 19, thereby triggering the generation and transmission of an alarm report 56 on day 19, thereby leaving 10.5 days before the retinal fluid volume is predicted to reach the selected maximum pre-treatment retinal fluid volume of 50 nanometers. In the higher retinal fluid volume progression curve 98, the increase in retinal fluid volume from day 9 to day 10 (approximately 2.40 nanometers / day) can be associated with an alarm limit of 15.0 nanometers, which is met on day 10, thereby triggering the generation and transmission of an alarm report 56 on day 10, thereby leaving 10.0 days before the retinal fluid volume is predicted to reach the selected maximum pre-treatment retinal fluid volume of 50 nanometers.

[0071]

[0121] FIG. 10 shows a graph of several different types of responders to a treatment application that can be tracked and identified via method 10. The types of responders shown include an exemplary intermediate responder 100, an exemplary fast responder 102, and an exemplary slow responder 104. In the exemplary intermediate responder 100 shown, the fluid volume decreases from an initial 240 nanometers to 50 nanometers in about 10 days. In the exemplary fast responder 102, the fluid volume decreases from an initial 240 nanometers to 50 nanometers in about 7 days. In contrast, in the exemplary slow responder 104, the fluid volume decreases from an initial 240 nanometers to 50 nanometers in about 27 days. In some embodiments, the observed response of a particular subject to a particular treatment is classified into an appropriate type of response (e.g., one of a fast responder, an intermediate responder, and a slow responder), and the classification of the response is provided to a treatment professional along with a recommendation regarding the subject's future treatment application based on the classification of the subject's response combined with the details of the treatment application that resulted in the observed response.

[0072]

[0122] FIG. 11 is a graph showing exemplary non-responders 110 to treatment applications that can be tracked and identified via method 10. To help illustrate possible subject differences between treatment applications, the graph includes exemplary intermediate responders 112 and fast responders 114. If the retinas of the three illustrated responder types are imaged only in the clinic at the same time as the treatment application (performed on days 0 and 30 in the illustrated example), each of the subjects' retinal diseases will show the same retinal fluid volume. In contrast, by adding additional remote images between treatment applications (performed daily in the illustrated example), the differences in response between the exemplary non-responders 110, exemplary intermediate responders 112, and fast responders 84 are observable and can form at least part of the basis for selecting appropriate subsequent treatments for each of the subjects' retinal diseases, particularly for the non-responders 110, via the selection of different treatment regimens that take into account non-response to previous treatment applications.

[0073]

[0123] Table 1 lists exemplary parameters related to a subject's retinal disease that can be quantified for each OCT imaging of the subject's retina. [Table 1]

[0074]

[0124] The parameters in Table 1 can be used by a treating professional to track and / or plan future treatment of a subject's retina. The parameters in Table 1 can be quantified for each OCT image of a subject's retina using appropriate image processing techniques, including but not limited to the image processing techniques described herein.

[0075]

[0125] Determining the volume of liquid

[0076]

[0126] In many embodiments of method 10, the OCT image data of each imaging session of the subject's retina is processed using appropriate image processing techniques to detect whether retinal fluid is present (e.g., intraretinal and / or subretinal fluid volume), and if so, the volume of retinal fluid. For example, in some embodiments, a selection of parallel OCT B-scans (each corresponding to a cross-sectional OCT image of the retina) are processed to detect whether the B-scan contains an area of ​​retinal fluid, and if so, the area of ​​retinal fluid in the B-scan. The volume of retinal fluid can then be calculated from the fluid area in each of the selection of parallel OCT B-scans, and the distance between adjacent B-scans, using Equation 1.

[0077]

[0127] FV i =(FA i +FA i+1 )*0.5*DT / 2+(FA i +FA i-1 )*0.5*DT / 2(Equation 1) In the formula, FA i B-scan i is the area of ​​retinal fluid in FA i+1 B-scan i+1is the area of ​​retinal fluid in FA i-1 B-scan i-1 is the area of ​​retinal fluid in DT=the distance between adjacent B-scans.

[0078]

[0128] 12A and 12B show exemplary retinal fluid volume trajectory plots showing how pre-scheduled treatment and extended visits can expose the retina to additional harmful fluid accumulation before treatment. When used in conjunction with periodic OCT retinal imaging as described herein, patient-specific fluid volume thresholds and alarms can be used to customize the timing of treatment to the state and progression of a particular patient's retinal disease state, resulting in more timely administration of treatment in response to the active phase of retinal disease where fluid accumulation occurs. As a result of more timely administration of treatment, compared to pre-scheduled treatment, the retina can be less exposed to additional harmful fluid accumulation before treatment, which can result in improved visual outcome.

[0079]

[0129] 13A-13F show plots of exemplary retinal fluid volume trajectories and associated customized treatments applied when indicated by patient-tailored fluid volume thresholds and alarms. As illustrated, patient-tailored treatment timing enabled by home OCT retinal imaging may result in improved management in highly heterogeneous retinal diseases. Furthermore, home OCT retinal imaging may allow for faster extension of retreatment intervals by delaying retreatment until an appropriate threshold of retinal fluid volume is exceeded. Home OCT retinal imaging may also provide disease status / progression data to help determine appropriate treatment (e.g., between more expensive brand-name injectable therapeutic compounds and generic injectable therapeutic compounds, between more expensive and longer-lasting treatments and less expensive and shorter-lasting treatments).

[0080]

[0130] Regular (e.g., daily) at-home OCT retinal imaging can be used to detect intraretinal fluid accumulation and track intraretinal fluid volume and distribution over time. The intraretinal fluid volume and distribution over time can be included in structured input to a treatment algorithm (e.g., a machine learning algorithm) to generate treatment recommendations (e.g., timing of injections, recommended treatment compounds, recommended injection volumes, etc.), treatment alerts, and / or treatment outcome predictions. The structured input to the treatment algorithm can also include treatment data (injection date and / or time, injection compound, injection volume, etc.) and / or visual acuity data.

[0081]

[0131] At-home OCT retinal imaging can be used to generate three-dimensional information about retinal pathology. Daily at-home OCT retinal imaging adds a time dimension to the three-dimensional information, thereby creating a four-dimensional dataset of retinal fluid data. Retinal fluid volume and distribution can be shown on a retinal fluid map, such as a fluid volume thickness map. Changes in retinal fluid volume and distribution can be shown using a series of fluid volume thickness maps and / or a fluid volume change rate map showing changes in fluid volume and distribution between two selected dates and / or times. The determined distribution of fluid in the retina and / or variations in the amount and / or distribution of fluid in the retina over time can be displayed using any suitable technique, such as any of the two-dimensional fluid distribution and / or variation maps described herein.

[0082]

[0132] 14 and 15 illustrate a technique 120 for managing treatment of retinal disease via home OCT retinal imaging. In operation 122, home OCT three-dimensional imaging of the retina (e.g., throughout the volume of the retina) is used on a suitable periodic basis (e.g., once a day) to generate OCT retinal image data for each periodic imaging session of the retina. In operation 124, the OCT retinal image data for each periodic imaging session of the retina is processed to detect the presence of fluid in the retina, the distribution of fluid in the retina, and the amount of fluid in the retina. The home OCT retinal image data for each imaging session can be segmented using suitable known algorithms (e.g., image processing algorithms to detect fluid regions, artificial intelligence (AI) algorithms to achieve fluid localization and fluid volume quantification). In some embodiments, the processing of the home OCT retinal image data for each periodic imaging session of the retina includes processing of depthwise (B-scan) cross-sectional OCT images of the retina to detect fluid regions, and integration of the detected fluid regions (both in depth and radially relative to the optical axis of the eye) to determine the amount and distribution of fluid in the retina. In operation 126, one or more fluid distribution contour maps 128 can be prepared for each day of the series of days, using contours (e.g., color contours, contour lines) to indicate the amount and distribution of fluid accumulation in the retina. The fluid distribution contour maps 128 can be displayed in any suitable combination and order to indicate the variation in the amount and distribution of fluid in the retina over time. In operation 130, a fluid volume trajectory plot 132 is created and displayed, showing the variation of a suitable parameter indicative of the amount of fluid in the retina over time. For example, a suitable algorithm can be used to process the daily home OCT retinal image data to generate a fluid volume trajectory for any suitable retinal fluid volume parameter. In operation 134, the fluid volume quantification data and / or the fluid localization data are processed to generate treatment recommendations (e.g., timing of injections, recommended treatment compounds, recommended injection volumes, etc.). For example, the fluid volume quantification data and the fluid localization data can be input into an AI-based algorithm to generate treatment recommendations and / or vision predictions.The AI-based algorithm can be trained using the fluid volume quantification data, fluid localization data, and treatment data (injection date and time, injection volume, injected compound). In some cases, the treatment data can be obtained from the patient's electronic medical record. The AI-based algorithm can be configured to classify the patient's retinal pathology and inform medical decisions regarding how to treat the patient's retinal pathology. An alert can also be generated in response to the pooled fluid exceeding an appropriate fluid threshold, as described herein. The patient's electronic medical record (EMR) can be updated to include the treatment recommendation and / or alert. In operation 136, the fluid volume quantification data and / or the fluid localization data are processed to predict a treatment outcome, such as a predicted improvement in vision.

[0083]

[0133] FIG. 16 illustrates some exemplary retinal fluid volume trajectory segments that may be automatically identified in some embodiments. The exemplary retinal fluid volume trajectory segments may include one or more activation segments 138, one or more treatment response segments 140, and one or more steady state segments 142. Each activation segment 138 is characterized by an increase in retinal fluid volume. Each treatment response segment 140 is following administration of a treatment (e.g., injection of a therapeutic compound into the eye) and is typically characterized by a decrease in retinal fluid volume resulting from the administration of the treatment. Each steady state segment 142 is characterized by a retinal fluid volume that fluctuates between appropriate thresholds. FIGS. 17A and 17B illustrate plots of exemplary retinal fluid volume trajectories with automatically identified segments.

[0084]

[0134] The retinal fluid volume trajectory may be segmented using any suitable technique. In some embodiments, mathematical / statistical tools are used to identify one or more activation intervals 138 by detecting a trend toward increasing retinal fluid volume, one or more steady state intervals 142 by detecting a trend toward decreasing retinal fluid volume, and one or more treatment response intervals 140 by detecting a trend toward decreasing retinal fluid volume. To smooth the daily rate of fluid volume change and reduce the noise level of the retinal fluid volume trajectory, a polynomial may be fitted to the retinal fluid volume trajectory and analyzed to detect intervals 138, 140, 142. The retinal fluid volume trajectory may be processed to determine the noise level (e.g., mean square error) versus the level of volume change. Treatment events (e.g., therapeutic compound injections) may be automatically identified using any suitable technique (e.g., via import from the patient's electronic medical record). In the absence of treatment records, treatment events may also be manually added to the patient's dataset. Table 2 lists exemplary characteristics of treatment response intervals and activation intervals. Table 3 defines exemplary hydrodynamic parameters applicable to treatment and activation intervals.

[0085]

[0135] Table 2 - Hydrodynamic parameters - activation and response [Table 2]

[0086]

[0136] Table 3 - Hydrodynamic parameters - activation and response [Table 3] [Table 4] [Table 5] [Table 6] [Table 7]

[0087]

[0137] Any suitable combination of retinal fluid parameters described herein can be used as input parameters to any suitable algorithm that generates an output related to the treatment and / or management of a patient's retinal disease. For example, any suitable combination of retinal fluid parameters described herein can be used as input parameters to a reactivation prediction algorithm to generate a prediction of the pattern, timing, and / or evolution of a future reactivation interval 138 of a patient's retinal fluid volume trajectory. Any suitable combination of retinal fluid parameters described herein can be used as input parameters to a treatment response prediction algorithm to generate a prediction of the pattern, timing, and / or evolution of a future treatment response interval 140 of a patient's retinal fluid volume trajectory. Any suitable combination of retinal fluid parameters described herein can be used as input parameters to a treatment recommendation algorithm to generate a treatment recommendation for a patient (e.g., a recommended date for a therapeutic injection, a recommended therapeutic compound for a therapeutic injection, and / or a recommended amount of a therapeutic compound for a therapeutic injection). Any suitable combination of retinal fluid parameters described herein can be used as input parameters to a feature ranking algorithm to generate a ranking of the relevance of the retinal fluid parameters to any of the predictions and / or recommendations described herein. Any suitable combination of retinal fluid parameters described herein can be used as input parameters into a retinal disease classification algorithm to generate a classification of a patient's retinal disease. The classification of a patient's retinal disease can be indicative of the severity of the patient's retinal disease and / or the predicted effectiveness of a treatment for the patient's retinal disease.

[0088]

[0138] Any suitable technique can be used to formulate the algorithms described herein. For example, machine learning (either supervised or unsupervised) can be used to formulate the algorithms described herein. Machine learning can be used to formulate a reactivation prediction algorithm and / or a treatment response prediction algorithm and define a training data set using an appropriate number of retinal fluid volume trajectories (and possibly previous treatment data). Machine learning can also be used to formulate a treatment recommendation algorithm and define a training data set using an appropriate number of retinal fluid volume trajectories together with related medically appropriate treatment application data, such that the treatment recommendation algorithm is configured to automatically generate medically appropriate future treatment recommendations based on the patient's retinal fluid volume trajectory and, if available, any previous treatment application to the patient.

[0089]

[0139] 18A-24E show exemplary retinal fluid volume trajectory plots and associated fluid volume / distribution maps generated using the systems and methods described herein. FIG. 18A shows an exemplary segmented retinal fluid volume trajectory plot including a first response interval and a second response interval. FIG. 18B shows a fluid volume map of an OCT retinal image obtained during a first response interval of the retinal fluid volume trajectory of FIG. 18A. FIG. 18C shows a fluid volume map of an OCT retinal image obtained during a second response interval of the retinal fluid volume trajectory of FIG. 18A. FIG. 19A shows an exemplary retinal fluid volume trajectory plot of a patient's retinal portion including a first activation interval, a first response interval, a second activation interval, and a second response interval. FIG. 19B shows a fluid volume map of an OCT retinal image obtained during a first activation interval of FIG. 19A. FIG. 19C shows a fluid volume map of an OCT retinal image obtained during a second activation interval of FIG. 19A. FIG. 20A shows an exemplary retinal fluid volume trajectory plot of a retinal portion of another patient. FIG. 20B shows a fluid volume map of an OCT retinal image obtained during a first activation interval of FIG. 20A. FIG. 20C shows a fluid volume map of an OCT retinal image obtained during a first response interval of FIG. 20A. FIG. 20D shows a fluid volume map of an OCT retinal image obtained during a second activation interval of FIG. 20A. FIG. 20E shows a fluid volume map of an OCT retinal image obtained during a second response interval of FIG. 20A. FIG. 20F shows a treatment response fluid reduction percentage map for the first response interval of FIG. 20A. FIG. 20G shows a treatment response fluid reduction percentage map for the second response interval of FIG. 20A. FIG. 20H shows a first half-life fluid volume map of the retinal fluid volume trajectory of FIG. 20A. FIG. 20I shows a second half-life fluid volume map of the retinal fluid volume trajectory of FIG. 20A. FIG. 21A shows an exemplary retinal fluid volume trajectory plot of a retinal portion of another patient. FIG. 21B shows a fluid volume map of an OCT retinal image obtained during a first activation interval of FIG. 21A. FIG. 21C shows a fluid volume map of an OCT retinal image obtained during a first response interval of FIG. 21A. FIG. 21D shows a fluid volume map of an OCT retinal image obtained during a second activation interval of FIG. 21A. FIG. 21E shows a fluid volume map of an OCT retinal image obtained during a second response interval of FIG. 21A. FIG. 21F shows a treatment response fluid reduction percentage map for the first response interval of FIG. 21A. FIG. 21G shows a treatment response fluid reduction percentage map for the second response interval of FIG. 21A.FIG. 21H shows a first half-life fluid volume map of the retinal fluid volume trajectory of FIG. 21A. FIG. 21I shows a second half-life fluid volume map of the retinal fluid volume trajectory of FIG. 21A. FIG. 22A shows an exemplary retinal fluid volume trajectory plot of a retinal portion of another patient. FIG. 22B shows a final fluid volume map of an OCT retinal image obtained during the first response interval of FIG. 22A. FIG. 22C shows a treatment response fluid reduction percentage map for the first response interval of FIG. 22A. FIG. 22D shows a final fluid volume map of an OCT retinal image obtained during the second response interval of FIG. 22A. FIG. 22E shows a treatment response fluid reduction percentage map for the second response interval of FIG. 22A. FIG. 22F shows a final fluid volume map of an OCT retinal image obtained during the third response interval of FIG. 22A. FIG. 22G shows a treatment response fluid reduction percentage map for the third response interval of FIG. 22A. Figures 23A, 23B, and 23C show fluid volume maps of three activation intervals for a patient, while Figures 24A, 24B, 24C, 24D, and 24E show activation at different locations within the patient's retina.

[0090]

[0140] FIG. 25 illustrates an exemplary graphical user interface (GUI) 200 of the retinal disease management system 48 (shown in FIG. 2). The GUI 200 is configured for use by a treating physician to manage the treatment of patients with retinal disease. The GUI 200 includes a patient search and display portion 202, which includes patient search input fields 204, 206, a patient group pull-down menu 208, and a patient list 210. A treating physician can enter patient search criteria in one of the patient search input fields 204, 206 and / or can select a patient group using the patient group pull-down menu 208. The patient list 210 displays a list of patients that correspond to the patient search criteria or the selected patient group. In the illustrated embodiment, the patient list 210 displays, for each patient included in the patient list 210, a patient identification (PID), a patient clinic identification (clinic ID), a patient name, a patient date of birth, and a patient age.

[0091]

[0141] To access care management information for a particular patient, the treating physician may select one of the patients in the patient list 210 using any suitable technique (e.g., touching a touch-sensitive screen to select the patient of interest, moving a cursor to select the patient of interest, entering a patient identification (PID), etc.). In the illustrated embodiment, the selected patient in the patient list 210 is indicated via a selection box 212 in which the selected patient is displayed. The GUI 200 includes a selected patient section 214 that displays the patient's name, patient identification (PID), patient's age, patient's date of birth, and the patient's clinic identification (Clinic ID).

[0092]

[0142] For a selected patient, the GUI 200 may display any suitable number and / or combination of retinal fluid maps 216, 218, such as any of those described herein. The displayed retinal fluid maps 216, 218 may be for any selected OCT imaging sessions of the selected patient's retina to assist the treating physician in assessing the current state and / or progression of the selected patient's retinal disease between two or more selected OCT imaging sessions. The displayed retinal fluid maps 216, 218 may have any suitable configuration, such as any configuration described herein. In the illustrated embodiment, each of the displayed retinal fluid maps 216, 218 includes an origin marker, a center of mass marker, and a maximum fluid concentration marker. The origin marker is used to display the location of the origin of the initial retinal fluid in the measured sequence of retinal fluid conditions. The center of mass marker is used to mark the location of the center of mass of the retinal fluid. The maximum fluid concentration is used to mark the location of the maximum retinal fluid concentration. In the illustrated configuration, the GUI 200 displays a fluid volume summary 220, including total central retinal fluid volume 222 and total peripheral retinal fluid volume 224 for the selected patient.

[0093]

[0143] For the selected patient, the GUI 200 may display an activation data section 226, which displays retinal fluid activation data for the selected patient. The displayed retinal fluid activation data may include any suitable combination of retinal fluid activation data, such as any of the retinal fluid activation parameters described herein. For example, in the illustrated example, the activation data section 226 displays an activation frequency, a retinal fluid expansion rate, an activation location, a current total retinal fluid value, and an overall (e.g., composite) activation parameter. The magnification may indicate a current magnification of the retinal fluid value for the selected patient (based on the change in the appropriate retinal fluid extent value between recent retinal OCT images), providing information to the treating physician as to how fast the retinal fluid for the selected patient is increasing. The overall activation parameter may be a calculated parameter based on any suitable weighted combination of the parameters in the activation data section 226.

[0094]

[0144] For a selected patient, the GUI 200 can display a response data section 228, which displays treatment response data for the selected patient. For example, in the displayed example, the response data section 228 includes a previous response duration, a contraction rate during the previous response, a minimum local fluid volume achieved during the previous response, a minimum total fluid volume achieved during the previous response, and an overall (e.g., composite) response parameter for the selected patient. The contraction rate can indicate a current contraction rate of the retinal fluid value of the selected patient (based on the change in retinal fluid value between recent retinal OCT images) to inform the treating physician as to how quickly the retinal fluid status of the selected patient improved during the response period. The overall response parameter can be a calculated parameter based on any suitable weighted combination of the parameters in the response data section 228.

[0095]

[0145] For a selected patient, the GUI 200 can display a recommended treatment 230 for treating the selected patient's retinal disease. For example, in the displayed example, the recommended treatment 230 displays the component therapeutic compounds of the therapeutic compound for injection into the selected patient's eye, and the corresponding concentrations of the component therapeutic compounds. The recommended treatment 230 includes a recommended amount of the therapeutic compound. The GUI 200 also displays a next activation time prediction for when the next activation of the fluid collection will occur. The recommended treatment 230 and / or the next activation time prediction can be determined using any suitable approach, such as, for example, based on accumulated retinal imaging data and associated treatment data for a suitable population of similarly situated patients.

[0096]

[0146] The specification and drawings are appropriately to be regarded in an illustrative rather than a restrictive sense. However, it will be apparent that various modifications and changes can be made therein without departing from the broader spirit and scope of the disclosure as set forth in the appended claims.

[0097]

[0147] Other variations are within the spirit of the disclosure. Thus, while the disclosed technology is susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It is to be understood, however, that there is no intention to limit the disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions and equivalents falling within the spirit and scope of the disclosure, as defined by the appended claims.

[0098]

[0148] Use of the terms "a" and "an" and "the" and similar referents in the context of describing the disclosed embodiments (particularly in the context of the claims below) should be construed to encompass both the singular and the plural unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" should be construed as open-ended terms (i.e., meaning "including, but not limited to") unless otherwise indicated. The term "connected" should be construed as partially or wholly contained in, attached to, or joined together, even if there are intervening elements. Recitation of ranges of values ​​herein is merely intended to serve as a shorthand method of individually referring to each separate value contained within the range, unless otherwise indicated herein, and each separate value is incorporated herein as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples or exemplary language (e.g., "etc.") provided herein is intended merely to better illuminate embodiments of the disclosure and does not limit the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

[0099]

[0149] Disjunctive language, such as the phrase "at least one of X, Y, or Z," is intended to be understood within the context of common usage to indicate that an item, term, etc. can be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z), unless otherwise indicated. Thus, such disjunctive language generally does not and should not imply that a particular embodiment requires the presence of each of at least one of X, at least one of Y, or at least one of Z.

[0100]

[0150] Preferred embodiments of the present disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Variations of these preferred embodiments will become apparent to those skilled in the art upon reading the foregoing description. The inventors expect those skilled in the art to use such variations as appropriate, and the inventors intend for the present disclosure to be carried out in ways other than as specifically described herein. Accordingly, the present disclosure includes all modifications and equivalents of the subject matter recited in the appended claims as permitted by applicable law. Moreover, the present disclosure includes any combination of the above-described elements in all possible variations thereof unless otherwise indicated herein or clearly contradicted by context.

[0101]

[0151] All references cited in this specification, including publications, patent applications, and patents, are herein incorporated by reference to the same extent as if each reference was individually and specifically indicated to be incorporated by reference and was set forth in its entirety herein.

Claims

1. 1. A system for managing treatment of a retinal disease in an eye of a subject, the system comprising: a communication unit configured to receive optical coherence tomography (OCT) image data of a subject's retina for each of a series of OCT imaging sessions of the retina; At least one processor; a tangible storage device storing non-transitory instructions executable by the at least one processor, the non-transitory instructions including instructions to the at least one processor to: processing the OCT image data of the retina to determine a series of measured retinal fluid level values; generating a treatment recommendation for the subject via execution of a treatment algorithm using input based on the series of measured retinal fluid level values; Tangible storage devices A system comprising:

2. The system of claim 1 , wherein the treatment algorithm is a machine learning algorithm trained using training data comprising a plurality of series of retinal fluid degree values.

3. The system of claim 2 , wherein the training data includes treatment application data associated with the plurality of series of retinal fluid level values.

4. The system of claim 1 , wherein the treatment recommendation includes a recommended date for a therapeutic injection into the subject's eye.

5. The system of claim 4 , wherein the treatment recommendation comprises a recommended compound for the therapeutic injection.

6. The system of claim 5 , wherein the treatment recommendation comprises a recommended amount of the recommended compound for the therapeutic injection.

7. 1. A system for predicting progression of a retinal disease in an eye of a subject, the system comprising: a communication unit configured to receive optical coherence tomography (OCT) image data of a subject's retina for each of a series of OCT imaging sessions of the retina; At least one processor; a tangible storage device storing non-transitory instructions executable by the at least one processor, the non-transitory instructions including instructions to the at least one processor to: processing the OCT image data of the retina to determine a series of measured retinal fluid level values; generating a prediction of progression of the retinal disease via execution of a predictive algorithm using input based on the series of measured retinal fluid level values. Tangible storage devices A system comprising:

8. The system of claim 7 , wherein the predictive algorithm is a machine learning algorithm trained using training data comprising a plurality of series of retinal fluid degree values.

9. The system of claim 8 , wherein the training data includes treatment application data associated with the plurality of series of retinal fluid level values.

10. The system of claim 9 , wherein the prediction of the progression of the retinal disease comprises a predicted therapeutic response of the subject to a treatment for the retinal disease.

11. The system of claim 10 , wherein the treatment of the retinal disease comprises an injection of a therapeutic compound into the eye of the subject.

12. The system of claim 7 , wherein the prediction of the progression of the retinal disease comprises a predicted progression of an increase in a series of retinal fluid level values.

13. 1. A system for classifying retinal diseases in an eye of a subject, the system comprising: a communication unit configured to receive optical coherence tomography (OCT) image data of a subject's retina for each of a series of OCT imaging sessions of the retina; At least one processor; a tangible storage device storing non-transitory instructions executable by the at least one processor, the non-transitory instructions including instructions to the at least one processor to: processing the OCT image data of the retina to determine a series of measured retinal fluid level values; determining a classification of the retinal disease of the subject's eye via execution of a classification algorithm using input based on the series of measured retinal fluid level values, the classification being selected from a plurality of predetermined classifications. Tangible storage devices A system comprising:

14. 14. The system of claim 13, wherein the classification algorithm is a machine learning algorithm trained using training data comprising a plurality of series of retinal fluid degree values.

15. The system of claim 14 , wherein the training data includes treatment application data associated with the plurality of series of retinal fluid level values.

16. The system of claim 13 , wherein the classification of the retinal disease in the subject's eye indicates a severity of the retinal disease in the subject's eye.

17. The system of claim 13 , wherein the classification of the retinal disease of the subject's eye indicates the effectiveness of a treatment for the retinal disease of the subject's eye.

Images