A computer program for determining the effectiveness of a fluid in treating an eye

Abstract

According to the present invention there is provided a computer program, for determining the effectiveness of a fluid in treating an eye (15) which has an adverse health condition, the computer program being stored on a portable electronic device (4), the portable electronic device (4) having a processor (6) and a camera (5a,5b); and wherein the portable electronic device (4) can connect with an eye drop sprayer device (2) which has a spray nozzle (3) to form an eye drop assembly (1); and wherein the eye drop sprayer device (2) holds a fluid which can be administered to an eye (15); wherein the computer program which when executed by a processor (6) will cause the processor (6) to, receive a first image (40a,50a) captured by the camera (5a,5b), wherein said first image (40a,50a) depicts an eye (15) before fluid has been administered to the eye (15); receive a second image (40b, 50b) captured by the camera (5a,5b), wherein said second image (40b,50b) depicts the eye (15) after fluid has been administered to the eye (15); initiate processing of the first image (40a,50a) and / or second image (40b,50b) to determine if the fluid was effective in treating the eye. There is further provided a portable electronic device (4) having a memory (6) which stores the computer program; and a corresponding system (30) which comprises one or more such portable electronic devices (4) which can selectively communicate with a remote server (31) or external platform (31) having stored therein a machine learning algorithm (32).

Description

A computer program for determining the effectiveness of a fluid in treating an eyeField of the invention

[0001] The present invention concerns a computer program for determining the effectiveness of a fluid in treating an eye which has an adverse health condition. The computer program is stored on a portable electronic device which has a processor and a camera; wherein the portable electronic device selectively connects with an eye drop sprayer device which has fluid to be administered to the eye, to form an eye drop assembly. A first image captured by the camera before the fluid has been administered to the eye, and a second image captured by the camera after the fluid has been administered to the eye, are used to determine the effectiveness of a fluid in treating the eye. There is further provided a portable electronic device having a memory which stores the computer program; and a corresponding system which comprises one or more such portable electronic devices which can selectively communicate with a remote server or external platform having stored therein a machine learning algorithm or rule-based algorithm.Background of the invention

[0002] Self-administration of fluids (such as eye drops or other ophthalmic medications) to an eye poses many difficulties: Typically, the fluids come in a plastic bottle having a dispensing nozzle; the user is required tilt their head backwards, align the dispensing nozzle of the bottle with their eye, and then, while maintaining this position, deliver a precise volume of fluid into their eye. The Physician will normally indicate in a medical prescription the precise volume of fluid which is to be administered to the eye. It can be difficult for a user to deliver such a precise volume of fluid, especially if the user lacks dexterity or fine motor skills.

[0003] Importantly, control of the effectiveness of the fluid in treating the eye is only done on the user's next visit to the Physician. If a fluid has been ineffective in treating the eye, then the user may have already gone days or weeks without have received effective treatment; during this period of ineffective treatment the health condition of the eye may have deteriorated further. It is desirable to have a means to control the effectiveness of the fluid in treating the eye without the user having to visit their Physician. It is even more desirable to have a means to conveniently and accurately, regularly control the effectiveness of the fluid in treating the eye.

[0004] Under current practices a Physician will prescribe a fixed treatment plan and the user will follow that fixed treatment plan until their next visit to the Physician. The fixed treatment plan will typically specify, a specific fluid medication to be administered, a specific volume to be administered, a frequency of administration (e.g. three times a day, morning, afternoon and evening), and a duration (e.g. for 7 days). However, as the user follows the fixed treatment plan, the health condition of the eye may improve or deteriorate over time; for optimum treatment the treatment plan should change with changes in the health condition of the eye. For example, if the health condition of the eye deteriorates then the user may be required to administer a higher volume of the fluid to the eye, and / or the user may be required to administer fluid more frequently to the eye, and / or a different type of fluid should be administered.Conversely, if the health condition of the eye improves then the user may be required to administer a lesser volume of the fluid to the eye, and / or the user may be required to administer fluid less frequently to the eye, and / or a different type of fluid should be administered, and / or it may be advisable for the user to stop administering any fluid to the eye. In other words, the optimum treatment of the eye changes over time based on how the health condition of the eye progresses over time; accordingly, by following a fixed treatment plan which does not take account of changes in the health condition of the eye over time, the user does not receive optimum treatment. It would be desirable to be able to accurately assess changes inthe health of the eye and to adjust the treatment plan based on any changes, so that the user is receiving optimum treatment.Summary of the invention

[0005] According to the present invention there is provided a computer program having the features recited in independent claim 1.

[0006] In the present invention the computer program is provided on a portable electronic device (preferably a smartphone) which connects with the eye drop sprayer device to form an eye drop assembly; the computer program when executed by the processor causes the processor of the portable electronic device to, receive a first image captured by the camera, wherein said first image depicts an eye before fluid has been administered to the eye; receive a second image captured by the camera, wherein said second image depicts the eye after fluid has been administered to the eye; initiate processing of the first image and / or second image to determine if the fluid was effective in treating the eye.

[0007] The processing of the first image and / or second image to determine if the fluid was effective in treating the eye, may comprise processing of the first image and / or second image, to determine if the fluid was effective in treating a predefined health condition which the eye is suffering from, and / or to determine if the fluid was effective in causing one or more predefined changes to the eye (e.g. dilation of the pupil of the eye). In other words, treating the eye may include, either, treating the eye to improve a predefined health condition which the eye is suffering from, and / or treating the eye to cause one or more predefined change in the state of the eye.

[0008] In an embodiment the computer program when executed by a processor will cause the processor to initiate processing of the first image and second image to determine if one or more predefined changes in theeye has occurred as a result of the fluid having been administered to the eye. In an embodiment it is determined that the eye has a predefined defect, or predefined health condition, if the one more predefined changes in the eye did not occur after the fluid was administered to the eye. Or, it may be determined that the eye has a predefined defect, or predefined health condition, if the one more predefined changes in the eye did occur after the fluid was administered to the eye.

[0009] In a preferred embodiment the computer program when executed by the processor causes the processor initiate the processing of the first image and second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye, by sending the first image and second image to a remote server or external platform, wherein the remote server or external platform has stored thereon a machine learning algorithm which has been trained using images of eyes in which the one or more predefined changes have occurred and images of eyes in which the one or more predefined changes have not occurred. The machine learning algorithm is configured to determine if the predefined changes in the eye have occurred by comparing the second image to the first image, and / or by comparing each of the first image and second image to one or more references images (preferably the one or more reference images comprise images of eyes in which the one or more predefined changes have occurred and images of eyes in which the one or more predefined changes have not occurred).

[0010] In an embodiment the computer program which when executed by a processor will cause the processor to initiate processing of the first image and / or second image to determine if the eye depicted in the second image is healthier than the eye depicted in the first image; initiate determining an effectiveness of the fluid in treating the eye, based on if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0011] In a preferred embodiment the computer program when executed by the processor causes the processor initiate the processing of the first image and / or second image and initiate determining an effectiveness of the fluid in treating the eye, by sending the first image and / or second image to a remote server or external platform, wherein the remote server or external platform has stored thereon a machine learning algorithm which has been trained using images of healthy eyes and images of eyes having predefined health conditions. The machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image by comparing the second image to the first image, and / or by comparing each of the first image and second image to one or more references images (preferably the one or more reference images comprise images of healthy eyes and images of eyes having predefined health conditions). The machine learning algorithm may be configured to determine the effectiveness of the fluid in treating the eye based on said comparison. The machine learning algorithm then sends an output to the processor, wherein said output comprises an indication of whether the eye depicted in the second image is healthier than the eye depicted in the first image, and / or comprises an indication of the effectiveness of the fluid in treating the eye. The processor will preferably display said received output on a display screen of the portable electronic device. The processor may also forward the received output to another destination; for example, the processor may forward the received output to the user's Physician so that the Physician can review the contents of the output.

[0012] It should be understood in the present disclosure a rule-based algorithm can be used to instead of a machine learning algorithm. It should be understood that the rule-based algorithm may be configured to perform any step which is described in the present disclosure as being performed by the machine learning algorithm.

[0013] In another embodiment the processing of the first image and / or second image to determine if the fluid was effective in treating the eye is done locally by the processor on the portable electronic device.

[0014] In an embodiment the processing of the first image and second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye is done locally by the processor on the portable electronic device. In other words, the computer program when executed by the processor causes the processor to process the first image and / or second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye.

[0015] In an embodiment the processing of the first image and / or second image to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, and determining an effectiveness of the fluid in treating the eye, is done locally by the processor on the portable electronic device. In other words, the computer program when executed by the processor causes the processor to process the first image and / or second image to determine if the eye depicted in the second image is healthier than the eye depicted in the first image; and determine the effectiveness of the fluid in treating the eye, based on if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0016] The dependent claims recite optional features of various embodiments of the invention. It should be understood that any of the subsequently described features may be optional features of any of embodiments described in the present disclosure. Even if a feature is described in the present disclosure as being a feature of an embodiment, it should be understood that that feature could be an optional feature of any of the other embodiments of the present disclosure. Any embodiment disclosed in the present disclosure may have any one or more of the features of any of the other embodiments disclosed in the present disclosure.

[0017] In an embodiment the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than theeye depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in the second image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image; determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0018] In an embodiment the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes, and determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0019] In an embodiment the computer program when executed by a processor will cause the processor to initiate processing of the first image and / or second image to determine if one or more predefined changes in the eye has occurred as a result of the fluid having been administered to the eye.

[0020] In an embodiment the computer program when executed by a processor will cause the processor to, send the first image and second image to a remote server or external platform, wherein the remote server or external platform has stored therein a machine learning algorithm which has been trained using images of eyes in which one or more predefined changes has occurred and images of eyes in which said one or more predefined changes has not occurred, wherein the machine learning algorithm is configured to, determine using the first and second image, if said one or more predefined changes has occurred in the eye depicted in the second image, and, determine that the eye has a predefined defect, or predefined health condition, depending on if said one or more predefined changes in the eye has occurred in the eye depicted in the second image.

[0021] In an embodiment the machine learning algorithm is further configured to determine from the second image, if the fluid which has been used to treat the eye, has caused any side effects.

[0022] In an embodiment the computer program when executed by a processor will cause the processor to, receive the output from the remote server or external platform; and display on a display screen of the portable electronic device said indication of the received output and / or an indication of the effectiveness of the fluid in treating the eye.

[0023] In an embodiment the computer program when executed by a processor will cause the processor to, processes the first image and second image to determine if the eye depicted in the second image is healthier than the eye depicted in the first image; and determine the effectiveness of the fluid in treating the eye, based on if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0024] In an embodiment the computer program when executed by a processor will cause the processor to, determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.

[0025] In an embodiment the computer program is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in the second image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image; determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0026] In an embodiment the computer program is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes, and determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0027] In an embodiment the computer program which when executed by a processor will cause the processor to process the first image and / or second image to determine if one or more predefined changes in the eye has occurred as a result of the fluid having been administered to the eye.

[0028] In an embodiment the computer program which when executed by a processor will cause the processor to determine that the eye has a predefined defect, or predefined health condition, depending on if said one or more predefined changes in the eye has occurred in the eye depicted in the second image.

[0029] In an embodiment the computer program when executed by a processor will cause the processor to determine from the second image, if the fluid which has been used to treat the eye, has caused any side effects.

[0030] In an embodiment the computer program when executed by a processor will cause the processor to receive as an input a treatment plan which has been input by the user.

[0031] In an embodiment the computer program when executed by a processor will cause the processor prompt the user to input the treatment plan into the portable electronic device.

[0032] In an embodiment the computer program when executed by a processor will cause the processor to display on a display screen of the mobile device the treatment plan.

[0033] In an embodiment the treatment plan comprises at least one or more of: a duration for treatment; a frequency of treatment; a type of fluid to be administered; the volume of fluid to be administered; the part of the eye to which to administer the fluid.

[0034] In an embodiment the treatment plan comprises a user authentication associated with it, wherein said user authentication is defined by predefined characteristics of the eye of the user; and wherein the computer program when executed by a processor will cause the processor to authenticate the user by processing an image of the eye of the user captured by the camera of the portable electronic device, to determine if the eye depicted in the image comprises said predefined characteristics.

[0035] In an embodiment the computer program when executed by a processor will cause the processor to adjust the treatment plan based on the health of the eye depicted in the second image, to provide an adjusted treatment plan.

[0036] In an embodiment the adjusted treatment plan is formed by adjusting at least one of, a duration for treatment, a frequency of treatment, a type of fluid to be administered, a volume of fluid to be administered, the part of the eye to which to administer the fluid, that was specified in a treatment plan that the user input to the portable electronic device.

[0037] In an embodiment the computer program when executed by a processor will cause the processor to process the second image to determine the amount of predefined features depicted in the second image, and to determine the health of the eye depicted in the secondimage based the amount of predefined features depicted in the second image.

[0038] In an embodiment the computer program when executed by a processor will cause the processor to adjust the treatment plan based on the determined effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan.

[0039] In an embodiment the computer program when executed by a processor will cause the processor to send the second image to a machine learning algorithm and wherein the machine learning algorithm is configured to adjust the treatment plan based on the health of the eye depicted in the second image, to provide an adjusted treatment plan.

[0040] In an embodiment the machine learning algorithm is configured to process the second image to determine the health of the eye depicted in the second image; and send to the processor said adjusted treatment plan.

[0041] In an embodiment the machine learning algorithm is configured to process the second image to determine the amount of predefined features depicted in the second image, to determine the health of the eye depicted in the second image.

[0042] In an embodiment the machine learning algorithm is configured to adjust the treatment plan based on the determined effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan.

[0043] In an embodiment the computer program when executed by a processor will cause the processor to display on a display screen of the portable electronic device the adjusted treatment plan.

[0044] In an embodiment the computer program when executed by a processor will cause the processor to, detect using one or more imagescaptured by the camera of the portable electronic device, when the spray nozzle is aligned with the eye; and initiate the eye drop sprayer device to spray fluid from the spray nozzle into the eye when the spray nozzle is aligned with the eye.

[0045] In an embodiment the computer program when executed by a processor will cause the processor to, initiate the eye drop sprayer device to spray a predefined volume of fluid from the spray nozzle into the eye.

[0046] In an embodiment the predefined volume fluid sprayed corresponds to a volume of fluid specified in a treatment plan that the user input, or, wherein the predefined volume fluid sprayed corresponds to a volume of fluid specified in an adjusted treatment plan.

[0047] In an embodiment the computer program when executed by a processor will cause the processor to initiate the eye drop sprayer device to spray the predefined volume of fluid from the spray nozzle into the eye only if the user has been authenticated.

[0048] In an embodiment the computer program when executed by a processor will cause the processor to generate one or more alarms on a schedule corresponding to a schedule of a treatment plan that the user input or corresponding to a schedule of an adjusted treatment plan, wherein said alarms remind the user to administer the fluid to the eye at times corresponding to times specified in said treatment plan or adjusted treatment plan.

[0049] In an embodiment the computer program when executed by a processor will cause the processor to, detect which type of fluid is in the eye drop sprayer device; and prompt the user to change the fluid, if the fluid in the eye drop sprayer device does not correspond with a fluid type specified in a treatment plan that the user input, or, if the fluid in the eye drop sprayer device does not correspond with a fluid type specified in an adjusted treatment plan.

[0050] In an embodiment the computer program when executed by a processor will cause the processor to, detect if the fluid in the eye drop sprayer device is empty or below a predefined critical volume; and to prompt the user to change the fluid or refill the fluid, if the fluid in the eye drop sprayer is empty or below the predefined critical volume.

[0051] In an embodiment the computer program when executed by a processor will cause the processor to prompt the user to visit the physician if the eye depicted in the second image is less healthy than the eye depicted in the first image.

[0052] In an embodiment the computer program which when executed by a processor will cause the processor to further, receive as input one or more images depicting the eye, captured by the camera of the portable electronic device when the eyedrop assembly is in a position in which the spray nozzle is aligned with the eye; determine from the received one or more images when the eye is open; and initiate the eye drop sprayer device to spray fluid from the spray nozzle when the eye is open.

[0053] In an embodiment the computer program when executed by a processor will cause the processor to, receive as input a plurality of images captured by the camera of the portable electronic device; and determine a blinking frequency of the eye from said input; use the determined blinking frequency to determine a time instant when the eye is due to be open; and initiate the eye drop sprayer device to spray fluid at said determined time instant so that the fluid is sprayed from the spray nozzle when the eye is due to be open.

[0054] In an embodiment the computer program which when executed by a processor will cause the processor to further, receive as input at least a third image depicting the eye, captured by the camera at a time instant corresponding to when the eye drop sprayer device was initiated to spray fluid from the spray nozzle; determine from the at least third image if eye was closed when the fluid reached the eye.

[0055] In an embodiment the computer program is when executed by a processor will cause the processor to further, prompt the user to align the spray nozzle with the eye again if it is determined from the at least third image that the eye was closed when the fluid reached the eye.

[0056] In an embodiment the computer program which when executed by a processor will cause the processor to further, receive as input one or more images depicting the eye, captured by the camera of the portable electronic device, when the eyedrop assembly is in a position in which the spray nozzle is aligned with the eye; determine from the received one or more images when the eye is closed; and initiate the eye drop sprayer device to spray fluid from the spray nozzle when the eye is closed.

[0057] In an embodiment the computer program which when executed by a processor will cause the processor to further, determine, based on the type of fluid in the eye drop sprayer device if the fluid is to be administered to an open eye or to a closed eye; carry out any of the above-mentioned steps if it is determined that that the fluid is to be administered to an open eye; carry out any of the above-mentioned steps if it is determined that that the fluid is to be administered to a closed eye.

[0058] In an embodiment the mobile device has a light, and wherein the computer program which when executed by a processor will cause the processor to further, operate the light to shine light, having a predefined characteristic, into the eye; operate the camera to capture one or more images of the eye as the light shines into the eye; and process said captured one or more images to determine the reaction of the eye to the light having a predefined characteristic. In an embodiment the computer program which when executed by a processor will cause the processor to, carry out the afore-mentioned steps prior to administering fluid to the eye to determine a first reaction of the eye; and carry out the afore-mentioned steps after administering fluid to said eye to determine a second reaction of the eye.

[0059] In an embodiment the computer program which when executed by a processor will cause the processor to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye.

[0060] In an embodiment the computer program which when executed by a processor will cause the processor to further, operate the light to change the characteristics of the light shining into the eye; operate the camera to capture one or more images of the eye as the characteristics of the light are changed; and process said captured one or more images to determine the reaction of the eye to the changing light characteristics. In an embodiment the computer program which when executed by a processor will cause the processor to, carry out the afore-mentioned steps prior to administering fluid to the eye to determine a first reaction of the eye; and carry out the afore-mentioned steps after administering fluid to said eye to determine a second reaction of the eye.

[0061] In an embodiment the computer program which when executed by a processor will cause the processor to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye.

[0062] In an embodiment the computer program which when executed by a processor will cause the processor to, prompt a user to capture one or more images of the eye, over a period of time; and store the captured images to form a documentation of the status of the health of the eye over said period of time.

[0063] In an embodiment the computer program which when executed by a processor will cause the processor to process the first image and / or second image to determine if the eye has a predefined health condition or is healthy.

[0064] In an embodiment the computer program when executed by a processor will cause the processor to, send the first image and second image to an external platform, wherein said external platform can be accessed by a Physician to view said first and second image, and wherein the physician can provide an input via the platform; receive from the platform said input of the physician; and display on the display screen of the portable mobile device said received input.

[0065] In an embodiment the said input of the Physician comprises at least one of, comments, and / or an indication of the effectiveness of the fluid in treating the eye, and / or an adapted treatment plan.

[0066] In an embodiment the fluid comprises a medication.

[0067] In an embodiment the portable electronic device is a smartphone.

[0068] According to a further aspect of the present invention there is provided a portable electronic device having said computer program stored in a memory of the portable electronic device. According to a further aspect of the present invention there is provided a corresponding system which comprises one or more such portable electronic devices which can selectively communicate with a remote server or external platform having stored therein a machine learning algorithm or rule-based algorithm.

[0069] In an embodiment of the system the machine learning algorithm is configured to receive at least the first and second image from a portable electronic device, wherein each of the first and second images depict an eye; and determine if the eye depicted in the received second image is healthier than the eye depicted in the first image; and send an output to said portable electronic device which comprises an indication if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0070] In an embodiment of the system the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.Brief description of the drawings

[0071] Exemplary embodiments of the invention are disclosed in the present description, some with reference to the following drawings:Figure 1 illustrates the computer program according to an embodiment of the present invention, executed by a processor to cause the processor to prompt the user to input their personal data and treatment plan prescribed by a Physician;Figure 2 illustrates the computer program according to an embodiment of the present invention, executed by a processor to cause the processor to guide the user through preparation steps before the fluid can be administered to the eye; including assisting in alignment of the spray nozzle with the eye, and displaying a prompt to remove glasses.Figure 3 illustrates an embodiment of the present invention; in particular Figure 3 illustrates a system according to an aspect of the present invention, which has a portable electronic device according to a further aspect of the present invention; wherein the portable electronic device has a memory which stores a computer program according to the an embodiment of the present invention; and wherein the portable electronic devices can selectively communicate with a remote server or external platform having stored thereon a machine learning algorithm;Figure 4a is an example of a first image captured by the camera, wherein said first image depicts an eye before fluid has been administered to the eye; Figure 4b is an example of a second image captured by the camera, wherein said second image depicts the eye after fluid has been administered to the eye;Figure 5a is an example of a first image captured by the camera, wherein said first image depicts an eye before fluid has been administered to the eye; Figure 5b is an example of a second image captured by the camera, wherein said second image depicts the eye after fluid has been administered to the eye;Figure 6 illustrates the computer program according to an embodiment of the present invention, executed by a processor to display the progress of the treatment, in particular illustrate the details of the treatment administered already and details of remaining treatments due;Figure 7 illustrates the display of prompts which assist the user in aligning the spray nozzle with the eye.Detailed description of exemplary embodiments of the present invention

[0072] The following terms or expressions as used herein should normally be interpreted as outlined in this section, unless defined otherwise by the description or unless the specific context indicates or requires otherwise:

[0073] The words 'comprise', 'comprises' and 'comprising' and similar expressions are to be construed in an open and inclusive sense, as 'including, but not limited to' in this description and in the claims. In contrast to this, the terms "consist of", "consists of" and "consisting of" asused herein are so-called closed language meaning that only the mentioned components are present.

[0074] The terms 'a' or 'an' do not exclude a plurality; i.e. the singular forms 'a', 'an' and 'the' should be understood as to include plural referents unless the context clearly indicates or requires otherwise. In other words, all references to singular characteristics or limitations of the present disclosure shall include the corresponding plural characteristic or limitation, and vice versa, unless explicitly specified otherwise or clearly implied to the contrary by the context in which the reference is made. The terms 'a', 'an' and 'the' hence have the same meaning as 'at least one' or as 'one or more' unless defined otherwise.

[0075] The expressions, 'one embodiment', 'an embodiment', 'a specific embodiment' and the like mean that a particular feature, property or characteristic, or a particular group or combination of features, properties or characteristics, as referred to in combination with the respective expression, is present in at least one of the embodiments of the invention. The occurrence of these expressions in various places throughout this description do not necessarily refer to the same embodiment. Moreover, the particular features, properties or characteristics may be combined in any suitable manner in one or more embodiments.

[0076] Referring to Figure 3, according to a first aspect of the present invention there is provided a computer program for determining the effectiveness of a fluid in treating an eye which has an adverse health condition, the computer program being stored on a portable electronic device 4, the portable electronic device having a processor 6 and a camera 5a, 5b; and wherein the portable electronic device 4 can selectively connect with an eye drop sprayer device 2 which has a spray nozzle 3 to form an eye drop assembly 1; and wherein the eye drop sprayer device 2 holds a fluid which can be administered to an eye 15 of a user 20; wherein the computer program which when executed by a processor 6 will cause the processor 6 to,receive a first innage captured by the camera 5a, 5b, wherein said first image depicts an eye 15 before fluid has been administered to the eye 15; receive a second image captured by the camera 5a, 5b, wherein said second image depicts the eye 15 after fluid has been administered to the eye 15; initiate processing of the first image and / or second image to determine if the fluid was effective in treating the eye.

[0077] The processing of the first image and / or second image to determine if the fluid was effective in treating the eye, may comprise processing of the first image and / or second image, to determine if the fluid was effective treating a predefined health condition which the eye is suffering from, and / or to determine if the fluid was effective the causing a one or more predefined changes to the eye (e.g. dilation of the pupil of the eye).

[0078] In the present embodiment the computer program which when executed by a processor will cause the processor to initiate processing of the first image and / or second image to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; and initiate determining an effectiveness of the fluid in treating the eye 15.

[0079] Preferably, determining the effectiveness of the fluid in treating the eye 15 is based on if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image.

[0080] It should be understood that any of the subsequently described features may be optional features. Even if a feature is described in the present disclosure as being a feature of an embodiment, it should be understood that that feature could be an optional feature of any of the other embodiment of the present invention. Any embodiment disclosed in the present disclosure may have any one or more of the features of any of the other embodiments disclosed in the present disclosure.

[0081] It should be understood that the portable electronic device 4 may connect with the eye drop sprayer device 2 in any suitable way. For example, the portable electronic device 4 may wirelessly connect with the eye drop sprayer device 2 to form an eye drop assembly 1; or, the portable electronic device 4 may physically connect with the eye drop sprayer device 2, to form an eye drop assembly (for example, the portable electronic device 4 may connect, via a physical wire connection, with the eye drop sprayer device 2, to form an eye drop assembly 1). In the preferred embodiment the eye drop sprayer device 2 comprises a plug member which can be inserted into a socket of the portable electronic device 4, so that the portable electronic device 4 and eye drop sprayer device 2 are connected to form an eye drop assembly 1. Preferably, the socket of the portable electronic device 4 to which the plug of the eye drop sprayer device 2 connects, is the same socket which is used to receive the plug of a charging cable which is used to charge the portable electronic device 4.

[0082] The fluid preferably comprises a liquid and / or a semi-solid formulation and / or a semi-solid mixture.

[0083] The term "fluid" as used herein is to be understood in a broad sense and means, unless otherwise stated, a fluid may be a liquid or liquid composition comprising a liquid vehicle or solvent and at least one further ingredient dissolved or dispersed (for example in the form of a suspension or colloidal dispersion). In some embodiments, the fluid may be a physiologically acceptable liquid, for example a cosmetically or pharmaceutically acceptable liquid, preferably a pharmaceutically acceptable liquid. In some embodiments, the fluid may be an aqueous liquid or liquid composition comprising at least one of an excipient and an active pharmaceutical ingredient, for example an ophthalmic active pharmaceutical ingredient. The term "excipient" as used herein means, unless otherwise stated, means a substance that is formulated alongside with an active pharmaceutical ingredient in a pharmaceutical composition and includes, for example, salts, preservatives, antioxidants, buffers or pH- modifiers, viscosity enhancers, tonicity modifiers, surfactants, adjuvants, colorants and others.

[0084] The term "active pharmaceutical ingredient" (API) as used herein means, unless otherwise stated, means a compound or combination of compounds which are pharmaceutically active against an undesired condition (of a subject). In particular embodiments, the fluid may be an ophthalmologically acceptable liquid for the (ophthalmic) application of the liquid to an ocular surface as discussed above. In particular embodiments, the fluid may be liquid to be applied to a body surface, specifically to an ocular surface of a subject is provided in sterilized form.

[0085] In some embodiments, the fluid or the active pharmaceutically ingredients optionally comprised by the fluid to be dispensed by the device according to the present invention are suitable for the prevention, delay or treatment of an ophthalmic disease or disorder and for the topical administration to the eye of a subject in need thereof.

[0086] In some embodiments, the fluid may comprise a physiologically or pharmaceutically acceptable solvent for topical application, for example water, alcohols, for example ethanol, 2-propanol (isopropanol), glycerol or benzyl alcohol, glycols, especially 1,2-propanediol (propylene glycol), polyethylene glycols, mineral oils, paraffins, isopropyl myristate, oleic acid and others. In further embodiments, the fluid may comprise an ophthalmologically acceptable solvent, for example water, ethanol, glycerol, 1,2-propanediol, propylene glycol, polyethylene glycol, mineral oils, flaxseed oil, castor oil or mixtures thereof (for example compositions sometimes referred to as "artificial tears" which may comprise a demulcent such as carboxymethylcellulose (CMC), hydroxypropyl methylcellulose (hypromellose), hydroxyethylcellulose, methylcellulose, dextran 70, gelatin, glycerol, polyethylene glycol (300, 400), polysorbate 80, polyvinyl alcohol and / or polyvinyl pyrrolidone (povidone)), either in the form of an aqueous mixture or solution or in non-aqueous form.

[0087] The eye as used herein preferably refers to an eye of human or animal, preferably a warm-blooded animal, especially to an eye of a human. The eye could be an eye of a user; in this case the user is self-administrating the fluid to their own eye. In another embodiment the eye could be an eye of a third party (such as the eye of a patient or the eye of an animal); in this case the user is administrating the fluid to the eye of the third party; in other words, the user is using the eye drop assembly 1 to administer the fluid to the eye of a third party.

[0088] The term "portable electronic device" 4 as used herein may also be understood in a broad sense and may mean, unless otherwise stated, any one of: a smartphone, or a tablet computer, or a (handheld) gaming console, or a basic phone, or a flip phone, or a slider phone, or a QWERTY phone, or a rugged phone, or a phablet, or a folding phone, or a netbook, or a laptop computer, or a digital media player, or a handheld personal computer, or a wearable computer, or a smartwatch and others, preferably a smartphone or a tablet computer, especially a smartphone.

[0089] The eye drop sprayer device 2 may take any suitable form. The eye drop sprayer device 2 may be any device which can dispense a fluid; the fluid may be a liquid or a semi-solid formulation / mixture. The eye drop sprayer device 2 may be configured to dispense the fluid in the form of coarse-droplets, and / or may be configured to dispense a fluid in the form of fine droplets (in the form of a mist for example) such as, for example, a fine droplet micro-mist sprayer. The eye drop sprayer device 2 may be configured to dispense fluid by any suitable means; for example, the eye drop sprayer may be configured to dispense fluid by mechanical means and / or electro-mechanical means.

[0090] Figure 1, illustrates the computer program according to an embodiment of the present invention, executed by the processor 6 of the portable mobile device 4, to cause the processor 6 to generate a prompt for the user 20, which is displayed on a display screen 8, to input their personal data. The display screen 8 is configured to be also a touchscreen 8 (i.e. the display screen 8 is configured to have touchscreen functionality). In the present disclosure the term "display screen 8" and "touchscreen 8" are used interchangeably since they refer to the same part of the portableelectronic device 4. The user 20 can enter their personal data via the touchscreen 8 of the portable mobile device 4. The personal data can include any suitable information; in this example the personal data includes the name, address, email and contact phone number of the user 20, as well as the age and the eye condition(s) that the user 20 is suffering from. In the example shown in Figure 1 the user 20 has selected that they are suffering from eye conditions "Dry eye", "Myopia" and "Cataracts".

[0091] The processor 6 then generates a prompt, which is displayed on the display screen 8 of the portable mobile device 4, for the user 20 to input a treatment plan. The user can input the details of the treatment plan via the touchscreen 8. Most preferably the treatment plan that the user 20 inputs will be a treatment plan that the Physician has prescribed to the user 20.

[0092] The treatment plan may include any suitable aspects. For example, the treatment plan may comprise at least one or more of: a duration for treatment; a frequency of treatment; a type of fluid to be administered (e.g. the name of the fluid medication); the volume of fluid to be administered; the part of the eye to which to administer the fluid (e.g. if the fluid should be administered to the sclera of the eye, or to the pupil, or to the edge of the eye lid etc.). In this example the fluid comprises a fluid medication suitable for treating one or more predefined ophthalmic health conditions. In the example shown in Figure 1 the treatment plan comprises, a type of fluid to be administered (e.g. the name of the fluid medication), the days of the week the fluid is to be administered to the eye, the number of administrations per day (i.e. the number of intakes per days), and the time of the day for each administration (i.e. the time of the day for each intake).

[0093] As mentioned, in this example shown in Figure 1 the fluid present in the eye drop sprayer device 2 of the eye drop assembly 1 is a fluid medication suitable for treating one or more predefined ophthalmic health conditions. The processor 6 generates a prompt, which is displayedon the display screen 8 of the portable mobile device 4, for the user 20 to select the name of the fluid is to be administered. In other words, the processor 6 generates a prompt for the user 20 to select which fluid medication has been prescribed by the Physician. The prompt provides the user 20 with pictures 13 of five different fluid medications to select from. The user selects the picture 13 corresponding to the fluid medication which has been prescribed by the Physician. The user selects the picture 13 via the touchscreen 8. If the prescribed fluid medication is not yet provided in the eye drop sprayer device 2 of the eye drop assembly 1 then the user 20 will need to provide the fluid medication in the eye drop sprayer device 2 of the eye drop assembly 1 before continuing. Preferably the prescribed fluid medication is already present in the eye drop sprayer device 2 of the eye drop assembly 1 when the user selects the picture 13.

[0094] In an embodiment computer program when executed by a processor 6 will cause the processor 6 to detect which type of fluid is in eye drop sprayer device 2; and prompt the user to change the fluid, if the fluid in eye drop sprayer device 2 does not correspond with the fluid medication that the user selected. The processor 6 may, for example, detect which type of fluid is in eye drop sprayer device 2 by reading a bar code, or QR code, or some other identifier, which is on a cartridge in which the fluid is contained.

[0095] In an embodiment the computer program when executed by the processor 6 will cause the processor 6 to, detect if the fluid in eye drop sprayer device 2 is empty or below a predefined critical volume; and to prompt the user 20 to change the fluid or refill the fluid, if the fluid in the eye drop sprayer device 2 is empty or is below the predefined critical volume.

[0096] After the user 20 has selected the fluid medication, the user 20 will enter the remaining details of the treatment plan. Specifically, the processor 6 generates a prompt, which is displayed on the display screen 8 of the portable mobile device 4, for the user 20 to enter the days of theweek the fluid is to be administered to the eye, the number of administrations per day (i.e. the number of intakes per days), and the time of the day for each administration (i.e. the time of the day for each intake).

[0097] The computer program when executed by the processor 6 will cause the processor 6 to receive all of the personal data and treatment plan that was input by the user 20. In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to display the treatment plan (or a summary of the treatment plan) which the user 20 input, on a display screen 8 of the portable electronic device 4.

[0098] In an embodiment the computer program when executed by the processor 6 will cause the processor 6 to prompt the user 20 to capture an image of their eye 15 and to process the captured image to determine a user authentication. In an embodiment the user 20 will position the portable electronic device 4 in front of their face; and the processor 6 will initiate the camera 5a, 5b of the portable mobile device 4 to automatically capture an image of the user's eye once the user has moved the portable electronic device 4 to a predefined position relative to their eye 20. Preferably the computer program when executed by the processor 6 will cause the processor 6 to prompt the user 20 in which direction to move the portable electronic device to bring it to the predefined reference position relative to the eye 20.

[0099] Preferably, the user authentication is defined by characteristics of the eye depicted in the captured image. In an embodiment the computer program when executed by the processor 6 will cause the processor 6 to associate the user authentication with the treatment plan that the user 20 input. For example, before administering the fluid to the eye 15 according to the treatment plan, the computer program when executed by a processor 6 will cause the processor 6 to first authenticate the user 20; the processor 6 will prompt the user to capture an image of the eye 20 using the camera 5a, 5b of the portable mobile device 4; and the processor 6 will process the captured image of the eye 15 to determine if the eye 15 hasT1 said characteristics which define the user authentication. Only if the user 20 is successfully authenticated will the processor 6 then proceed to guide the user to set up / align the eye drop assembly 1 to administer the fluid to the eye 15.

[0100] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to generate alarms on a schedule corresponding to a schedule of the treatment plan which the user input, wherein said alarms remind the user to administer the fluid to the eye 15 at times corresponding to times specified in said treatment plan. For example, in the treatment plan shown in Figure 1 that the user 20 input, the fluid is to be administered twice a day (i.e. two intakes per day), and the first administration (i.e. first intake) should be at 10:00hrs and the second administration (i.e. second intake) should be at 18:00hrs; the processor 6 may generate a first alarm at 10:00hrs (or shortly before 10:00hrs) to remind the user to carry out the first administration (i.e. first intake), and may generate a second alarm at 18:00hrs (or shortly before 18:00hrs) to remind the user to carry out the second administration (i.e. second intake). The alarms generated by the processor 6 may take any suitable form.

[0101] After the processor 6 has received the personal data and treatment plan input by the user 20, and preferably after the user 20 has been successfully authenticated, the computer program when executed by a processor 6 will cause the processor 6 to guide the user 20 through preparation steps before the fluid can be administered to the eye 15.

[0102] Figure 2 illustrates the computer program according to an embodiment of the present invention, executed by a processor 6 to cause the processor 6 to guide the user through preparation steps before the fluid can be administered to the eye 15. These preparation steps, include, but are not limited it, assisting in alignment of the spray nozzle 3 with the eye 15, and displaying a prompt to remove glasses or any other obstacles that maybe covering the eye 15.

[0103] In a preferred embodiment the computer program when executed by a processor 6 will cause the processor 6 to receive images of the eye captured by the camera 5a, 5b; use said received images to determine the relative position of the spray nozzle 3 to the eye 15; and use the determined relative position of the spray nozzle 3 to the eye 15 to determine a direction to move the eye drop assembly 1 to bring the spray nozzle 3 into alignment with the eye 15. The processor 6 will display on the display screen 8 a prompt of the determined direction to move the eye drop assembly 1.

[0104] In the examples shown in Figure 2 the prompts are in the form of text displayed on the display screen 8 of the portable electronic device 4. However, it should be understood that the prompts are not limited to being in the form of text displayed on the display screen 8, rather in any of the embodiments of the present disclosure, the prompts may take any suitable form. For example, in an embodiment the prompt(s) may comprise a haptic prompt. The haptic prompt may be a vibration of the portable electronic device 4. In an embodiment the prompt(s) may comprise an audio prompt. The prompt(s) may comprise any combination of different types of prompts. For example, the prompt(s) may comprise a combination of text displayed on the display screen 8 plus an audio prompt (such as an audio of the text displayed on the display screen); or the prompt(s) may comprise a combination of text displayed on the display screen 8 plus a haptic prompt (such as vibration of the portable electronic device 4).

[0105] Figure 3 illustrates a system 30 according to a further aspect of the present invention. The system 30 comprises said portable electronic device 4 which is removably connected to the eye drop sprayer device 2 to form the eye drop assembly 1. The eye drop sprayer device 2 holds the fluid which is to be administered to an eye 15 of the user 20. The portable electronic device 4 comprises the processor 6 and a camera 5a, 5b; in this example the portable electronic device 4 comprises a first camera 5a, which is a rear-facing camera 5a, and a second camera 5b, which is a front-facing camera 5b. It should be understood that the portable electronic device 4 may comprise any number of cameras; for example, the portable electronicdevice 4 may further comprise one or more additional cameras, in addition to the front-facing camera 5a and the rear-facing camera 5b. For example, the portable electronic device 4 may further comprise a time-of-flight camera. The portable electronic device 4 may comprise any one or more of: a wide-angle camera, a video camera, a time-of flight-camera, an infrared camera.

[0106] The system 30 further comprises a remote server 31 or external platform 31 having stored therein a machine learning algorithm which has been trained using images of healthy eyes and images of eyes having predefined health conditions. The portable electronic device 4 of the eye drop assembly 1 can selectively communicate with the remote server 31 or external platform 31 e.g. to send images to the remote server or external platform and to receive an output from the machine learning algorithm. The portable electronic device 4 has a memory 9 which stores a computer program according to an embodiment of the present invention. It should be understood that the portable electronic device 4 may take any suitable form; in this example shown in Figure 3, the portable electronic device is a smartphone 4. Although the system 30 is shown in Figure 3 to comprise one portable electronic device 4, it should be understood that the system 30 may comprise a plurality of portable electronic devices 4 each of which can selectively communicate with the remote server 31 or external platform 31.

[0107] Before administering any fluid to the eye 15, the computer program which when executed by a processor 6 will cause the processor 6 to instruct the user 20 on how to position the eye drop assembly 1 so that the camera 5a, 5b of the portable electronic device 4 can capture a first image to capture an image of the eye 15. Preferably the user 20 will use the front facing camera 5a to capture a first image of the eye 15, and the processor 6 will generate prompts, which are displayed on the display screen 8, on how to manoeuvre the portable electronic device 4 so that the portable electronic device 4 is preferably brought into a predefined position relative to the eye 15. The processor 6 may detect when the portable electronic device 4 is positioned in said predefined position relative to the eye 15, and initiate the camera 5a to capture the first imageof the eye 5a. Importantly, said first image depicts an eye 15 before fluid has been administered to the eye 15.

[0108] The computer program when executed by a processor 6 will cause the processor 6 to receive the first image.

[0109] Preferably, after the first image has been captured, the computer program when executed by a processor 6 will cause the processor 6 to assist the user 20 to align the spray nozzle 3 of the eye drop assembly 1 with the eye 15 so that the fluid can be sprayed into the eye 15. The processor 6 may determine the position of the spray nozzle 3 relative to the eye 15 using images captured by the camera 5a; and based on said determined position of the spray nozzle 3 relative to the eye 15, determine a direction to move the eye drop assembly 1 to bring the spray nozzle 3 into alignment with the eye 15. As illustrated in Figure 7, the processor 6 displays on the display screen 8 prompts detailing which direction to move the eye drop assembly 1 to bring the spray nozzle 3 into alignment with the eye 15.

[0110] In a preferred embodiment the computer program when executed by a processor 6 will cause the processor 6 to, detect using one or more images captured by the camera 5a of the portable electronic device 4, when the spray nozzle 3 is aligned with the eye 15; and initiate the eye drop sprayer device 2 to spray fluid from the spray nozzle 3 into the eye 15 when the spray nozzle is aligned with the eye 15. Most preferably, the processor 6 will initiate the eye drop sprayer device 2 to spray a predefined volume of fluid from the spray nozzle 3 into the eye 15, wherein the predefined volume fluid sprayed corresponds to a volume of fluid specified in a treatment plan that the user input.

[0111] Most preferably, the computer program when executed by a processor will cause the processor 6 to initiate the eye drop sprayer device 2 to spray the predefined volume of fluid from the spray nozzle 3 into the eye 15 only if the user has been successfully authenticated.

[0112] In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to further, receive as input one or more images depicting the eye, captured by the camera 5a, 5b, when the eyedrop assembly 1 is in a position in which the spray nozzle 3 is aligned with the eye 15; and determine from the received one or more images when the eye 15 is open; and initiate the eye drop sprayer device 2 to spray fluid from the spray nozzle when the eye 15 is open.

[0113] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, receive as input a plurality of images captured by the camera 5a, 5b; and determine a blinking frequency of the eye 15 from said input plurality of images; and use the determined blinking frequency to determine a time instant when the eye 15 is due to be open; and initiate the eye drop sprayer device 2 to spray fluid at said determined time instant so that the fluid is sprayed from the spray nozzle 3 when the eye 15 is due to be open.

[0114] In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to further, receive as input at least a third image depicting the eye 15, captured by the camera 5a, 5b at a time instant corresponding to when the eye drop sprayer device 2 was initiated to spray fluid from the spray nozzle 3; and determine from the at least third image if eye 15 was closed when the fluid reached the eye 15. Preferably, the computer program when executed by a processor 6 will cause the processor 6 to further, prompt the user to align the spray nozzle 3 with the eye 15 again if it is determined from the at least third image that the eye 15 was closed when the fluid reached the eye 15.

[0115] In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to further, receive as input one or more images depicting the eye 15, captured by the camera 5a, 5b, when the eyedrop assembly 1 is in a position in which the spray nozzle 3 is aligned with the eye 15; determine from the received one or more imageswhen the eye 15 is closed; and initiate the eye drop sprayer device 2 to spray fluid from the spray nozzle 3 when the eye 15 is closed.

[0116] In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to further, determine, based on the type of fluid in eye drop sprayer device 2 if the fluid is to be administered to an open eye or to a closed eye; carry out the above- mentioned steps to administer the fluid to an open eye if it is determined that that the fluid is to be administered to an open eye; and / or carry out the above-mentioned steps to administer the fluid to a closed eye if it is determined that that the fluid is to be administered to a closed eye. In the preferred embodiment, when the user 15 selects the type of fluid medication when inputting the treatment plan, the processor 6 will retrieve from a database information about the selected fluid medication. Preferably the information retrieved will include details of how the fluid should be administered; in particular the retrieved information will include details of whether the fluid should be administered to an open eye or closed eye. The processor 6 may determine, based on the information retrieved if the fluid is to be administered to an open eye or to a closed eye.

[0117] After the administering the fluid to the eye 15, the computer program which when executed by a processor 6 will cause the processor 6 to instruct the user 15 on how to position the eye drop assembly 1 so that the camera 5a, 5b of the portable electronic device 4 can capture a second image of the eye 15. Preferably the user 20 will use the front facing camera 5a to capture the second image of the eye 15, and the processor 6 will generate prompts, which are displayed on the display screen 8, on how to manoeuvre the portable electronic device 4 so that the portable electronic device 4 is preferably brought into a predefined position relative to the eye 15. The processor 6 may detect when the portable electronic device 4 is positioned in said predefined position relative to the eye 15, and initiate the camera 5a to capture the second image of the eye 15. Importantly, said second image depicts an eye 15 after the fluid has been administered to the eye 15.

[0118] The computer program when executed by a processor 6 will cause the processor 6 to receive the second image.

[0119] At this point the processor 6 will have received both, the first image which depicts an eye 15 before the fluid has been administered to the eye 15, and also the second image which depicts an eye 15 after the fluid has been administered to the eye 15.

[0120] The computer program when executed by a processor 6 will cause the processor 6 to, initiate processing of the first image and / or second image to determine if the fluid was effective in treating the eye. In this embodiment the computer program which when executed by a processor will cause the processor 6 to initiate processing of the first image and / or second image to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; and initiate determining an effectiveness of the fluid in treating the eye 15. Most preferably the processor 6 determines the effectiveness of the fluid in treating the eye 15 based on if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image.

[0121] The processor 6 can initiate processing of the first image and / or second image and initiate determining the effectiveness of the fluid in treating the eye 15, in different ways. For example, in one embodiment the processing of the first image and / or second image and determining the effectiveness of the fluid in treating the eye 15, may be done locally by the processor 6 on the portable electronic device 4. In another embodiment the processor 6 sends the first image and / or second image to a machine learning algorithm stored on a remote server or external platform; and the machine learning algorithm processes the first image and / or second image and determines an effectiveness of the fluid in treating the eye 15.

[0122] In the example shown in Figure 3, the computer program when executed by a processor 6 will cause the processor 6 to, send the first image and second image to a remote server 31 or external platform 31, whereinthe remote server 31 or external platform 31, has stored therein a machine learning algorithm 32 which has been trained using images of healthy eyes and images of eyes having predefined health conditions. The machine learning algorithm 32 is configured to, determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; and to send to the processor 6 an output which comprises an indication of whether the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image.

[0123] The machine learning algorithm 32 is configured to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.

[0124] In this embodiment the machine learning algorithm 32 is further configured to determine the effectiveness of the fluid in treating the eye 15 based on the comparison. In a preferred embodiment the machine learning algorithm 32 is further configured to determine a magnitude of effectiveness of the medication in treating the eye 15.

[0125] In an embodiment the machine learning algorithm 32 is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in the second image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image; determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

[0126] It should be understood in the present disclosure a rule-based algorithm can be used to instead of a machine learning algorithm. It should be understood that the rule-based algorithm may be configured to performany step which is described in the present disclosure as being performed by the machine learning algorithm.

[0127] Figure 4a illustrates an example of a first image 40a and Figure 4b illustrates an example of a second image 40b. The eyes 15 of the user 20 have a health condition known as "red eye". The first image 40a depicts the eyes 15 before fluid medication for "red eye" has been administered; while the second image 40b depicts the eyes 15 after fluid medication for "red eye" has been administered. In this particular example the machine learning algorithm 32 may configured to determine if the eye 15 depicted in the second image 40b is healthier than the eye 15 depicted in the first image 40a, by identifying the number of red pixels appearing in the first image 40a; identifying the number of red pixels appearing in the second image 40b; comparing the identified number of red pixels appearing in the first image with the identified number of red pixels appearing in the second image; determining that eye 15 depicted in the second image 40b is healthier that the eye 15 depicted in the first image 40a if the number of red pixels appearing in the second image 40b is less than the number of red pixels appearing in the first image 40a. In this example, red pixels may be defined as pixels having a wavelength in the range 600nm-700nm.

[0128] As can be seen from Figures 4a and 4b, the number of red pixels appearing in the second image 40b is less than the number of red pixels appearing in the first image 40a, hence the machine learning algorithm 32 will determine that eye 15 depicted in the second image 40b is healthier that the eye 15 depicted in the first image 40a. Conversely, if the number of red pixels appearing in the second image 40b is more than the number of red pixels appearing in the first image 40a, then the machine learning algorithm 32 will determine that eye 15 depicted in the second image 40b is less healthy than the eye 15 depicted in the first image 40a - in other words the machine learning algorithm 32 will determine that the "red eye" condition has gotten worse between the time of capturing the first image 40a and capturing the second image 40b.

[0129] If the machine learning algorithm 32 determines that the eye 15 depicted in the second image 40b is healthier than the eye 15 depicted in the first image 40a, then the machine learning algorithm 32 can determine that the fluid is effective in treating the eye for "red eye". Conversely, if the machine learning algorithm 32 determines that the eye 15 depicted in the second image 40b is less healthy than the eye 15 depicted in the first image 40a, then the machine learning algorithm 32 can determine that the fluid is ineffective in treating the eye for "red eye".

[0130] In a preferred embodiment the machine learning algorithm 32 is further configured to determine a magnitude of effectiveness of the fluid in treating the eye 15. For example, the magnitude of effectiveness of the fluid in treating the eye for "red eye" could be determined based on the difference between the number of red pixels appearing in the second image 40b and the number of red pixels appearing in the first image 40a; for example, if the difference is larger than a first predefined threshold, then it can be determined that the fluid is highly effective in treating the eye for "red eye"; if the difference is smaller than the first predefined threshold but larger than a second predefined threshold, then it can be determined that the fluid is mildly effective in treating the eye for "red eye; if the difference is smaller than the first predefined threshold and second predefined threshold then it can be determined that the fluid is ineffective in treating the eye for "red eye".

[0131] It should be understood that the pixel colour (e.g. red pixels, in the above example) are only one possible example of a predefined feature present in the first image and second image which can be used to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image. The machine learning algorithm 32 is preferably configured to use any suitable predefined features present in the first and second images to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image. Most preferably the machine learning algorithm 32 is configured to select to use one or more of a plurality of predefined features present in the first and second images, to determine if the eye 15 depicted in the second image ishealthier than the eye 15 depicted in the first image, depending on the health condition being treated.

[0132] Figure 5a illustrates another example of a first image 50a and Figure 5b illustrates another example of a second image 50b. The eyes 15 of the user 20 have a health condition known as "anisocorie" which is characterized in by an unequal size of the eyes' pupils. The first image 50a depicts the eyes 15 before fluid medication for "anisocorie" has been administered; as can be seen in the first image 50a one of the pupils is larger than the other. The second image 50b depicts the eyes 15 after fluid medication for "anisocorie" has been administered; as can be seen in the second image 50b the pupils of both eyes 15 are of equal size.

[0133] In this particular example the machine learning algorithm 32 may configured to determine if the eye 15 depicted in the second image 50b is healthier than the eye 15 depicted in the first image 50a, by identifying the difference between the sizes of the pupils of the eyes 15 appearing in the first image 50a; identifying the difference between the sizes of the pupils of the eyes 15 appearing the second image 50b; determining if the difference between the sizes of the pupils of the eyes 15 appearing the second image 50b is less than the difference between the sizes of the pupils of the eyes 15 appearing in the first image 50a. The machine learning algorithm 32 will determine that eye 15 depicted in the second image 50b is healthier that the eye 15 depicted in the first image 50a if the difference between the sizes of the pupils of the eyes 15 appearing the second image 50b is less than the difference between the sizes of the pupils of the eyes 15 appearing in the first image 50a.

[0134] If the machine learning algorithm 32 determines that the eye 15 depicted in the second image 50b is healthier than the eye 15 depicted in the first image 50a, then the machine learning algorithm 32 can determine that the fluid is effective in treating the eye for "anisocorie". In a preferred embodiment the machine learning algorithm 32 can determine that the fluid is effective in treating the eye for "anisocorie" only if there is nodifference between the sizes of the pupils of the eyes 15 appearing the second image 50b. Conversely, if the machine learning algorithm 32 determines that the eye 15 depicted in the second image 40b is less healthy than the eye 15 depicted in the first image 40a, then the machine learning algorithm 32 can determine that the fluid is ineffective in treating the eye for "anisocorie".

[0135] In a preferred embodiment the machine learning algorithm 32 is further configured to determine a magnitude of effectiveness of the fluid in treating the eye. For example, the magnitude of effectiveness of the fluid in treating the eye for "anisocorie" could be determined based on the difference between the sizes of the pupils of the eyes 15 appearing the second image 50b; for example, if the difference is larger than a first predefined threshold, then it can be determined that the fluid is ineffective in treating the eye 15 for "anisocorie"; if the difference is smaller than the first predefined threshold but larger than a second predefined threshold, then it can be determined that the fluid is mildly effective in treating the eye 15 for "anisocorie"; if the difference is smaller than the first predefined threshold and second predefined threshold then it can be determined that the fluid is highly effective in treating the eye 15 for "anisocorie".

[0136] In another embodiment, the machine learning algorithm 32 may be configured to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes; and determining, based on the comparison, if the eye 15 depicted in the second image more closely resembles a healthy eye than the eye depicted in the first image.

[0137] The reference images may comprise images of healthy eyes and / or images of eyes having predefined health conditions. For example, if the second image more closely resembles a reference image of a healthy eye, than the first image, then the machine learning algorithm 32 candetermine that the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; if the first image more closely resembles a reference image of a healthy eye, than the second image, then the machine learning algorithm 32 can determine that the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image. Or, for example, if the second image more closely resembles a reference image of an eye having predefined health condition, than the first image, then the machine learning algorithm 32 can determine that the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image; if the first image more closely resembles a reference image of an eye having predefined health condition, than the second image, then the machine learning algorithm 32 can determine that the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image.

[0138] If the machine learning algorithm 32 determines that the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, then the machine learning algorithm 32 can determine that the fluid is effective in treating the eye. If the machine learning algorithm 32 determines that the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image, then the machine learning algorithm 32 can determine that the fluid is ineffective in treating the eye.

[0139] In a preferred embodiment the machine learning algorithm 32 is further configured to determine a magnitude of effectiveness of the fluid in treating the eye. For example, machine learning algorithm 32 can determine a magnitude of effectiveness of the fluid based on how much the second image more closely resembles a reference image of a healthy eye, than the first image. Or, in another example, the machine learning algorithm 32 can determine a magnitude of effectiveness of the fluid based on how much the first image more closely resembles a reference image of an eye having predefined health condition, than the second image.

[0140] In a preferred embodiment the machine learning algorithm 32 is further configured to process the first image and / or second image to determine if the eye has a predefined health condition or is healthy. In other words, in a preferred embodiment the machine learning algorithm 32 is further configured to process the first image and / or second image to provide a diagnosis. The machine learning algorithm 32 may be configured to send the diagnosis to the portable electronic device 4 where it is displayed on the display screen 8 of the portable electronic device 4.

[0141] The machine learning algorithm 32 will send output to the processor 6, wherein said output includes an indication of whether the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, and / or an indication of whether the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image. In a preferred embodiment the output will further include an indication of the effectiveness of the fluid in treating the eye 15. In a preferred embodiment the output will further include an indication of the magnitude of effectiveness of the fluid in treating the eye 15.

[0142] The computer program when executed by a processor 6 will cause the processor 6 to, receive the output from the machine learning algorithm 32; and to display on a display screen 8 of the portable electronic device 4 an indication of the received output. In other words the computer program when executed by a processor 6 will cause the processor 6 to display on the display screen 8 any one or more of: an indication of whether the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, and / or an indication of whether the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image; and / or an indication of the effectiveness of the fluid in treating the eye; and / or an indication of the magnitude of effectiveness of the fluid in treating the eye.

[0143] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to prompt the user to visit thephysician if: the eye depicted in the second image is less healthy than the eye depicted in the first image, and / or if it the fluid is ineffective in treating the eye, and / or if the magnitude of effectiveness of the fluid in treating the eye is below a predefined threshold.

[0144] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to display on the display screen 8 of the portable electronic device 4 the progress of the treatment. Specifically, as shown in Figure 6, the computer program when executed by a processor 6 will cause the processor 6 to display on the display screen 8 of the portable electronic device 4 details of the treatment already completed (including the number of the dosages already given) and details of the treatment plan remaining (including the number of remaining dosages to be given). In the example illustrated in Figure 6 the computer program when executed by a processor 6 will cause the processor 6 to display on the display screen 8 of the portable electronic device 4a a summary of the treatment plan, including the name of the fluid medication being administered and the frequency of administration (i.e. the number of daily intakes).

[0145] The computer program when executed by a processor 6 will cause the processor 6 to, initiate processing of the first image and / or second image to determine if the fluid was effective in treating the eye. In an embodiment the computer program when executed by a processor 6 will cause the processor to initiate processing of the first image and / or second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye.

[0146] In a preferred embodiment the computer program when executed by the processor causes the processor initiate the processing of the first image and / or second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye, by sending the first image and second image to a remote server or external platform, wherein the remote server orexternal platform has stored thereon a machine learning algorithm which has been trained using images of eyes in which the one or more predefined changes have occurred and images of eyes in which the one or more predefined changes have not occurred. The machine learning algorithm is configured to determine if the one or more predefined changes in the eye have occurred by comparing the second image to the first image, and / or by comparing each of the first image and second image to one or more references images (preferably the one or more reference images comprise images of eyes in which the one or more predefined changes in the eye have occurred and images of eyes in which the one or more predefined changes in the eye have not occurred).

[0147] In an embodiment it is determined that the eye has a predefined defect, or predefined health condition, if the one or more predefined changes in the eye have not occured after the fluid was administered to the eye. Or, it is determined that the eye has a predefined defect, or predefined health condition, if the one or more predefined changes in the eye did not occur after the fluid was administered to the eye. For example, ophthalmology Atropin is used in a concentration of one percent to temporarily paralyze the muscle of an eye before an eye examination and to dilate the pupil. A user may capture a first image of the eye prior to administering ophthalmology Atropin to the eye, and capture a second image of the eye after administering ophthalmology Atropin to the eye. In this example the one or more predefined changes in the eye comprise a widening of the pupil. Accordingly, the first and second images are sent to the machine learning algorithm and the machine learning algorithm determines if the pupil of the eye depicted in the second image is wider than the pupil of the eye depicted in the first image; if the pupil of the eye depicted in the second image is not wider than the pupil of the eye depicted in the first image, then the machine learning algorithm will determine that the eye is suffering from a predefined defect, or predefined health condition, in the form of a pupil disorder. The machine learning algorithm may output the determined predefined defect, or predefined health condition, to the portable electronic device. The computer program when executed by a processor will cause the processor to receive theoutput from the machine learning algorithm and to display an indication of the determined predefined defect, or predefined health condition, on the display screen; in this particular example an indication that the eye is suffering from a pupil disorder will be display on the display screen.

[0148] In a preferred embodiment the machine learning algorithm 32 is further configured to determine if the fluid which has been used to treat the eye 15, has caused any side effects.

[0149] In an embodiment the machine learning algorithm 32 is configured to determine if the fluid which has been used to treat the eye 15, has caused any side effects by comparing the second image to one or more reference images. Preferably the one or more reference images are images depicting eyes having side effects which are known to occur from said fluid.

[0150] In an embodiment the machine learning algorithm 32 is configured to identifying if there are one or more predefined features appearing in the second image, wherein the predefined features are features associated with one or more predefined side effects. The machine learning algorithm 32 may determine if a side effect has occurred and which side effect has occurred, based on the one or more predefined features appearing in the second image.

[0151] Consider, an example wherein the fluid which is used to treat the eye 15 is Roclanda / Rocklatan (which contains Latanaprost, for the reduction of elevated intraocular pressure in (glaucoma) patients).Predefined side effects which are known to be occasionally caused by Roclanda include, subconjunctival hemorrhage (broken blood vessel in eye); vortex keratopathy; whorl keratopathy; eye redness and / or conjunctival hyperemia. Each of these predefined side effects may have one or more predefined image features associated with them; for example, the side effect subconjunctival hemorrhage may have the predefined image feature of red pixels appearing in the sclera region of the eye, associated with it.Staying with this particular example, the machine learning algorithm 32 may be configured to identifying if there are red pixels in the sclera region of the eye 15 depicted in the second image; and if the number of red pixels in the sclera region of the eye 15 depicted in the second image is above a predefined threshold, then the machine learning algorithm 32 may determine that the fluid has caused the side effect of subconjunctival hemorrhage to occur. If there are no predefined image features identified in the second image by the machine learning algorithm 32 then the machine learning algorithm 32 determines that no side effects have been caused by the fluid used to treat the eye 15.

[0152] Different known side effects of different known fluids are predefined and available to the machine learning algorithm 32 when processing the second image to determine if a side effect has occurred. All of the know side effects will have respective one or more predefined image features associated with them, and the machine learning algorithm 32 can determine if a side effect has occurred and which side effect has occurred based on the image features identified in the second image. Preferably predefined the known side effects of different known fluids and their respective associated predefined image features will be stored in a memory which is accessible to the machine learning algorithm 32.

[0153] If the machine learning algorithm 32 determines that the fluid which has been used to treat the eye 15, has caused a side effect, then the output which the machine learning algorithm 32 sends to the processor 6, will further comprise an indication of the side effect. The computer program when executed by a processor 6 will cause the processor 6 to, receive the output from the machine learning algorithm 32; and to display on a display screen 8 of the portable electronic device 4 an indication of the received output. In other words the computer program when executed by a processor 6 will cause the processor 6 to display on the display screen 8 any one or more of: an indication of whether the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, and / or an indication of whether the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image; and / or an indication ofthe effectiveness of the fluid in treating the eye; and / or an indication of the magnitude of effectiveness of the fluid in treating the eye; and / or an indication of the side effect that the fluid has caused (and / or an indication that no side-effects were detected).

[0154] In a preferred embodiment the computer program when executed by a processor 6 will cause the processor 6 to, prompt the user to visit their Physician, if it has been determined that the fluid which has been used to treat the eye 15, has caused any side effects.

[0155] In a preferred embodiment the computer program when executed by a processor 6 will cause the processor 6 to adjust the treatment plan based on the health of the eye depicted in the second image, and / or based on the effectiveness of the fluid in treating the eye and / or based on the magnitude of effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan.

[0156] For example, processor 6 receives the output from the machine learning algorithm 32; and the computer program when executed by a processor 6 will cause the processor 6 to adjust the treatment plan (e.g. adjust the original treatment plan that the user input) based on the received output, to provide an adjusted treatment plan. In other words, the processor 6 may adjust the treatment plan based on whether the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; and / or base on the effectiveness of the fluid in treating the eye 15; and / or based on the magnitude of effectiveness of the fluid in treating the eye 15. For example, if it has been determined by the machine learning algorithm 32 that the eye 15 depicted in the second image is less healthy than the eye 15 depicted in the first image then the processor 6 may adjust the treatment plan to increase the number of administrations per day (i.e. increase the number of intakes per days). In another example, if it has been determined by the machine learning algorithm 32 that the fluid is ineffective in treating the eye, and / or that the magnitude of the effectiveness is below a predefined threshold, the processor 6 mayrecommend to the user 20 to administer a different type of fluid medication to the eye 15, and will preferably adjust the treatment plan to be suitable for that different type of fluid medication.

[0157] Preferably, the computer program when executed by a processor 6 will cause the processor 6 to display on a display screen 8 of the portable electronic device 4 the adjusted treatment plan. Most preferably any one or more of above-mentioned features / step described with respect to the treatment plan equally apply to the adjusted treatment plan. For example, preferably, the processor 6 will transfer the user authentication to be associated with the adjusted treatment plan; and preferably the alarms will be adjusted to the adjusted treatment plan. In an embodiment the adjusted treatment plan may, or may not, be offered to the Physician for signing off before shown to the user. For example, in an embodiment, the computer program when executed by a processor 6 will cause the processor 6 to send the adjusted treatment plan to the user's Physician; the Physician can approve, or disapprove, the adjusted treatment plan. The processor 6 will receive an indication of whether the Physician approved, or disapproved, the adjusted treatment plan. In an embodiment the processor 6 will only present the adjusted treatment plan to the user if the Physician has approved the adjusted treatment plan.

[0158] In should be understood that the computer program when executed by a processor 6 will cause the processor 6 to make any suitable adjustment to the treatment plan to provide said adjusted treatment plan. For example, the computer program when executed by a processor 6 will cause the processor 6 to adjust at least one of, a duration for treatment; a frequency of treatment; a type of fluid to be administered; the volume of fluid to be administered; the part of the eye to which to administer the fluid (e.g. if the fluid should be administered to the sclera of the eye or to the pupil or to the edge of the eye lid). For example, the computer program when executed by a processor 6 may cause the processor 6 to adjust any one or more of: the type of fluid to be administered (i.e. suggest a new fluid medication to be administered), and / or the days of the week the fluid is to be administered to the eye, and / or the number ofadministrations per day (i.e. the number of intakes per days), and / or the time of the day for each administration (i.e. the time of the day for each intake).

[0159] In a further embodiment the machine learning algorithm 32 is configured to adjust the treatment plan based on the health of the eye depicted in the second image, and / or based on the effectiveness of the fluid in treating the eye and / or based on the magnitude of effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan. The machine learning algorithm 32 outputs the adjusted treatment plan to the processor 6. The computer program when executed by a processor 6 will cause the processor 6 to receive the adjusted treatment plan from the machine learning algorithm 32, and display on a display screen 8 of the portable electronic device 4 the adjusted treatment plan.

[0160] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to prompt a user to capture one or more images of the eye 15, preferably at predefined intervals, over a period of time; and store the captured images to form a documentation of the status of the health of the eye 15 over said period of time. This documentation, and / or details of the treatment plans (and / or adjusted treatment plans) followed by the user, and / or data relating to the effectiveness of the fluid in treating the eye, and / or data relating to the magnitude of the effectiveness of the fluid in treating the eye, can be sent to a server where it is stored. This data can optionally be used to optimize treatment plans and / or to develop improved fluid medications.

[0161] In the embodiment illustrated in Figure 3, the processing of the first image and / or second images to determine if the fluid was effective in treating the eye is done remotely by a machine learning algorithm 32 which is stored on a remote server 31 or external platform 31; in particular, the processing of the first image and / or second images and determining an effectiveness of the fluid in treating the eye 15, is done remotely by amachine learning algorithm 32 which is stored on a remote server 31 or external platform 31.

[0162] In a further embodiment the processing of the first image and / or second image to determine if the fluid was effective in treating the eye may be done locally by the processor 6 on the portable electronic device 4; in other words the computer program when executed by a processor 6 will cause the processor 6 to process the first image and / or second image to determine if the fluid was effective in treating the eye.

[0163] In an embodiment, the computer program when executed by a processor 6 will cause the processor 6 to process the first image and / or second image to determine if one or more predefined changes in the eye have occurred as a result of the fluid having been administered to the eye. For example, the computer program when executed by a processor 6 will cause the processor 6 to determine if one or more predefined changes in the eye have occurred by comparing the second image to the first image, and / or by comparing each of the first image and second image to one or more references images (preferably the one or more reference images comprise images of eyes in which the one or more predefined changes in the eye have occurred and images of eyes in which the one or more predefined changes in the eye have not occurred). The computer program when executed by a processor 6 will cause the processor 6 to determine that the eye has a predefined defect, or predefined health condition, if the one or more predefined changes in the eye did not occur after the fluid was administered to the eye; or, the computer program when executed by a processor 6 will cause the processor 6 to determine that the eye has a predefined defect, or predefined health condition, if one or more predefined changes in the eye have occurred after the fluid was administered to the eye. The computer program when executed by a processor 6 will cause the processor 6 to display an indication of the determined predefined defect, or predefined health condition, on the display screen 8 of the portable electronic device 4.

[0164] In an embodiment, the processing of the first image and / or second image and determining an effectiveness of the fluid in treating the eye 15, may be done locally by the processor 6 on the portable electronic device 4. In such an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, processes the first image and second images to determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image; and determine the effectiveness of the fluid in treating the eye 15, preferably based on if the eye depicted in the second image is healthier than the eye 15 depicted in the first image.

[0165] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, determine if the eye 15 depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images. Preferably the one or more reference images comprise reference images which depict healthy eyes and reference images which depict eyes with predefined health conditions.

[0166] It should be understood that the computer program when executed by a processor 6 may cause the processor 6 to carry out any of the steps that are described above in the preceding embodiment as being carried out by the machine learning algorithm 32. In particular, the computer program when executed by a processor 6 may cause the processor 6 to carry out any of the steps that are described above in the preceding embodiment as being carried out by the machine learning algorithm 32, to determine if the fluid was effective in treating the eye.

[0167] For example, the computer program when executed by a processor 6 may cause the processor 6 to, determine if the eye 15 depicted in the second image is healthier than the eye 15 depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in thesecond image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image; determining based on the comparison if the eye depicted in the second image more closely resembles a healthy eye than the eye depicted in the first image. It should be understood that the computer program may be configured to display on the display screen 8 of the electronic device 4 any one or more of the aspects described above in the preceding embodiment which includes the machine learning algorithm 32.

[0168] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes, and determine based on the comparison if the eye depicted in the second image more closely resembles a healthy eye than the eye depicted in the first image.

[0169] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, process the first image and / or second image to determine if the eye has a predefined health condition or is healthy. In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, display diagnosis on the display screen 8 of the portable electronic device 4.

[0170] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to determine if the fluid which has been used to treat the eye 15, has caused any side effects. In an embodiment, the computer program when executed by a processor 6 will cause the processor 6 to determine if the fluid which has been used to treat the eye 15, has caused any side effects by, comparing the second image to one or more reference images. Preferably the one or more reference images may be images of eyes having side effects which are known to occur from said fluid. In an embodiment the computer program when executedby a processor 6 will cause the processor 6 to determine if the fluid which has been used to treat the eye 15, has caused any side effects by identifying if there are one or more predefined features appearing in the second image, wherein the predefined features are features associated with one or more predefined side effects. The processor 6 may determine if a side effect has occurred, and / or which side effect has occurred, based on the one or more predefined features appearing in the second image. In a preferred embodiment the computer program when executed by a processor 6 will cause the processor 6 to, prompt the user to visit their Physician, if it determined that the fluid which has been used to treat the eye 15, has caused any side effects.

[0171] In a further example the portable electronic device 4 may further comprise one or more lights which are selectively operable to shine a light having a predefined characteristic.

[0172] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to, operate the light of the portable electronic device 4 to shine light having a predefined characteristic, into the eye 15; and operate the camera 5a, 5b to capture one or more images of the eye 15 as the light shines into the eye 15; and process said captured one or more images to determine the reaction of the eye 15 to the light having a predefined characteristic. In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to, carry out the aforementioned steps prior to administering fluid to the eye 15 to determine a first reaction of the eye 15; and carry out the aforementioned steps again after administering fluid to the eye 15 to determine a second reaction of the eye 15. In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye 15.

[0173] In an embodiment the computer program when executed by a processor 6 will cause the processor 6 to further, operate the light tochange the characteristics of the light shining into the eye 15; and operate the camera 5a, 5b to capture one or more images of the eye 15 as the characteristics of the light are changed; and process said captured one or more images to determine the reaction of the eye 15 to the changing light characteristics. In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to, carry out the aforementioned steps prior to administering fluid to the eye 15 to determine a first reaction of the eye; and carry out the aforementioned steps again after administering fluid to the eye 15 to determine a second reaction of the eye. In an embodiment the computer program which when executed by a processor 6 will cause the processor 6 to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye.

[0174] In yet a further embodiment of the present invention, the computer program when executed by a processor 6 will cause the processor 6 to, send the first image and second image to an external platform, wherein said external platform can be accessed by a Physician to view said first and second image, and wherein the Physician can provide an input via the platform; and receive from the platform said input of the Physician; and display on the display screen 8 of the portable mobile device 4 said received input. The input of the Physician may comprise at least one of, comments, and / or an indication of the effectiveness of the fluid in treating the eye and / or determining side effects of the fluid, and / or an adapted treatment plan, via the platform.

[0175] Various modifications and variations to the described embodiments of the invention will be apparent to those skilled in the art without departing from the scope of the invention as defined in the appended claims. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiment.

Claims

Claims1. A computer program, for determining the effectiveness of a fluid in treating an eye (15) which has an adverse condition, the computer program being stored on a portable electronic device (4), the portable electronic device (4) having a processor (6) and a camera (5a, 5b); and wherein the portable electronic device (4) can connect with an eye drop sprayer device (2) which has a spray nozzle (3) to form an eye drop assembly (1); and wherein the eye drop sprayer device (2) holds a fluid which can be administered to an eye (15); wherein the computer program which when executed by a processor (6) will cause the processor (6) to, receive a first image (40a, 50a) captured by the camera (5a, 5b), wherein said first image (40a, 50a) depicts an eye (15) before fluid has been administered to the eye (15); receive a second image (40b, 50b) captured by the camera (5a, 5b), wherein said second image (40b, 50b) depicts the eye (15) after fluid has been administered to the eye (15); initiate processing of the first image (40a, 50a) and / or second image (40b, 50b) to determine if the fluid was effective in treating the eye.

2. The computer program according to claim 1 which when executed by a processor (6) will cause the processor (6) to initiate processing of the first image (40a, 50a) and / or second image (40b, 50b) to determine if the eye (15) depicted in the second image (40b, 50b) is healthier than the eye (15) depicted in the first image (40a, 50b); and initiate determining an effectiveness of the fluid in treating the eye (15), based on if the eye (15) depicted in the second image (40b, 50b) is healthier than the eye depicted in the first image (40a, 50a).

3. The computer program according to claim 1 or 2 wherein the computer program when executed by a processor will cause the processor to, send the first image and second image to a remote server or external platform, wherein the remote server or external platform, has storedtherein a machine learning algorithm which has been trained using images of healthy eyes and images of eyes having predefined health conditions, wherein the machine learning algorithm is configured to, determine if the eye depicted in the second image is healthier than the eye depicted in the first image; send an output which comprises an indication if the eye depicted in the second image is healthier than the eye depicted in the first image, to the processor.

4. The computer program according to claim 3 wherein the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.

5. The computer program according to claim 4 wherein the machine learning algorithm is configured to determine the effectiveness of the fluid in treating the eye based on the comparison.

6. The computer program according to any one of claims 3-5 wherein the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in the second image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image; determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

7. The computer program according to any one of claims 3-5 wherein the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes, and determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

8. The computer program according to any one of the preceding claims which when executed by a processor will cause the processor to initiate processing of the first image and / or second image to determine if one or more predefined changes in the eye has occurred as a result of the fluid having been administered to the eye.

9. The computer program according to claim 8 wherein the computer program when executed by a processor will cause the processor to, send the first image and second image to a remote server or external platform, wherein the remote server or external platform has stored therein a machine learning algorithm which has been trained using images of eyes in which one or more predefined changes has occurred and images of eyes in which said one or more predefined changes has not occurred, wherein the machine learning algorithm is configured to, determine using the first and second image, if said one or more predefined changes has occurred in the eye depicted in the second image, and, determine that the eye has a predefined defect, or predefined health condition, depending on if said one or more predefined changes in the eye has occurred in the eye depicted in the second image.

10. The computer program according to any one of claims 2-9 wherein the machine learning algorithm is further configured to determine from the second image, if the fluid which has been used to treat the eye, has caused any side effects.11.The computer program according to any one of claims 2-10 wherein the computer program when executed by a processor will cause the processor to, receive the output from the remote server or external platform; and display on a display screen of the portable electronic device said indication of the received output and / or an indication of the effectiveness of the fluid in treating the eye.

12. The computer program according to claim 1 wherein the computer program when executed by a processor will cause the processor to, processes the first image and second image to determine if the eye depicted in the second image is healthier than the eye depicted in the first image; and determine the effectiveness of the fluid in treating the eye, based on if the eye depicted in the second image is healthier than the eye depicted in the first image.

13. The computer program according to claim 12 wherein the computer program when executed by a processor will cause the processor to, determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.

14. The computer program according to claim 13 wherein the computer program is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, identifying one or more predefined features appearing in the first image; identifying one or more predefined features appearing in the second image; comparing the identified predefined features appearing in the first image with the identified predefined features appearing in the second image;determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

15. The computer program according to claim 13 wherein the computer program is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by, comparing the first image to one or more reference images which depict healthy eyes and comparing the second image to one or more reference images which depict healthy eyes, and determining based on the comparison if the eye depicted in the second image is healthier than the eye depicted in the first image.

16. The computer program according to any one of claims 12-15 which when executed by a processor will cause the processor to process the first image and / or second image to determine if one or more predefined changes in the eye has occurred as a result of the fluid having been administered to the eye.

17. The computer program according to claim 16 which when executed by a processor will cause the processor to determine that the eye has a predefined defect, or predefined health condition, depending on if said one or more predefined changes in the eye has occurred in the eye depicted in the second image.

18. The computer program according to any one of claims 12-17 wherein the computer program when executed by a processor will cause the processor to determine from the second image, if the fluid which has been used to treat the eye, has caused any side effects.

19. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to receive as an input a treatment plan which has been input by the user.

20. The computer program according to claim 19 wherein the computer program when executed by a processor will cause the processor prompt the user to input the treatment plan into the portable electronic device.

21. The computer program according to claim 19 or 20 wherein the computer program when executed by a processor will cause the processor to display on a display screen of the mobile device the treatment plan.

22. The computer program according to any one of claims 19-21 wherein the treatment plan comprises at least one or more of: a duration for treatment; a frequency of treatment; a type of fluid to be administered; the volume of fluid to be administered; the part of the eye to which to administer the fluid.

23. The computer program according to any one of claims 19-22 wherein the treatment plan comprises a user authentication associated with it, wherein said user authentication is defined by predefined characteristics of the eye of the user; and wherein the computer program when executed by a processor will cause the processor to authenticate the user by processing an image of the eye of the user captured by the camera of the portable electronic device, to determine if the eye depicted in the image comprises said predefined characteristics.

24. The computer program according to any one of claims 19-23 wherein the computer program when executed by a processor will cause the processor to adjust the treatment plan based on the health of the eye depicted in the second image, to provide an adjusted treatment plan.

25. The computer program according to claim 24 wherein the adjusted treatment plan is formed by adjusting at least one of, a duration for treatment, a frequency of treatment, a type of fluid to be administered, a volume of fluid to be administered, the part of the eye to which toadminister the fluid, that was specified in a treatment plan that the user input to the portable electronic device.

26. The computer program according to any one of claims 24 or 25 wherein the computer program when executed by a processor will cause the processor to process the second image to determine the amount of predefined features depicted in the second image, and to determine the health of the eye depicted in the second image based the amount of predefined features depicted in the second image.

27. The computer program according to any one of claims 19-26 wherein the computer program when executed by a processor will cause the processor to adjust the treatment plan based on the determined effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan.

28. The computer program according to any one of claims 19-25 wherein the computer program when executed by a processor will cause the processor to send the second image to a machine learning algorithm and wherein the machine learning algorithm is configured to adjust the treatment plan based on the health of the eye depicted in the second image, to provide an adjusted treatment plan.

29. The computer program according to claim 28 wherein a machine learning algorithm is configured to process the second image to determine the health of the eye depicted in the second image; and send to the processor said adjusted treatment plan.

30. The computer program according to claim 29 wherein a machine learning algorithm is configured to process the second image to determine the amount of predefined features depicted in the second image, to determine the health of the eye depicted in the second image.

31. The computer program according to any one of claims 27-30 wherein a machine learning algorithm is configured to adjust the treatment plan based on the determined effectiveness of the fluid in treating the eye, to provide an adjusted treatment plan.

32. The computer program according to any one of claims 19-31 wherein the computer program when executed by a processor will cause the processor to display on a display screen of the portable electronic device the adjusted treatment plan.

33. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to, detect using one or more images captured by the camera of the portable electronic device, when the spray nozzle is aligned with the eye; and initiate the eye drop sprayer device to spray fluid from the spray nozzle into the eye when the spray nozzle is aligned with the eye.

34. The computer program according to claim 33 wherein the computer program when executed by a processor will cause the processor to, initiate the eye drop sprayer device to spray a predefined volume of fluid from the spray nozzle into the eye.

35. The computer program according to claim 34 wherein the predefined volume fluid sprayed corresponds to a volume of fluid specified in a treatment plan that the user input, or, wherein the predefined volume fluid sprayed corresponds to a volume of fluid specified in an adjusted treatment plan.

36. The computer program according to claim 34 or 35 wherein the computer program when executed by a processor will cause the processor to initiate the eye drop sprayer device to spray the predefined volume offluid from the spray nozzle into the eye only if the user has been authenticated.

37. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to generate one or more alarms on a schedule corresponding to a schedule of a treatment plan that the user input or corresponding to a schedule of an adjusted treatment plan, wherein said alarms remind the user to administer the fluid to the eye at times corresponding to times specified in said treatment plan or adjusted treatment plan.

38. The computer program a according to any one of the wherein the computer program when executed by a processor will cause the processor to, detect which type of fluid is in the eye drop sprayer device; and prompt the user to change the fluid, if the fluid in the eye drop sprayer device does not correspond with a fluid type specified in a treatment plan that the user input, or, if the fluid in the eye drop sprayer device does not correspond with a fluid type specified in an adjusted treatment plan.

39. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to, detect if the fluid in the eye drop sprayer device is empty or below a predefined critical volume; and to prompt the user to change the fluid or refill the fluid, if the fluid in the eye drop sprayer is empty or below the predefined critical volume.

40. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to prompt the user to visit the physician if the eye depicted in the second image is less healthy than the eye depicted in the first image.41.The computer program according to any one of the preceding claims wherein the computer program which when executed by a processor will cause the processor to further, receive as input one or more images depicting the eye, captured by the camera of the portable electronic device when the eyedrop assembly is in a position in which the spray nozzle is aligned with the eye; determine from the received one or more images when the eye is open; and initiate the eye drop sprayer device to spray fluid from the spray nozzle when the eye is open.

42. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to, receive as input a plurality of images captured by the camera of the portable electronic device; and determine a blinking frequency of the eye from said input; use the determined blinking frequency to determine a time instant when the eye is due to be open; and initiate the eye drop sprayer device to spray fluid at said determined time instant so that the fluid is sprayed from the spray nozzle when the eye is due to be open.

43. The computer program according to any one of claims 41 or 42 wherein the computer program which when executed by a processor will cause the processor to further, receive as input at least a third image depicting the eye, captured by the camera at a time instant corresponding to when the eye drop sprayer device was initiated to spray fluid from the spray nozzle; determine from the at least third image if eye was closed when the fluid reached the eye.

44. The computer program according to claim 43 wherein the computer program is when executed by a processor will cause the processor tofurther, prompt the user to align the spray nozzle with the eye again if it is determined from the at least third image that the eye was closed when the fluid reached the eye.

45. The computer program according to any one of the preceding claims wherein the computer program which when executed by a processor will cause the processor to further, receive as input one or more images depicting the eye, captured by the camera of the portable electronic device, when the eyedrop assembly is in a position in which the spray nozzle is aligned with the eye; determine from the received one or more images when the eye is closed; and initiate the eye drop sprayer device to spray fluid from the spray nozzle when the eye is closed.

46. The computer program according to any one of the preceding claims wherein the computer program which when executed by a processor will cause the processor to further, determine, based on the type of fluid in the eye drop sprayer device if the fluid is to be administered to an open eye or to a closed eye; carry out the steps of claim 41 or 42 if it is determined that that the fluid is to be administered to an open eye, carry out the steps of claim 45 if it is determined that that the fluid is to be administered to a closed eye.

47. The computer program according to any one of the preceding claims, wherein the mobile device has a light, and wherein the computer program which when executed by a processor will cause the processor to further, operate the light to shine light, having a predefined characteristic, into the eye; operate the camera to capture one or more images of the eye as the light shines into the eye; and process said captured one or more images to determine the reaction of the eye to the light having a predefined characteristic.

48. The computer program according to claim 47 wherein the computer program which when executed by a processor will cause the processor to, carry out the steps of claim 47 prior to administering fluid to the eye to determine a first reaction of the eye; and carry out the steps of claim 47 after administering fluid to said eye to determine a second reaction of the eye.

49. The computer program according to claim 48 wherein the computer program which when executed by a processor will cause the processor to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye.

50. The computer program according to any one of claims 47-49 wherein the computer program which when executed by a processor will cause the processor to further, operate the light to change the characteristics of the light shining into the eye; operate the camera to capture one or more images of the eye as the characteristics of the light are changed; and process said captured one or more images to determine the reaction of the eye to the changing light characteristics.

51. The computer program according to claim 50 wherein the computer program which when executed by a processor will cause the processor to, carry out the steps of claim 50 prior to administering fluid to the eye to determine a first reaction of the eye; and carry out the steps of claim 50 after administering fluid to the eye to determine a second reaction of the eye.

52. The computer program according to claim 51 wherein the computer program which when executed by a processor will cause the processor to compare the first reaction and second reaction to determine the effectiveness of the fluid in treating the eye.

53. The computer program according to any one of the preceding claims, wherein the computer program which when executed by a processor will cause the processor to, prompt a user to capture one or more images of the eye, over a period of time; and store the captured images to form a documentation of the status of the health of the eye over said period of time.

54. The computer program according to any one of the preceding claims, wherein the computer program which when executed by a processor will cause the processor to process the first image and / or second image to determine if the eye has a predefined health condition or is healthy.

55. The computer program according to any one of the preceding claims wherein the computer program when executed by a processor will cause the processor to, send the first image and second image to an external platform, wherein said external platform can be accessed by a Physician to view said first and second image, and wherein the physician can provide an input via the platform; receive from the platform said input of the physician; and display on the display screen of the portable mobile device said received input.

56. The computer program according to claim 55 wherein said input of the Physician comprises at least one of, comments, and / or an indication of the effectiveness of the fluid in treating the eye, and / or an adapted treatment plan.

57. The computer program according to any one of the preceding claims wherein the fluid comprises a medication.

58. The computer program according to any one of the preceding claims wherein the portable electronic device is a smartphone.

59. A portable electronic device (4) having a memory (6) which stores a computer program according to any one of the preceding claims.

60. A system (30) comprising, one or more portable electronic devices (4) according to claim 59; and a remote server (31) or external platform (31) having stored therein a machine learning algorithm (32) which has been trained using images of healthy eyes and images of eyes having predefined health conditions; and wherein each of the one or more portable electronic devices (4) can selectively communicate with the remote server (31) or external platform (31).

61. The system according to claim 60 wherein the machine learning algorithm is configured to receive at least the first and second image from a portable electronic device, wherein each of the first and second images depict an eye; and determine if the eye depicted in the received second image is healthier than the eye depicted in the first image; and send an output to said portable electronic device which comprises an indication if the eye depicted in the second image is healthier than the eye depicted in the first image.

62. The system according to claim 61 wherein the machine learning algorithm is configured to determine if the eye depicted in the second image is healthier than the eye depicted in the first image, by comparing the second image to the first image, or by comparing each of the first image and second image to one or more references images.

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