Description:FIELD OF THE INVENTION
[001] The present invention relates to a system and method for determining health-status of a person. More particularly, the present invention pertains to a system and method for determining health-status through non-invasive symptomatic examination of irises.

BACKGROUND OF THE INVENTION
[002] In modern healthcare, diagnosing health conditions typically relies on various medical tests such as blood tests, urine analysis, and other laboratory-based examinations. These conventional diagnostic approaches require physical samples, making them invasive, time-consuming, and sometimes uncomfortable for patients. Additionally, such tests are often organ-specific and may not provide a holistic assessment of the body's overall health.

[003] Iris-based health diagnosis has been historically practiced through manual iris examination by experienced doctors and iridologists. In this approach, specialists analyze specific signs in a patient’s iris to assess the condition of select organs or body systems. However, manual iris diagnosis requires highly specialized expertise, which is not widely available. Moreover, subjective interpretation by different practitioners may lead to inconsistencies in diagnosis. One of known prior art is patent document CN103924648 titled - Intelligent toilet capable of dynamically monitoring human body health status. Another patent document is US20110187845 titled - System for iris detection, tracking and recognition at a distance.

[004] Accordingly, there is a need for a non-invasive health diagnosis system that can analyze iris images to determine a person’s overall health condition efficiently and accurately. Therefore, there is a need for a system and method for determining health-status through non-invasive symptomatic examination of irises.

OBJECTS OF THE INVENTION
[005] Some of the objects of the arrangement of the present disclosure are aimed to ameliorate one or more problems of the prior art or to at least provide a useful alternative and are listed herein below.

-A principle object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that analyzes iris images using deep learning to enable real-time health assessment without requiring physical samples or invasive procedures.

-Another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that enables segmentation of iris images into diagnostic zones corresponding to an iridology chart for a comprehensive health assessment.

-Yet another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that utilizes a deep learning multimodal convolutional neural network (CNN) to analyze symptomatic features, such as color gradation, shade variation, lacunae, pigment patterns, and structural defects, for accurate health diagnosis.

-Still another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that stores and updates diagnosis parameters in a database, allowing continuous learning and refinement of symptomatic correlations based on acquired iris data.

-Another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that enhances diagnostic accuracy by capturing multiple layers of the iris, thereby revealing deeper symptomatic details.

Still another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that provides an illumination mechanism by defining the light-source to emanate light at an angle of 45 degrees, optimizing visibility of symptomatic features.

Yet another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that determines a comprehensive health status, including health analysis and health recommendations, to assist in preventive healthcare and early disease detection.

Another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that displays the determined health status on a display screen, providing users with a clear and accessible health report.

Still another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that reduces dependence on human expertise by eliminating the need for manual iridology examinations, making health diagnostics more widely accessible.

Yet another object of the present disclosure is to provide a system and method for determining health-status through non-invasive symptomatic examination of irises that enables faster and cost-effective health analysis compared to conventional medical tests, promoting preventive healthcare solutions.

Other objects and advantages of the present disclosure will be more apparent from the following description when read in conjunction with the accompanying figures, which are not intended to limit the scope of the present disclosure.

SUMMARY OF THE INVENTION
[006] The present invention discloses a system for determining health-status through non-invasive symptomatic examination of irises, enabling real-time health assessment without requiring physical samples or invasive procedures. In accordance with one embodiment, the system includes a light-source, an imaging-device, a database, a controller and a display-screen. The light-source is defined to illuminate the irises of a person undergoing examination for visibility of symptomatic-features. The imaging-device is defined to capture digital-representations of the irises of the person undergoing examination. The database is communicatively connected to the imaging-device. The database defined to: i) store the captured digital-representations of the imaging-device,
ii) maintain predefined diagnosis-parameters associated with symptomatic features of healthy and unhealthy irises, and
iii) update diagnosis-parameters based on acquired-iris-data. The controller is communicatively connected to the imaging-device and the database. The controller includes a processor and a memory storing executable instructions. The controller is defined to receive and process the captured digital-representations, segment captured digital-representations of irises into zones corresponding to an iridology-chart. Each zone represents a body-feature, analyze the segmented zones using a deep learning multimodal convolutional neural network (CNN) trained to detect symptomatic-diagnosis-parameters, compare the detected symptomatic-diagnosis-parameters with predefined and updated diagnosis-parameters stored in the database, determine a health-status based on a predefined-criterion, indicating symptomatic variations associated with healthy or unhealthy conditions and displaying on a display-screen.

-In one embodiment, the light-source is defined to emanate light on irises at an angle of 45 degrees.

-Typically, the imaging-device is defined to capture layers of irises in the digital-representations.

-In additional embodiment, the health-status includes a health-analysis and health-recommendations.

[007] The present disclosure also discloses a method for determining health-status through non-invasive symptomatic examination of irises, in accordance with one embodiment. The method includes:
• providing a system for determining health-status through non-invasive symptomatic examination of irises, defined with a light-source, an imaging-device, a database, a controller and a display-screen,
• illuminating the irises of a person undergoing examination using said imaging-device;
• capturing digital-representations of the irises using said imaging-device, wherein the digital-representations captures symptomatic features;
• storing the captured digital-representations in said database, wherein said database maintains:
i) predefined diagnosis parameters corresponding to symptomatic features of healthy and unhealthy irises, and
ii) updated diagnosis-parameters derived from acquired-iris-data;
• segmenting the captured digital-representations into zones based on an iridology-chart, each zone corresponding to a body-feature;
• analyzing the segmented zones using a deep learning multimodal convolutional neural network (CNN) trained to learn symptomatic diagnosis-parameters;
• comparing the analyzed symptomatic diagnosis-parameters with the predefined and updated diagnosis-parameters stored in the database;
• determining a health-status of the person based on a predefined criterion; and
• displaying, by the controller, the health-status on the display-screen.
-In one embodiment, the method includes illuminating performed at an angle of 45 degrees the irises.
-Typically, the method includes capturing layers of irises by the imaging-device.
-In one embodiment, the method includes providing the health-status with a health-analysis and health-recommendations.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[008] The present disclosure will now be described with the help of the accompanying drawings, in which:

Figure 1 illustrates a schematic perspective view of a system (100) for determining health-status (41) through non-invasive symptomatic examination of irises (05), in accordance with one embodiment of the present disclosure, defined with a light-source (10), an imaging-device (20), a database (30), a controller (40) and a display-screen (50);

Figure 2 illustrates a schematic view of the arrangement of the light-source (10) and the imaging-device (20) of the Figure 1;

Figure 3 illustrates a block diagram of the steps followed by the light-source (10) and the imaging-device (20) to capture irises (05);

Figure 4 illustrates a schematic representation of the digital-representation (21) in form of an image illustrating the captured left and right irises (05);

Figure 5 illustrates a schematic representation of the image with segmented size, typically of length of 1100 pixels and height of 1100 pixels;

Figure 6 illustrates a schematic representation of the iridology-chart (23);

Figure 7 illustrates a block diagram showing steps of processing by the controller (40) from receiving the digital-representations (21) till analysis of segmented zones (22); and

Figure 8 illustrates a representation of the digital-representations (21) inputted and the controller (40) processes to obtain zones (22) to input in deep learning multimodal convolutional neural network (CNN) (40c).

DETAILED DESCRIPTION OF THE INVENTION
[009] Referring now to the drawings, Figures 1 to 8, where the present invention is generally referred to with numeral (100), it can be observed that a system, in accordance with an embodiment, for determining health-status (41) through non-invasive symptomatic examination of irises (05) is provided which includes a light-source (10), an imaging-device (20), a database (30), a controller (40) and a display-screen (50).

[010] The light-source (10) is defined to illuminate the irises (05) of a person (06) undergoing examination for visibility of symptomatic-features. The light-source (10) operates with an optical-fiber-lighting-system that provides enhance visibility of symptomatic-features. In one embodiment, the light-source (10) is disposed such that light-rays fall on irises (05) at an angle of 45 degrees. In one embodiment, the optical fiber lighting system is defined with the light-source (10) emits light and is polarized/controlled by using a polarizer (10a) and the polarized light is directed to an optical-fiber-cable (OFC) (10b) to emanate at the angle of 45 degrees.

[011] The imaging-device (20) is defined to capture digital-representations (21) of the irises (05) of the person (06) undergoing examination. Herein, the digital-representation includes images and/or videos. Typically, the imaging-device (20) includes capturing flashes from high-resolution-camera (20a) integrated with a magnification lens (20b). The imaging-device (20) captures digital-representations of the left and right irises, revealing symptomatic details. The high-resolution-camera (20a) can be Sony Alpha 7C. In one embodiment, the imaging-device (20) is capable of capturing layers of the irises (05), thereby revealing intricate symptomatic details. For example, the imaging-device (20) can capture digital-representations (21) of three or more layers to capture iris information.

[012] The database (30) is communicatively connected to the imaging-device (20). The database (30) is defined to firstly store the captured digital-representations (21) of the imaging-device (20).
Secondly, the database (30) stores/maintains predefined diagnosis-parameters associated with symptomatic features of healthy and unhealthy irises. Typically, the database (30) is fed with a dataset of healthy irises and a dataset of unhealthy irises of other users or persons. Thirdly, the database (30) updates diagnosis-parameters based on acquired-iris-data and stores therewithin. The database (30) self-updates periodically when a threshold volume of data is acquired and hence is continuous learning and refinement.

[013] The controller (40) is communicatively connected to the imaging-device (20) and the database (30). As known in the art, the controller (40) includes a processor (40a) for processing information and a memory (40b) for storing executable instructions. The controller (40) is initially defined to receive and process the captured digital-representations (21). The controller (40) segments captured digital-representations (21) of irises into zones (22) corresponding to an iridology-chart (23). Each zone (22) representing a body-feature like kidney, thyroid, stomach, cardiovascular systems and the like other body-parts. The controller (40) analyze the segmented zones (22) using a deep learning multimodal convolutional neural network (CNN) (40c) trained to detect symptomatic diagnosis parameters (40d). The controller (40) compares the detected symptomatic diagnosis parameters (40d) with the predefined and updated diagnosis parameters stored in the database (30). The controller (40) determines a health-status (41) based on a predefined-criterion (40e), indicating healthy or unhealthy conditions. For example, the predefined-criterion (40e) can be, but not limited to, discoloration, lacunae, pigment variations, or structural anomalies. In one embodiment, the health-status (41) includes report which includes a health-analysis (41a) and health-recommendations (41b).

[014] The present disclosure also discloses a method for determining health-status (41) through non-invasive symptomatic examination of irises (05). To execute the best method the initial step is providing the system (100) for determining health-status through non-invasive symptomatic examination of irises (05), which is defined with the light-source (10), the imaging-device (20), the database (30), the controller (40) and the display-screen (50). The detailed description of the light-source (10), the imaging-device (20), the database (30), the controller (40) and the display-screen (50) is described in the above paragraphs and not repeated due to undesired repetition.

[015] The next step is illuminating the irises (05) of a person (06) undergoing examination using the light-source (10). The iris (05) is illuminated using the optical fiber lighting system, which projects light at a 45-degree angle to enhance visibility of symptomatic features. The optical fiber lighting system is defined with the light-source (10) emits light and is polarized/controlled by using a polarizer (10a) and the polarized light is directed to an optical-fiber-cable (OFC) (10b) to emanate at the angle of 45 degrees.

[016] The next step is capturing digital-representations (21) of the irises (05) using the imaging-device (20). The digital-representations captures symptomatic-features of irises (05). The imaging-device (20) includes the high-resolution camera (20a) with the magnification lens (20b) that facilitates to capture digital representations (21) of both left and right irises of the person undergoing examination. The imaging-device (20) is capable of capturing multiple layers (like three layers) of the irises, ensuring detailed symptomatic analysis.

[017] The next step is storing the captured digital-representations in the database (30). The database (30) also maintains predefined diagnosis parameters corresponding to symptomatic features of healthy and unhealthy irises. Further, the database (30) stores updated diagnosis-parameters derived from acquired-iris-data.

[018] In one embodiment, the captured digital-representations (21) undergo preprocessing steps, including noise reduction and contrast adjustment.

[019] The next step is segmenting the captured digital-representations (21) into zones (22) by mapping on the iridology-chart (23). Each zone corresponding to a body-feature like kidney, thyroid, stomach, cardiovascular systems and the like other body-parts.

[020] The next step is analyzing the segmented zones (22) using the deep learning multimodal convolutional neural network (CNN) (40c) trained to learn symptomatic diagnosis-parameters (40d).

[021] The next step is comparing the analyzed symptomatic diagnosis-parameters (40d) with the predefined and updated diagnosis-parameters stored in the database (30). The next step is determining the health-status (41) of the person (06) based on a predefined-criterion like discoloration, lacunae, pigment variations, or structural anomalies. In one embodiment, the health-status (41) includes health-analysis (41a) and health-recommendations (41b).

[022] Lastly, the controller (40) signals the health-status (41) on the display-screen (50), which are known in the art.

[023] Herein tabulated below are few examples of body-features and corresponding predefined-criterion (40e).
Body-Features Predefined-Criterion (40e)/pre-fed and updated diagnosis-parameters with symptomatic features/symptomatic-diagnosis-parameters (40d)
Stomach Acidity:
-Increased stomach acidity – Icy Blue or lighter coloured stomach ring zone indicates hyper-acidity
-Decreased stomach acidity – a darker coloured stomach ring zone indicates hypochlorhydria
Lymphatic system, including lymph nodes, spleen, thymus, bone marrow, tonsils, and lymphatic vessels Lymphatic Rosary - White cloud like spots around the edge of the iris indicates poor lymphatic circulation
Cholesterol Cholesterol Ring - Fairly solid white covering over the outer edge of the iris. Signifies elevated blood cholesterol and triglyceride levels
Deposits in body parts Drug deposits - Spots’ or ‘patches’ of a darker color, typically brownish. These are quite distinct and rather like isolated islands. Not connected to the underlying fibers. Signifies chemical toxicity acquired in the body
Pituitary Glands Dark Spots in Pineal Zone, Presence of Crypt or Lacunae, Orange / Red spots can be found
Thyroid Lacuna in Thyroid zone, Brownish Pigment (Translucent), Brownish wash
Kidney One sided lacuna in kidney zone, Small crypt, Yellow wash
Pancreas Orange Pigment or wash in Pancreas zone, Bridged Collarette or square collarette
Cardio Vascular Disease Asymmetric / Rhomboid lacuna in Heart Zone, Lacuna over aorta & heart zone, Prominent lacuna or structural defects in the heart and kidney regions
Gastric Fermentation Pupillary sphincter muscle with colour ranging from various shades of yellow and orange

[024] In accordance with an exemplary embodiment, considering a health-status of a female-person (06) was to be determined. Initially, the system (100) was fed with generic details (required to be published in the health-status - 41), like name of the patient/person – Anita Shah, age of the patient/person – 62 years, sex of the patient/person - Female and the geographic region of the patient/person – Mumbai, Maharashtra, India. The left and right irises (05) of the female-person (06) were captured to achieve the digital-representations (21), in form of images. For capturing the digital-representations (21), the beam incident of the light emanating the light-source (10) was set at an angle of 45 degrees. The imaging-device (20) captured the digital-representations (21) in presence of the light-source (20) emanating light at the angle of 45 degrees. The steps followed for capturing digital-representation (21) are iris-image-capture-start, illuminating the light-source (10) by controlling light-emitting-diode light of the light-source (10) through polarizer (10a), the imaging-device (20) with adjusted magnifying lens focus on irises (05), OFC carrying light beam from polarizer (10a) and the imaging-device (20) is merged and incident onto irises (05) at 45 degrees, capturing multiple digital-representations (21) by switching imaging-device (20), multiple irises digital-representations are captured in combination of polarizer (10a) and the imaging-device (20), the captured digital-representations are stored to the database (30) and accessed by the controller (40) for processing and herewith ends the steps of capturing-digital-representations (21).

[025] The controller (40), by using the processor (40a) and the memory (40b), receives and processes to filter the captured digital-representations (21). .

[026] In the next step the captured digital-representations (21) of irises (05) is segmented (in fixed geometry based on the iridology-chart - 23) into zones (22) corresponding to an iridology-chart (23). Each zone (22) representing the body-feature. More specifically, the left-iris (05) and the right-iris (05) are split in 13 zones each by convoluting with binary filter. After convoluting, there are total 26 split digital-representations (21) extracted. Thus, 26 binary filter of digital-representations (21) have been created based on the iridology chart (23) of Bernard Jenson by removal of noise in the digital-representations (21), detect edges, smoothing the digital-representations (21) and area measurement. Each extracted zone is cropped (typically, but not limited to achieve the size of 254 Pixel * 254 Pixel) with bounding box to exclude zero pixel. Cropped-zones (22) are processed through a known process of contrast adjustment and histogram equalization. The digital-representations (21) are then organized into 13 pairs for each detection-case. Each pair is then fed into separate dual-stream multimodal convolutional neural network (43) for detection and deep learning.

[027] In the deep learning multimodal convolutional neural network (CNN) (40c), the digital-representations (21) of the left-iris (05) and the right-iris (05) are fed as input. Using deep-learning-network built over RESNET architecture to obtain extracted-features. The extracted features are directed for concatenation to form fully connected layers to provide final prediction of the health-status (41).

[028] Thus, the segmented zones (22) for each body-feature (like thyroid, pancreas, pituitary glands, nerve-rings as illustrated) are processed in deep learning multimodal convolutional neural network (CNN) (40c) to achieve health prediction for each body part and provide a report of health-analysis (41a) and health-recommendations (41b).

[029] The report of health-analysis (41a) and health-recommendations (41b) includes general observations as depicted in table 1, health parameters requiring attention as depicted in table 2, satisfactory health parameters as depicted in table 3, recommendations as depicted in table 4. And health parameter requiring attention and detailed report.
GENERAL OBSERVATIONS
SR NO ASPECT CONTROLLER (40) ANALYSIS PREDICTION ACCURACY
1 IRIS COLOR The Iris is of mixed biliary type. It may be prone to digestive, hepatic (liver), gall bladder and blood sugar disturbances. 100%
2 IRIS BUILT Individual has poor capacity to hold nutrients and carry away metabolic waste from body. 100%
3 NERVE RINGS High Level of General Stress detected. It may require special attention and monitoring. 100%
Table 1 – General Observations

HEALTH PARAMETERS REQUIRING ATTENTION
SR NO ASPECT CONTROLLER (40) ANALYSIS PREDICTION ACCURACY
1 LYMPHATIC ROSARY NOT HEALTHY. Immunity needs critical monitoring and attention. Indl is likely to suffer constant colds, lymphatic swellings and fluid retention. 100%
2 CARDIOVASCULAR (CVD) NOT HEALTHY. Heart needs to be further investigated for Coronary Artery Diseases, Hypertension, etc. for detailed analysis. 98%
Table 2 – Health parameters requiring attention

SATISFACTORY HEALTH PARAMETERS
SR NO ASPECT CONTROLLER (40) ANALYSIS PREDICTION ACCURACY
1 STOMACH RINGS HEALTHY 91%
2 PITUTORY GLAND HEALTHY 100%
3 KIDNEY LYMPHATIC HEALTHY 100%
4 KIDNEY HYPER ACIDIC HEALTHY 100%
5 GASTRIC FERMENTATION HEALTHY 100%
6 PUPIL BORDER HEALTHY 100%
7 SODIUM / CHOLESTEROL RING HEALTHY 100%
8 SCURF RIM HEALTHY 100%
9 THYROID HEALTHY 100%
10 PANCREAS HEALTHY 100%
11 ARCUS SENILIS HEALTHY 100%
12 PSORA DRUG DEPOSIT HEALTHY 100%

Table 3 – Satisfactory health parameters

Sr. No. Recommendations
1 Avoid fried, greasy, and fatty foods.
2 Drink less alcohol.
3 Eat foods that are rich in minerals – leafy vegetables, nuts
4 Exercise regularly for 2 hours
5 Stay hydrated- Drink lots of water or fat-free milk instead of soda or other drinks with added sugars.

Table 4 – AI Recommendations based on health-status

[030] Thus, the system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) analyzes digital-representations (21) of irises (05) using deep-learning to enable real-time health assessment without requiring physical samples or invasive procedures. The system (100) and method for determining health-status through non-invasive symptomatic examination of irises (05) enables segmentation of digital-representations (21) of irises (05) into diagnostic zones (22) corresponding to an iridology chart (23) for a comprehensive health assessment. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) utilizes a deep learning multimodal convolutional neural network (CNN) (40c) to analyze symptomatic features, such as color gradation, shade variations, lacunae, pigment patterns, and structural defects, for accurate health diagnosis. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) stores and updates diagnosis parameters in a database, allowing continuous learning and refinement of symptomatic correlations based on acquired iris data. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) enhances diagnostic accuracy by capturing layers of the iris (05), thereby revealing deeper symptomatic details. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) provides an illumination mechanism by defining the light-source (10) to emanate light at an angle of 45 degrees, optimizing visibility of symptomatic features. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) that determines a comprehensive health-status (41), including health-analysis (41a) and health-recommendations (41b), to assist in preventive healthcare and early disease detection. The system (100) and method for determining health-status (41) through non-invasive symptomatic examination of irises (05) displays the determined health status on a display screen (50), providing users with a clear and accessible health report. The system (100) and method for determining health-status through non-invasive symptomatic examination of irises (05) reduces dependence on human expertise by eliminating the need for manual iridology examinations, making health diagnostics more widely accessible. The system (100) and method for determining health-status through non-invasive symptomatic examination of irises (05) enables faster and cost-effective health analysis compared to conventional medical tests, promoting preventive healthcare solutions.

[031] The foregoing description conveys the best understanding of the objectives and advantages of the present invention. Different embodiments, steps or alternatives may be made of the inventive concept of this invention. It is to be understood that all matter disclosed herein is to be interpreted merely as illustrative, and not in a limiting sense.

, Claims:We Claim:

1) A system (100) for determining health-status (41) through non-invasive symptomatic examination of irises (05), comprising:
Characterized by:
• a light-source (10) defined to illuminate the irises (05) of a person (06) undergoing examination for visibility of symptomatic-features;
• an imaging-device (20) defined to capture digital-representations (21) of the irises of the person (06) undergoing examination;
• a database (30) communicatively connected to said imaging-device (20), the database (30) defined to:
i) store the captured digital-representations (21) of said imaging-device (20);
ii) maintain predefined diagnosis-parameters associated with symptomatic features of healthy and unhealthy irises; and
iii) update diagnosis-parameters based on acquired-iris-data;
• a controller (40) communicatively connected to said imaging-device (20) and said database (30), said controller (40) comprising a processor (40a) and a memory (40b) storing executable instructions, the controller (40) defined to:
o receive and process the captured digital-representations (21);
o segment captured digital-representations (21) of irises into zones (22) corresponding to an iridology-chart (23), each zone (22) representing a body-feature;
o analyze the segmented zones (22) using a deep learning multimodal convolutional neural network (CNN) (40c) trained to detect symptomatic-diagnosis-parameters (40d);
o compare the detected symptomatic-diagnosis-parameters (40d) with predefined and updated diagnosis-parameters stored in the database (30); and
o determine a health-status (41) based on a predefined-criterion (40e), indicating symptomatic variations associated with healthy or unhealthy conditions and displaying on a display-screen (50).
2) The system (100) for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 1, wherein said light-source (10) defined to emanate light on irises (05) at an angle of 45 degrees.
3) The system (100) for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 1, wherein said imaging-device (20) defined to capture layers of irises (05) in the digital-representations (21).
4) The system (100) for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 1, wherein said health-status (41) includes a health-analysis (41a) and health-recommendations (41b).
5) A method for determining health-status (41) through non-invasive symptomatic examination of irises (05), comprising:
Characterized by:
• providing a system (100), for determining health-status through non-invasive symptomatic examination of irises (05), defined with a light-source (10), an imaging-device (20), a database (30), a controller (40) and a display-screen (50);
• illuminating the irises (05) of a person (06) undergoing examination using said imaging-device (20);
• capturing digital-representations (21) of the irises (05) using said imaging-device (20), wherein the digital-representations captures symptomatic features;
• storing the captured digital-representations in said database (30), wherein said database (30) maintains:
i) predefined diagnosis parameters corresponding to symptomatic features of healthy and unhealthy irises, and
ii) updated diagnosis-parameters derived from acquired-iris-data;
• segmenting the captured digital-representations (21) into zones (22) based on an iridology-chart (23), each zone corresponding to a body-feature;
• analyzing the segmented zones (22) using a deep learning multimodal convolutional neural network (CNN) (40c) trained to learn symptomatic diagnosis-parameters (40d);
• comparing the analyzed symptomatic diagnosis-parameters (40d) with the predefined and updated diagnosis-parameters stored in the database (30);
• determining a health-status (41) of the person (06) based on a predefined criterion; and
• displaying, by said controller (40), said health-status (41) on said display-screen (50).
6) The method for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 5, wherein the step of illuminating is performed at an angle of 45 degrees the irises (05).
7) The method for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 5, includes capturing layers of irises (05) by said imaging-device (20).
8) The method for determining health-status (41) through non-invasive symptomatic examination of irises (05) as claimed in claim 5, includes providing said health-status (41) with a health-analysis (41a) and health-recommendations (41b).