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EP-4734820-A1 - SYSTEM AND METHOD FOR DETECTING DIABETIC RETINOPATHY

EP4734820A1EP 4734820 A1EP4734820 A1EP 4734820A1EP-4734820-A1

Abstract

Approaches for detecting presence of diabetic retinopathy (DR) in a patient's eye retinal image, are descried. In an example, the presence of referable DR is detected based on certain characteristics of eye indicating presence DR. To detect the presence of referable DR, a first detection model and a second detection model are trained based on a training information to predict presence of DR in an input eye image. Once trained, the first detection model and the second detection model may be used for ascertaining presence of referable DR in the input eye image.

Inventors

  • SAVOY, Florian Mickael
  • RAO PARTHASARATHY, Divya
  • SOSALE, Bhargav

Assignees

  • Medios Technologies Pte Ltd

Dates

Publication Date
20260506
Application Date
20240730

Claims (15)

  1. 1 . A system comprising: a processor; and a detection engine coupled to the processor, wherein the detection engine is to: generate a confidence score by operating a first detection model based on a processed information, wherein the processed information is extracted from a processed image of a patient’s eye; generate a reliance score by operating a second detection model based on an unprocessed information, wherein the unprocessed information is extracted from an unprocessed image of the patient’s eye; compare the confidence score and the reliance score individually with a predefined value to ascertain whether one of confidence score and the reliance score is greater than the predefined value; and on ascertaining one of the scores being greater than the predefined value, designate the patient’s eye as diabetic retinopathy positive eye.
  2. 2. The system as claimed in claim 1 , wherein the detection engine is to: obtain an input eye image from a repository, wherein the repository comprises a plurality of input eye images for diagnosing diabetic retinopathy; or capture, via a camera module, an input eye image of the patient’s eye, wherein the patient is under screening for diagnosing diabetic retinopathy; execute a quality assessment test on the input eye image to ascertain its quality; on ascertaining the quality of the retinal image as acceptable, determine field of view of the retinal image; and on determining the field of view as one of disc centered and macula centered, process the input eye image to crop black border to obtain a cropped input eye image; and execute a pre-processing step on the cropped input eye image to obtain the processed image.
  3. 3. The system as claimed in claim 1 and 2, wherein the processed image is pre-processed using the pre-processing step and the unprocessed image is not pre-processed, wherein the DR detection engine to obtain the processed image using the pre-processing step is to: process the cropped input eye image to subtract background color to obtain the processed image.
  4. 4. The system as claimed in claim 2, on ascertaining the quality of the retinal image as non-acceptable or on determining the field of view is not one of disc centered and macula centered, the detection engine is to: generate a warning prompt to be displayed on a display device to the patient indicating need to recapture the input eye image.
  5. 5. The system as claimed in claim 1 , wherein on ascertaining none of the scores being greater than the predefined value, the detection engine is to: designate the patient’s eye as diabetic retinopathy positive when following condition satisfies: x*(reliance score) + y*(confidence score) -z > 0 wherein x, y, and z represents any real number value except ‘0’.
  6. 6. The system as claimed in claim 1 , wherein on ascertaining none of the scores being greater than the predefined value, the detection engine is to: designate the patient’s eye as diabetic retinopathy negative when following condition satisfies: x*(reliance score) + y*(confidence score) -z <= 0 wherein x, y, and z represents any real number value except ‘0’.
  7. 7. The system as claimed in claim 1 , wherein while operating the first detection model based on the processed information to generate the confidence score and operating the second detection model based on the unprocessed information to generate the reliance score, the detection engine is to: derive the processed information from the processed image and the unprocessed information from the unprocessed image using a feature extraction technique, wherein the processed information and the unprocessed information comprises an attribute value corresponding to a plurality of eye characteristics; process the attribute values comprised in the processed information based on the first detection model to assign a weight for each of the plurality of eye characteristics to generate a first weighted information; process the attribute values comprised in the unprocessed information based on the second detection model to assign a weight for each of the plurality of eye characteristics to generate a second weighted information; generate, based on the first weighted information, the confidence score and the reliance score based on the second weighted information.
  8. 8. The system as claimed in claim 7, wherein the plurality of eye characteristics comprises size, location, and color of different types of lesions present in the retinal image, wherein the types of lesions comprises one of microaneurysms, retinal hemorrhages, soft exudates, hard exudates, in- traretinal microvascular anomalies, neovascularization, fibrous proliferation, and preretinal and vitreous hemorrhage, tractional retinal detachment, or combination thereof.
  9. 9. A method comprising: obtaining a training information comprising a processed training image and an unprocessed training image of a patient’s eye; deriving a processed training information corresponding to the processed training image and an unprocessed training information corresponding to the unprocessed training image; training a first detection model based on the processed training information; training a second detection model based on the unprocessed training information; wherein the first detection model, when trained, is to generate a confidence score and the second detection model, when trained, is to generate a reliance score.
  10. 10. The method as claimed in claim 9, wherein the processed training information and the unprocessed training information comprises a plurality of training eye characteristics with corresponding training attribute value, wherein the plurality of training eye characteristics comprises size, location, and color of different types of lesions present in the retinal image, wherein the types of lesions comprises one of microaneurysms, retinal hemorrhages, soft exudates, hard exudates, intraretinal microvascular anomalies, neovascularization, fibrous proliferation, and preretinal and vitreous hemorrhage, tractional retinal detachment or combination thereof..
  11. 11. The method as claimed in claim 9 to 10, wherein the training a first detection model based on the processed training information comprises: classifying the training attribute values corresponding to the processed training information as a first processed training eye characteristic, wherein the first processed training eye characteristic belongs to a first range of attribute values of the training eye characteristics; and associating the first processed training eye characteristic with a first confidence score based on the training attribute values corresponding to the processed training information.
  12. 12. The method as claimed in claim 9 to 10, wherein the training a second detection model based on the unprocessed training information comprises: classifying the training attribute values corresponding to the unprocessed training information as a first unprocessed training eye characteristic, wherein the first unprocessed training eye characteristic belongs to a first range of attribute values of the training eye characteristics; and associating the first unprocessed training eye characteristic with a first reliance score based on the training attribute values corresponding to the unprocessed training information.
  13. 13. The method as claimed in claim 8, further comprising: obtaining a subsequent processed training image comprised in a subsequent training information; ascertaining whether training attribute values corresponding to the subsequent processed training image lies in the range of training attribute values of first processed training eye characteristic; on ascertaining training attribute values does not correspond to first processed training eye characteristics, classifying the training attribute values corresponding to the subsequent processed training image as a second processed training eye characteristic in the first detection model which belongs to a second range of attribute values; and associating second processed training eye characteristic with a second confidence score based on the training attribute values.
  14. 14. The method as claimed in claim 9 and 13, further comprising: obtaining a subsequent unprocessed training image comprised in the subsequent training information; ascertaining whether training attribute values corresponding to the subsequent unprocessed training image lies in the range of training attribute values of first unprocessed training eye characteristics; on ascertaining training attribute values does not correspond to first unprocessed training eye characteristics, classifying the training attribute values corresponding to the subsequent unprocessed training image as a second unprocessed training eye characteristics in the second detection model which belongs to a second range of attribute values; and associating second unprocessed training eye characteristic with a second reliance score based on the training attribute values.
  15. 15. The method as claimed in claim 9, wherein the processed training image is obtained by performing a number of pre-processing steps, wherein the pre-processing steps comprises: processing a training retinal image to crop black border to obtain a cropped image; and processing the cropped image to subtract background color to obtain the processed training image.

Description

SYSTEM AND METHOD FOR DETECTING DIABETIC RETINOPATHY BACKGROUND [001] Eye is an organ whose function is to collect light from its surrounding and transform it into signals interpretable by the brain. Generally, an eye includes various components which enables the proper functioning, namely, cornea, iris, pupil, ciliary muscle, vitreous, retina, etc. Amongst these, the function of the retina is to transform incoming light from surrounding into electrical signals interpretable by the brain. There are some eye vision disorders or diseases which affects proper functioning of the retina of the eye. One of them is diabetic retinopathy which damages blood vessels in the retina resulting in swelling and leaking the blood vessels causing blurry vision, fluctuating vision, dark or empty areas in vision, or even vision loss. Generally, diabetic retinopathy is diagnosed by examining the retina of the eyes to detect the presence of numerous lesions, such as microneurysms, hemorrhages, soft and hard exudates and many more. However, none of the existing tests provide automatic detection of diabetic retinopathy with accurate results for large scale screening of population at an affordable cost. BRIEF DESCRIPTION OF FIGURES [002] Systems and/or methods, in accordance with examples of the present subject matter are now described and with reference to the accompanying figures, in which: [003] FIG. 1 illustrates a training system for training a first detection model and a second detection model enabling them to detect diabetic retinopathy, as per an example; [004] FIG. 2 illustrates a detection system for determining presence of diabetic retinopathy in an input eye image, as per another example; [005] FIG. 3 illustrates a method for training a first detection model and a second detection model, as per an example; and [006] FIG. 4A-4B illustrates a method for determining presence of referable DR in an input eye image, based on a trained first and second detection model, as per an example. DETAILED DESCRIPTION [007] Eyes are a sensory organ capable of reacting to visible light and allowing this light to be converted to brain interpretable signals. Brain utilizes such signals for various purposes including seeing things, keeping balance, etc. Amongst other features or components of the eyes, retina at the back of the eyes is one of the important components which causes the photoreceptors to turn the light into electrical signals on illumination with light. These electrical signals then travel from the retina through the optic nerve to the brain for further processing. Such electric signals are then processed by the brain to create a visual feed or perception of surrounding objects which we see as images or videos. [008] An individual may suffer from different eye vision disorders. Examples of such vision disorder may include, but are not limited to, blurred vision (refractive errors), age-related macular degeneration, glaucoma, cataract, diabetic retinopathy, etc. One such visual disorder is diabetic retinopathy, an eye condition that can cause vision loss and blindness in people who have diabetes. Diabetic retinopathy (DR) affects blood vessels in the retina and may involve the growth of abnormal blood vessels in the retina. Generally, diabetic retinopathy causes blurry vision, fluctuating vision, dark or empty areas in vision, or even vision loss. [009] Eye affected with diabetic retinopathy generally encompasses different types of lesions formed on the retina. Examples of such lesions include, but may not be limited to, microaneurysms, retinal hemorrhages, soft exudates, hard exudates, intraretinal microvascular anomalies (IRMA), neovascularization, fibrous proliferation, and preretinal and vitreous hemorrhage, tractional retinal detachment. DR is generally caused by high blood sugar persisted for long time due to diabetes. The damage caused by DR is irreversible, however, with proper and timely diagnosis may help in preventing progression of DR. [0010] Conventionally, DR is best diagnosed with a comprehensive dilated eye exam in which drops placed in the patient’s eye widen its pupils to allow a medical practitioner to have a better view inside the eyes of the patient. Other ancillary diagnosis processes include fluorescein angiography, optical coherence tomography (OCT), etc. However, the above disclosed techniques or other diagnostic methods require either a specialist medical practitioner or expensive equipment. As may be understood, presence of such specialized medical practitioner and equipment is limited to tertiary level health care centre which are far away from the reach of rural population, which is highest in India. To perform screening of large population at an affordable cost, there is a need for a system which performs automatic detection of DR having an on-the-edge operable configuration to reduce cost and time of operation of such system. [0011] Approaches for detecting presence of diabetic retinopathy (DR