US-20260128177-A1 - SYSTEMS AND METHODS FOR PROCESSING OF FUNDUS IMAGES

US20260128177A1US 20260128177 A1US20260128177 A1US 20260128177A1US-20260128177-A1

Abstract

Systems and methods for determining one or more recommendations for management of wellbeing of an individual are disclosed. An indication of risk of chronic kidney disease (CKD) is determined by a deep learning model based on one or more fundus images. Recommendations for management of the individual's wellbeing are based at least in part on the determined indication of risk of CKD.

Inventors

Seyed Ehsan Vaghefi Rezaei
David Michael SQUIRRELL
Song Yang
Songyang An
Li Xie
Michael Vincent Carroll McConnell
Shima Mohammadi Moghadam

Assignees

Toku Eyes Limited

Dates

Publication Date: 20260507
Application Date: 20251103

Claims (17)

1 . A method of determining an indication of risk of chronic kidney disease (CKD) of an individual, comprising: determining an indication of risk of chronic kidney disease (CKD) by a deep learning model based on one or more fundus images.
2 . The method of claim 1 , wherein the deep learning model comprises a plurality of retinal predictor models, and the method comprises processing the one or more fundus images using the plurality of retinal predictor models and outputting at least one feature from each of the retinal predictor models.
3 . The method of claim 2 , wherein the plurality of retinal predictor models are configured to output at least two of the following features: Estimated Glomerular Filtration Rate (eGFR), Albumin-to-Creatinine Ratio (ACR), Systolic Blood Pressure (SBP), Retinopathy grade, and Maculopathy grade.
4 . The method of claim 3 , wherein the plurality of retinal predictor models are further configured to output at least one of the following features: race and/or ethnicity, Total Cholesterol, Retinal Age, Smoking status, and a cardiovascular disease (CVD) risk biomarker velocity.
5 . The method of claim 2 , wherein the deep learning model comprises at least one CKD risk prediction model, and the method comprises processing the features output by the plurality of retinal predictor models using the at least one CKD risk prediction model to determine the indication of risk of chronic kidney disease (CKD).
6 . The method of claim 5 , wherein the at least one CKD risk prediction model comprises one or more of: a KDIGO model, a Framingham CKD risk (CKD-F) model, Chronic Kidney Disease Prognosis Consortium (CKD PC) model, a Kidney Failure Risk Equation (KFRE), and a Framingham Risk Score (FRS) model.
7 . The method of claim 5 , wherein the at least one CKD risk prediction model comprises a plurality of CKD risk prediction models, and weighted outputs of the plurality of CKD risk prediction models contribute to determining the indication of risk of chronic kidney disease (CKD).
8 . The method of claim 5 , wherein the method comprises inputting at least one patient demographic into the at least one CKD risk prediction model, wherein the at least one patient demographic comprises one or more of: Age, Gender, diabetic status, race and/or ethnicity, smoking status, CVD history, and hypertension medications.
9 . A system comprising: a memory storing program instructions; and at least one processor configured to execute program instructions stored in the memory, wherein the program instructions cause the processor to perform a method of determining an indication of risk of chronic kidney disease (CKD) of an individual, comprising: determining an indication of risk of chronic kidney disease (CKD) by a deep learning model based on one or more fundus images.
10 . The system of claim 9 , wherein the deep learning model comprises a plurality of retinal predictor models, and the at least one processor is further configured to process the one or more fundus images using the plurality of retinal predictor models and output at least one feature from each of the retinal predictor models.
11 . The system of claim 10 , wherein the plurality of retinal predictor models are configured to output at least two of the following features: Estimated Glomerular Filtration Rate (eGFR), Albumin-to-Creatinine Ratio (ACR), Systolic Blood Pressure (SBP), Retinopathy grade, and Maculopathy grade.
12 . The system of claim 11 , wherein the plurality of retinal predictor models are further configured to output at least one of the following features: race and/or ethnicity, Total Cholesterol, Retinal Age, Smoking status, and a cardiovascular disease (CVD) risk biomarker velocity.
13 . The system of claim 10 , wherein the deep learning model comprises at least one CKD risk prediction model, and the at least one processor is further configured to process the features output by the plurality of retinal predictor models using the at least one CKD risk prediction model to determine the indication of risk of chronic kidney disease (CKD).
14 . The system of claim 13 , wherein the at least one CKD risk prediction model comprises one or more of: a KDIGO model, a Framingham CKD risk (CKD-F) model, Chronic Kidney Disease Prognosis Consortium (CKD PC) model, a Kidney Failure Risk Equation (KFRE), and a Framingham Risk Score (FRS) model.
15 . The system of claim 13 , wherein the at least one CKD risk prediction model comprises a plurality of CKD risk prediction models, and weighted outputs of the plurality of CKD risk prediction models contribute to determining the indication of risk of chronic kidney disease (CKD).
16 . The system of claim 13 , wherein the at least one processor is configured to input at least one patient demographic into the at least one CKD risk prediction model, wherein the at least one patient demographic comprises one or more of: Age, Gender, diabetic status, race and/or ethnicity, smoking status, CVD history, and hypertension medications.
17 . A computer program product, the computer program product comprising: a non-transitory computer-readable medium having computer-readable program code stored thereon, the computer-readable program code comprising instructions that when executed by a processor, cause the processor to perform a method of determining an indication of risk of chronic kidney disease (CKD) of an individual, comprising: determining an indication of risk of chronic kidney disease (CKD) by a deep learning model based on one or more fundus images.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to U.S. patent application No. 63/715,370, filed Nov. 1, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The present technology relates to systems and methods for processing fundus images, more particularly the processing of fundus images to determine a risk of chronic kidney disease. BACKGROUND Chronic kidney disease (CKD) is well recognized as a major life-threatening and debilitating disease. CKD, when left untreated, has a high risk of progressing to kidney failure requiring dialysis or kidney transplantation. In addition, CKD can be an indicator of and contributor to multiple cardiovascular and metabolic diseases, including type 2 diabetes (DM), hypertension (HTN), atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), and arrhythmia (both atrial fibrillation and sudden death). The critical role played by CKD in the development of multiple chronic health conditions is exemplified by the cardiovascular-kidney-metabolic syndrome, as recently highlighted by the American Heart Association. Impaired kidney function is increasingly recognized as a key mediator of the relations between metabolic risk factors, such as obesity and DM, and cardiovascular disease, including heart failure (HF). DM and HTN can lead to CKD, and CKD can lead to HTN. The role of CKD in patients with DM is particularly profound. Per data from the National Health and Nutrition Examination Survey (NHANES) linked to the National Death Index, CKD was present in 42% of patients with DM. Furthermore, their mortality was substantially increased when CKD also present, with the excess mortality reaching 47% in diabetic individuals with both albuminuria and impaired GFR. Kidney disease is defined as an abnormality of kidney structure or function, which can resolve or become chronic. CKD is a general term for disorders affecting kidney structure and function with variable severity, clinical presentation, and rate of progression. CKD affects about 15% of the U.S. population, but 9 out of 10 people with CKD are unaware that they have the condition. CKD is more prevalent in Black persons and women and among persons 60 years and older, with more advanced disease associated with an increased risk of cardiovascular disease and death. Earlier stages of CKD are typically asymptomatic and progression to more advanced stages can be prevented or delayed. Progression of CKD can lead to kidney failure, which often requires treatment by dialysis or transplantation. Dialysis and/or kidney transplantation are among the most costly of chronic diseases (in terms of both burden and expense) and significantly reduce lifespan. Their costs consume a disproportionate amount of healthcare budgets. The Medicare program spends more than $130 billion-over 24 percent of total spending-on patients with kidney disease. Treatment for kidney failure and its complications represents approximately 7% of the Medicare budget, for less than 0.1% of the population. Failure to recognize CKD results in more severe complications, and late referral, results in worse outcomes even with therapy. Therefore, identification of people at earlier time points in the trajectory of CKD, with appropriate management and earlier referral of those who would benefit from specialist kidney services, should lead to both economic and clinical benefits. Early detection of CKD also allows for more optimal dialysis starts, which are defined as initial therapy with a permanent vascular or peritoneal access or a preemptive transplant. Optimal starts are associated with a 56% reduction in mortality and a 65% reduction in sepsis. Optimal starts were also associated with lower utilization including a 55% reduction in inpatient days. Thus, earlier detection of CKD and referral to nephrology resulted in lower morbidity and mortality associated with dialysis initiation as well as lower utilization of healthcare resources. The most widely used CKD risk staging system is the “heat map” from KDIGO (Kidney Disease: Improving Global Outcomes) based on estimated glomerular filtration rate (eGFR) and level of albuminuria. This KDIGO guidance is also supported by the National Kidney Foundation-Kidney Disease Outcomes Quality Initiative (NKF-KDOQI). The NKF/American Society of Nephrology (ASN) Task Force has provided updated guidance on estimating GFR, recommending updated equations that remove race from the calculation and indicating that combining filtration markers creatinine and cystatin C is more accurate and supports better clinical decisions. Albuminuria is typically quantified by a urine albumin/creatinine ratio (UACR). Importantly, high levels of proteinuria are associated with an increased risk of disease progression, even if the eGFR is normal. Thus, NKF and ASN recommend measuring both eGFR and UACR for evaluating CKD risk in patients with risk factors, including DM, HTN, and cardiovascula