METHOD AND SYSTEM FOR THE DETECTION OF CEREBRAL AMYLOID ANGIOPATHY
A method and system detects cerebral amyloid angiopathy (CAA) condition of a subject by capturing multiple images of the subject's retina, processing the images to detect any retinal hemorrhage, retinal vessel tortuosity, and retinal edema, to predict the likelihood of CAA.
1 . A method for detecting cerebral amyloid angiopathy (CAA) in a subject, comprising:
capturing multiple autofluorescence images of the subject's retina;
analyzing the images to detect and quantify perivascular amyloid accumulation along retinal blood vessels; and
determining a likelihood of CAA based on the detected perivascular amyloid accumulation.
2 . The method of claim 1 , further comprising:
creating a standardized region of interest using a registration function and detection of optic nerve head and fovea.
3 . The method of claim 1 , further comprising:
applying an image quality filter to eliminate low quality images.
4 . The method of claim 1 , further comprising:
detecting retinal hemorrhage and vessel tortuosity.
5 . The method of claim 1 , further comprising:
detecting retinal edema using optical coherence tomography (OCT).
6 . A method for detecting a cerebral amyloid angiopathy (CAA) condition of a subject, comprising:
i. capturing multiple images of a retina of the subject;
ii. creating a standardized region of interest of the multiple images using a registration function, image quality assessment, and detection of optic nerve head (ONH) and fovea;
iii. applying an image filter and blink detector to eliminate images that are of low image quality;
iv. performing background correction on the images;
V. applying a vessel detection algorithm on the images;
vi. applying a probability density function (PDF) fit of the retina and segmentation of retinal auto fluorescence;
vii detecting any retinal hemorrhage and retinal vessel tortuosity;
viii. detecting any retinal edema by optical coherence tomography (OCT)
ix. using individual elements and a combined data vector to predict the likelihood of CAA of the subject.
7 . The method of claim 6 , further including using the individual elements and combined data vector to predict the likelihood of Amyloid Related Imaging Abnormalities (ARIA-E and ARIA-H).
8 . The method of claim 6 , wherein the capturing multiple images of a retina captures images in blue auto fluorescence (AF), green AF, color, infrared (IR), and OCT.
9 . The method of claim 6 , wherein the step of applying an image filter and blink detector eliminates images of low image quality due to cataracts and lid obstructions.
10 . The method of claim 6 , wherein the steps a-h are performed in a camera which captures multiple images.
11 . The method of claim 6 , wherein step a is performed using an Optos wide field retinal imaging device.
12 . The method of claim 6 , further including the step of quantifying amyloid along blood vessels.
13 . The method of claim 6 , wherein step a is performed using a Center Vue Eidon or Heidelberg Spectralis device.
14 . A system for detecting cerebral amyloid angiopathy (CAA) in a subject, comprising:
An image capture device for capturing multiple autofluorescence images of the subject's retina;
A processor for analyzing the images to detect and quantify perivascular amyloid accumulation along retinal blood vessels, and determining a likelihood of CAA based on the detected perivascular amyloid accumulation.
15 . The system of claim 14 , wherein the processor:
creates a standardized region of interest using a registration function and detection of optic nerve head and fovea.
16 . The system of claim 14 , further comprising:
an image quality filter to eliminate low quality images.
17 . The system of claim 14 , wherein the processor:
detects retinal hemorrhage and vessel tortuosity.
18 . The system of claim 14 , wherein the processor:
detects retinal edema using optical coherence tomography (OCT).
19 . A system for detecting a cerebral amyloid angiopathy (CAA) condition of a subject, comprising:
an image capture device to capture multiple images of a retina of the subject;
a processor for:
i. creating a standardized region of interest of the multiple images using a registration function, image quality assessment, and detection of optic nerve head (ONH) and fovea;
ii. filtering the images and detecting blink to eliminate images that are of low image quality;
iii. performing background correction on the images;
iv. applying a vessel detection algorithm on the images;
v. applying a probability density function (PDF) fit of the retina and segmentation of retinal auto fluorescence; and
vi. detecting any retinal hemorrhage and retinal vessel tortuosity; and
an optical coherence tomography (OCT) device for detecting any retinal edema; and
wherein the processor uses individual elements and a combined data vector to predict the likelihood of CAA of the subject.
20 . The system of claim 19 , wherein the processor uses individual elements and combined data vector to predict the likelihood of Amyloid Related Imaging Abnormalities (ARIA-E and ARIA-H).
21 . The system of claim 19 , wherein the image capture device captures multiple images of a retina captures images in blue auto fluorescence (AF), green AF, color, infrared (IR), and OCT.
22 . The system of claim 19 , wherein the processor eliminates images of low image quality due to cataracts and lid obstructions.
23 . The system of claim 19 , wherein the processor is part of a camera which captures multiple images.
24 . The system of claim 19 , wherein the image capture device is an Optos wide field retinal imaging device.
25 . The system of claim 19 , wherein the processor quantifies amyloid along blood vessels.
26 . The system of claim 19 , wherein the image capture device is a Center Vue Eidon or Heidelberg Spectralis device.