The Cleveland Clinic Foundation

Case Western Reserve University

Sunil K Srivastava

Justis P Ehlers

Yuankai K Tao

Anant Madabhushi

Prateek Prasanna

William J Dupps

Prabhakar R Bandaru

Scott John

Tyler Bodily

Abhijith Karkisaval Ganapati

Deependra Kumar Ban

Ratnesh Lal

Gennadi Glinskii

Careen Y Lowder

David Xu

Peter K Kaiser

Atsuro Uchida

Duriye Damla Sevgi

Sumit Sharma

Systems and methods are provided for provided for automated selection of UWFA images. A first set of images representing an early phase of a UWFA image acquisition and a second set of images representing a late phase of the UWFA image acquisition are received and segmented to provide a vascular structure map for each of the first set of images and the second set of images. An image quality metric is assigned to each of the first set of images and the second set of images from the vascular structure map associated with each image. An image of the first set of images having an extreme value for the image quality metric across the first set of images and an image of the second set of images having an extreme value for the image quality metric across the second set of images are selected.

Automated selection of ultra-widefield angiography images

Systems and methods are provided for fully automated quality assessment of ultra-widefield angiography images. A series of ultra-widefield angiography images of a retina of a patient are obtained and each of the series of ultra-widefield angiography images are provided to a neural network trained on a set of labeled images to generate a quality parameter for each of the series of ultra-widefield angiography images representing a quality of the image. Each of the set of labeled images are assigned to one of a plurality of classes representing image quality. A user interface provides an instruction to a user to obtain a new series of ultra-widefield angiography images of the retina of the patient if no image of the series of ultra-widefield angiography images has a quality parameter that meets a threshold value.

Automated quality assessment of ultra-widefield angiography images

Methods, systems and devices for detecting the presence of a pathogen, for example, a virus (e.g., SARS-CoV-2), or its molecular components, in health care-related samples and/or environmental samples are disclosed. An example system for improving detection of a pathogen includes biosensor device comprising a detection chip and at least one probe that specifically recognizes a pathogen, where the detection chip comprises a graphene field-effect transistor (FET) chip and the probe, which comprises an aptamer, specifically binds to a DNA, RNA, or protein associated with the pathogen.

Methods and devices for detecting a pathogen and its molecular components

Systems and methods are provided for evaluating an eye using retinal fluid volumes to provide a clinical parameter. An optical coherence tomography (OCT) image of an eye of a patient is obtained. The OCT image is segmented to produce a total retinal volume and one or both of a subretinal fluid volume and an intraretinal fluid volume for a region of interest within the eye. A metric is generated as a function of the total retinal volume and one or both of the subretinal fluid volume and the intraretinal fluid volume. A clinical parameter for the patient is determined from the metric. The determined clinical parameter is provided to a user at a display.

Predicting clinical parameters from fluid volumes determined from OCT imaging

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.

Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-VEGF therapy in diabetic macular edema

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.

Predicting response to anti-vascular endothelial growth factor therapy with computer-extracted morphology and spatial arrangement features of leakage patterns on baseline fluorescein angiography in diabetic macular edema

Systems and methods are provided for image-guided delivery of therapeutics to an eye. An optical coherence tomography (OCT) imager is configured to produce at least one OCT image of the eye. A therapeutic delivery system is configured to deliver a therapeutic to the eye through a distal end of a delivery mechanism. A system control is configured to determine a position of the distal end of the delivery mechanism and control the therapeutic delivery system according to at least the determined position.

Image-guided delivery of ophthalmic therapeutics

Systems and methods are provided for automated analysis of angiographic images. An angiographic imaging system is configured to capture a first image of a region of interest, representing a first time, and a second image of a region of interest, representing a second time. A registration component is configured to register the first image to the second image. A difference component is configured to generate a difference image from the first image and the second image. A pattern recognition component is configured to assign a clinical parameter to the region of interest from the difference image and at least one of the first image and the second image.

Automated analysis of angiographic images

Systems and methods are provided for evaluating an eye of a patient from a set of OCT data. A layer segmentation component is configured to identify and segment a plurality of retinal layers within the set of OCT data. The plurality of layers include a layer of interest. A mapping component is configured to generate at least one of an en face representation of the layer of interest and a three-dimensional reconstruction of the layer of interest from the segmented plurality of layers. A parameter generator is configured to derive at least one parameter, representing a thickness of the layer of interest, from the at least one of the en face representation of the layer of interest and the three-dimensional reconstruction of the layer of interest. A user interface is configured to provide the determined at least one parameter to a display.

Clinic evaluation via outer retinal layer assessment

Systems and methods are provided for evaluating an eye of a patient. A first imager interface receives a first image of at least a portion of the eye generated via a first imaging modality, and a second imager interface receives a second image of at least a portion of the eye generated via a second imaging modality. A first feature extractor extracts a first set of numerical features from the first image, with one feature representing a spatial extent of one of a tissue layer, a tissue structure, and a pathological feature. A second feature extractor extracts a second set of numerical features from the second image, with one feature representing one of a number and a location of vascular irregularities within the eye. A pattern recognition component evaluates the first plurality of features and the second plurality of features to assign a clinical parameter to the eye.

Automated clinical evaluation of the eye

An apparatus for selectively connecting an accessory item to a surgical microscope includes a center pivot. An adapter arm includes an adapter support having a substantially planar support body which extends laterally in an outboard direction from the center pivot and has laterally spaced inboard and outboard support regions separated by a laterally oriented centerline. The adapter arm is configured to accept at least a portion of the accessory item in a supporting relationship. An undermount adapter includes a substantially planar undermount body having laterally spaced inboard and outboard body regions separated by a laterally oriented centerline. A plurality of microscope attachment throughholes each throughhole extend through the undermount body. Each throughhole is configured to accept a fastener for securement of the undermount adapter to the surgical microscope. An arm receiver is configured to accept the adapter tongue of the adapter arm in a supporting relationship.

Apparatus for selectively connecting items

A surgical imaging system is provided. The surgical imaging system includes a surgical microscope and a telecentric optical coherence tomography scanning unit configured to scan a sample through at least one optical component associated with the surgical microscope.

Microscope-integrated OCT system with an electrically tunable focus

One aspect of the present disclosure relates to a system that can perform an automated analysis of ocular inflammation (e.g., based on a non-contact image of the eye), which can lead to determining score for the ocular inflammation. The system can include a memory to store computer-executable instructions; and a processor that executes the computer-executable instructions to at least: receive an image of a portion of an eye taken by a non-contact imaging modality; determine a diagnostic parameter for ocular inflammation from the image of the portion of the eye; score the ocular inflammation with a continuous value based on the diagnostic parameter; and output the score to a display device.

Systems and methods for analysis of ocular inflammation

Systems and methods are provided for volumetric analysis of pathologies. A segmentation component is configured to determine, for each of a series of images of a region of interest containing a pathological feature, a set of segmentation boundaries within the image representing a cross-section of the pathological feature. A mesh generation component is configured to link the sets of segmentation boundaries from adjacent images in the series of images to generate a polygonal mesh representing a volumetric reconstruction of the pathological feature. A volumetric measurement component is configured to calculate volumetric parameters from the volumetric reconstruction representing the pathological feature. A user interface is configured to provide the calculated volumetric parameters to an associated display.

Growing community of inventors

Shaker Hts., OH, United States of America

Sunil K Srivastava