The present invention teaches a method of predicting the potential for manifestation of various medical conditions by analyzing human placenta. The method contemplates and includes determining the need for early monitoring, intervention or potential treatment for medical conditions likely to manifest as a child grows older and investigating the potential for various medical conditions. The method includes selecting and identifying a sample of the placenta to analyze by computer applied mathematical algorithms and preparing the placental sample to be analyzed by various procedures. Then the placental sample is captured by obtaining a three-dimensional digital image of the chorionic surface of the placental sample by use of a selected capturing device. The digital image is corrected for errors inherent in digital image acquisition and the resultant image data is loaded into a computer for analysis. The computer performs an analysis on the corrected digital image data using one or more algorithms to determine the vascular structure of the placenta, and the resultant data is interpreted and analyzed to determine the potential for manifestation of various medical conditions.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Relationships between morphological changes to an eye due to intraocular pressure changes and blood perfusion changes in the retina are determined by colocalizing retinal perfusion data and optic nerve head (ONH) mechanical deformation data. Perfusion changes from intraocular pressure (IOP) changes are determined by colocalizing retinal perfusion data with ONH mechanical deformation data. Optical coherence tomography-angiography (OCT-A) can be used to generate both retinal perfusion data and mechanical deformation data for an imaged volume. A three-dimensional model (e.g., connectivity map or connectivity model) of the vasculature can be generated from the OCT-A imaging data and used to predict changes in blood perfusion in various areas of the retina due to IOP-induced mechanical deformations.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Systems and methods for detecting and classifying retinal microaneurysms. The method including: receiving a time sequence of fluorescein angiography input images; generating a binary map of hyperfluorescent elements in the input images; determining which hyperfluorescent elements in the binary map are microaneurysms, by grading each against a combination of morphological metrics; classifying each of the detected microaneurysms as leaky or not leaky, the classification having: identifying an outer ring mask surrounding the detected microaneurysm in the binary map; identifying parenchyma in the outer ring mask using a fluorescence intensity determination; determining a rate of change of fluorescence intensity of the identified parenchyma over time; and classifying the detected microaneurysm as leaky where the rate of change is positive and not leaky where the rate of change is negative or zero; and outputting the classifications of the detected microaneurysms.

A computer-implemented method for automatically performing a longitudinal review of medical imaging data via one or more processors includes obtaining, at a computing device, a first image volume acquired of a subject with a medical imaging system of an imaging modality. The method also includes obtaining, at the computing device, a second image volume acquired of the subject with the medical imaging system or another medical imaging system of the imaging modality or a different imaging modality, wherein the first image volume was acquired at an earlier time point than the second image volume. The method further includes automatically aligning, via the computing device, the second image volume to the first image volume to generate aligned image volumes.

According to embodiment, an image processing apparatus comprising a specifying unit and a display controller. The specifying unit that specifies an acquisition position of an indicator relating to blood flow on a blood vessel-containing image collected by a medical image diagnostic apparatus. The display controller that displays the acquisition position on the blood vessel-containing image and displays the indicator on a display unit in association with the acquisition position.

Systems and methods are disclosed for assessing a risk of disease. One method includes obtaining an anatomic model associated with a target anatomy; modeling, using a processor, an injection of one or more virtual contrast agents into the anatomic model; performing a simulation of flow of blood and the one or more virtual contrast agents through the anatomic model; and computing one or more characteristics of concentration associated with the one or more virtual contrast agents at one or more locations in the anatomic model based on the simulation.

Methods for characterizing fluids from a patient. A time series of images of a conduit are received, and a conduit image region in the images is identified. The conduit image region may include a shallow section and/or a deep section. The shallow section includes a dimension of the conduit along an optical axis of the camera such that opacity of the fluids from redness associated with red blood cells or hemoglobin is limited to permit pixel color visualization in the images. The deep section includes a dimension is along the optical axis such that the redness provides a minimum opacity to permit the pixel color visualization. A concentration of a blood component of the fluids is determined based on the pixel color visualization. A volume of blood passing through the conduit is estimated based on the estimated concentration of the blood component and an estimated volumetric flow rate.