Methods for automated identification of vascular features are described. In some embodiments, one or more machine learning (ML)-based vascular classifiers are used, with their results being combined to with results of at least one other vascular classifier in order to produce the final results. Potentially advantages of this approach include the ability to combine certain strengths of ML classifiers with segmentation approaches based on more classical (formula-based) methods. These strengths may include particularly the identification of anatomically identified targets mixed within an image also showing similar looking but anatomically distinct targets.

A method for measuring a size change of a target lesion in an X-ray image is provided, including receiving a first X-ray image including the target lesion and a second X-ray image including the target lesion, calculating an occupancy of a region corresponding to the target lesion in criterion regions in each of the first X-ray image and the second X-ray image, and measuring a size change of the target lesion based on the calculated occupancies.

A computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, includes identifying (S130), from a temporal sequence of differential images, temporal sequences of a subset of sub-regions (120.sub.i,j) of the vasculature wherein contrast agent enters the sub-region, and the contrast agent subsequently leaves the sub-region; and inputting (S140) the identified temporal sequences of the subset into a neural network (130) trained to classify, from temporal sequences of angiographic images of the vasculature, a sub-region (120.sub.i,j) of the vasculature as including a vascular constriction (140).

In an embodiment, a method (100) is described. The method is a computer-implemented method. The method comprises receiving (102) imaging data of a region comprising a radiographic imaging apparatus couch; and an indication of a specified number, type and position of at least one couch accessory associated with use of the radiographic imaging apparatus couch by a subject. The method further comprises determining (104) a type and position of at least one target object in the region using an instance segmentation model for processing the imaging data. Determining the type of the at least one target object comprises determining whether or not the at least one target object is a type of couch accessory specified by the indication. The method further comprises comparing (106) the determined position of the at least one target object that is determined to be the type of couch accessory specified by the indication with the indicated specified position for the couch accessory. The method further comprises, in response to determining that the determined position of the at least one target object does not correspond to the indicated specified position for the couch accessory, indicating (108) that the couch accessory is incorrectly positioned.

A computer-implemented method of detecting and quantifying a spinal curve is disclosed herein. The method comprises obtaining an infrared radiometer camera, positioning the infrared radiometer camera for receiving thermal data for a spine of a subject, the camera being horizontally spaced about meters to about 3 meters from the spine, scanning at least a portion of the spine with the infrared radiometer camera to obtain the thermal data, analyzing the thermal data using machine learning software which uses a classification algorithm to determine the presence of the spinal curve, and calculating a first Cobb angle for the curve of the subject's spine. Corresponding systems and additional methods also are disclosed.

The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.

Systems for monitoring respiratory function of a person include a garment configured to be worn on a torso of the person. The garment includes one or more reference patterns positioned on the anterior torso of the person when the garment is worn. A camera is used to capture, at a first time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation, and to capture, at a second time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation. A computing device is used to transmit the images to a remote computing device for analysis.

The present invention provides, among other things, methods of identifying cancerous or pre-cancerous tissue including providing a first region of tissue from a subject, calculating a roughness exponent for the first region of tissue, and comparing the roughness exponent of the first region of tissue to 0.5, wherein a difference of less than 0.2 between the roughness exponent of the first region of tissue and 0.5 indicates that the tissue is cancerous or pre-cancerous. Additionally, the present invention provides methods including providing a first view of a region of tissue, providing a second view of a region of tissue, calculating a first fractal dimension for the first view of the region of tissue, and calculating a second fractal dimension for the second view of the region of tissue, wherein if the fractal dimension of at least one of the first fractal dimension and the second fractal dimension is in the fractal zone, the region of tissue is considered cancerous. Also provided are systems for performing these assessments.

A camera coupled to a processor is disclosed. The camera is configured to capture images of the subject. The processor is configured to amplify microscopic temporal variations between the images of the subject and generate a profile of at least one microscopic temporally detected physiological variation of the subject. The processor is further configured to compare the profile of the subject to a pre-existing first aggregate profile of a plurality of third-party subjects, said aggregate profile corresponding to the at least one microscopic temporally detected physiological variation of the third-party subjects, the aggregate third-party profile corresponding to a known state of the third-party subjects.

Devices, systems, computer program products and computer implemented methods are provided for dynamically assessing a moving object from a sequence of consecutive volumetric image frames of such object, which images are timely separated by a certain time interval, by: identifying in at least one image of the sequence the object of interest; segmenting the object to identify object contour; propagating the object contour as identified to other images of the sequence; and performing dynamic analysis of the object based on the object contour as propagated.