A self-adaptive multi-scale respiratory monitoring method based on a camera, relates to the technical field of video image signal identification processing, in order to solve the defect that the local optimal respiratory signal and the global optimal respiratory signal cannot be acquired by single image scale, a method was provided: (1) acquiring a respiratory monitoring object in real time;(2) performing multi-scale regular pre-segmentation on a video image, performing local respiratory signal identification and extraction on each unit area pre-segmented under each scale respectively, and defining the unit area with local respiratory signal output as a target area; and (3) comparing local respiratory signals extracted from the target area pre-segmented under each scale, determining an optimal segmentation scale, and taking a local respiratory signal extracted from the target area under the optimal segmentation scale as a monitoring respiratory signal output. The reliability is improved, and intelligent monitoring is realized.

According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.

The present disclosure provides methods and an apparatus for imaging and analysing images of presumed protein deposits in the retina, retinal tissue or retinal structures and discloses methods differentiating or classifying these deposits and other optical signals from retinal structures into 1) whether they contain or do not contain classes, of proteins or protein deposits called amyloids or other proteins and/or protein deposits related to neurodegenerative eye and brain disease(s); 2) which type(s) of amyloid or other proteins or protein deposits they contain, as well as 3) whether the form and/or properties of the deposit are associated with a class of diseases or with one or another specific condition(s) (or disease(s)); whether or not this is a disease or class of disease associated with the retina or more generally with the nervous system, including the brain or 4) classified as associated with one or another level of severity of condition(s), or disease(s).

An endoscopic image viewing support server includes an endoscopic image acquiring unit configured to acquire imaging information, a section-of-interest setting unit configured to estimate a degree of interest and classify a plurality of endoscopic images in accordance with the degree of interest, and an endoscopic image selecting unit configured to select an endoscopic image from each of sections of interest at a ratio based on the degree of interest. The section-of-interest setting unit is configured to determine an endoscopic image of interest from among the plurality of endoscopic images, and in a case where the plurality of endoscopic images are arranged in a chronological order, estimate the degree of interest for an endoscopic image in an endoscopic image group including the endoscopic image of interest by using image processing.

The present disclosure provides a system and method for image data acquisition. The method may include acquiring physiological data of a subject. The physiological data may correspond to a motion of the subject over time. The method may include obtaining a trained machine learning model configured to detect feature data represented in the physiological data. The method may include determining, based on the physiological data, an output result of the trained machine learning model that is generated based on the feature data. The method may include acquiring, based on the output result, image data of the subject using an imaging device.

Systems and methods for predicting a patient response to various agents and/or combinations of agents using ex vivo dosing and imaging are disclosed. In one example, a method of determining treatment efficacy includes analyzing a solid cell culture over time, e.g., first and second responses to a solid cell culture to respective treatments may be compared to determine a treatment efficacy of each treatment. Systems and methods for applying the treatments to the cell culture and analyzing the cell culture and efficacy are disclosed.

A method for training a neural network intended to analyze a dental situation of an updated patient. A historical learning database is created that relates to a dental body and to a spatial attribute associated with the dental body. The historical learning database includes more than 1,000 historical records, with each historical record relating to a respective historical patient. Each record including a set of historical images all depicting the dental body in the historical patient, called “historical dental body” and an item of spatial information including, for the historical patient, a set of values for the spatial attribute, called “historical spatial information.” The neural network is trained, by providing it with the sets of historical images as input and with the historical spatial information as output, with the spatial attribute defining an ordered sequence of variables in a three-dimensional reference frame.

Disclosed herein are methods and apparatus for making a determination about a cataract in an eye in ambient lighting conditions.

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

A biological information detection device includes: a video capture unit, a blood flow analysis unit, a local pulse wave detection unit, a pulse wave propagation velocity calculation unit, and a blood pressure estimation unit. The video capture unit obtains video information on a face of a living body. The blood flow analysis unit analyzes video data of at least three skin areas in the video information, as blood flow information. The local pulse wave detection unit is provided for each skin area to calculate pulse information based on the blood flow information sequenced chronologically. The pulse wave propagation velocity calculation unit calculates a pulse wave propagation velocity based on a phase difference between pieces of the pulse information at each skin area calculated by the local pulse wave detection unit. The blood pressure estimation unit estimates blood pressure based on the pulse wave propagation velocity.