Provided is an information processing device that performs display control to display a vessel image obtained by imaging a vessel in which a cell is seeded on a display and includes at least one processor. The processor acquires two or more vessel images obtained by imaging the same vessel at different dates and times and displays the acquired two or more vessel images on the display such that different imaging dates and times are distinguishable.

The system for cardiovascular parameter data quality determination can include a user device and a computing system, wherein the user device can include one or more sensors, the computing system, and/or any suitable components. The computing system can optionally include a data quality module, a cardiovascular parameter module, a storage module, and/or any suitable modules. The method for cardiovascular parameter data quality determination can include acquiring data and determining a quality of the data. The method can optionally include processing the data, and/or determining a cardiovascular parameter, training a data quality module, any suitable steps.

The present disclosure is related to systems and methods for radiation. The method may include obtaining a plurality of reference images of a target of a subject and reference physiological motion information of the subject. The plurality of reference images and the reference physiological motion information may be acquired in a radiation period. The method may include establishing a correlation model based on the plurality of reference images and the reference physiological motion information. The method may include monitoring real-time motion information of the target based on the correlation model during a radiation operation performed during the radiation period.

Disclosed are systems and methods for characterizing corneal shape abnormalities. These methods may be used to differentiate corneas having subclinical keratoconus from other conditions which cause distortion of corneal shape, including warpage of the cornea due to contact lens wear. Also disclosed is classification scheme to aid diagnosis of corneal conditions and thereby guide clinical decision making regarding patient treatment. This classification scheme is based on computed properties of corneal shape, is amenable to automation, and may be implemented in an integrated system or provided in the form of software encoded on a computer-readable medium.

A tongue-image-based diagnostic system and a tongue-image-based diagnostic method are disclosed. The diagnostic system includes a parameter collector configured to acquire environmental parameter information; a model establishment circuitry configured to perform a training process using image training data and the environmental parameter information and establish an estimation model; and an analysis circuitry configured to analyze acquired image information using the estimation model, and generate an analysis result corresponding to the acquired image information.

A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.

A system for monitoring vital signs includes: an imaging device for acquiring video image files of a living individual; a data analysis system including a processor and memory; a computer program running in the data analysis system to automatically analyze the video images, autonomously identify an area in the images where periodic movements associated with a selected vital sign may be detected and quantified; and, an interface that outputs an electrical signal corresponding to the waveform of the selected vital sign. The system may include a Graphical User Interface, which may display a visual graph of the waveform and a single video frame or a video stream of the individual.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires a first index value obtained based on fluid analysis that is performed based on an image including a blood vessel of a subject, the first index value being related to blood flow at each of positions in the blood vessel. The processing circuitry acquires external information including a second index value related to blood flow at each of the positions in the blood vessel. The processing circuitry changes one of an arrangement direction of index values in a first graph and an arrangement direction of index values in a second graph in accordance with the other one of the arrangement directions. The processing circuitry displays the first graph and the second graph on a display unit such that the arrangement directions of the index values match each other.

A computer-implemented method for providing a cardiac motion classification based on Cardiac Magnetic Resonance (CMR) image data, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t), acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the heart and for a plurality of times, t, the method including: a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the heart; a movement feature extraction and classification step of, for each of the masks and the corresponding anatomic layers, extracting a movement feature of the heart and classifying the movement feature into one of a number of pre-determined movement features; an associating step of associating the classified movement feature with the corresponding layer for the cardiac motion classification.