A non-transitory recording medium recoding a program that causes a computer to execute processing includes: recording operation field images obtained by chronologically shooting an operation field under endoscopic surgery; determining presence or absence of a predetermined or larger amount of bleeding based on the operation field images; and playing back partial images according to a set playback mode in a time range from a time before a start of bleeding to the time after the bleeding among the recorded operation field images, when it is determined that the predetermined or larger amount of bleeding is present.

A method for evaluating image data of a patient showing a target region to be treated with an embolizing agent includes providing a three-dimensional time-resolved image data set of a vascular system portion of the patient. A structural parameter that describes a geometry of at least the vascular system portion and/or a basic information item including dynamic parameters that describe hemodynamics in the vascular system portion is established from the image data set by an analysis algorithm. An embolization information item describing a plurality of embolizing agents that are to be used is provided. An actuation information item describing a suitable composition of the plurality of embolizing agents, for an intervention facility used for carrying out the treatment is established by an establishing algorithm that uses the basic information item and the embolization information item, and the actuation information item is provided to the intervention facility.

A method for training a machine learning algorithm that classifies predictive regions-of interest (“ROI”) of progression of idiopathic pulmonary fibrosis. The method includes acquiring a set of computed tomography (CT) images of a plurality of patients and selecting a plurality of ROIs within the set of images. Each of the ROIs designates a label that indicates progression of pulmonary fibrosis and training a machine learning algorithm by inputting the plurality of ROIs and the associated labels into the algorithm. The algorithm identifies the ROIs in the set of images as indicating regions of pulmonary fibrosis within the set of images based on the features.

A program causes a computer to execute processing including: acquiring an endoscope image captured by an endoscope; inputting the acquired endoscope image into a plurality of learning models learned so as to output diagnosis support information regarding a lesion included in the endoscope image; acquiring a plurality of pieces of diagnosis support information output from each of the learning models; and outputting a plurality of pieces of the acquired diagnosis support information and information regarding each of the learning models in association with each other. Alternatively, the program causes the computer to execute the processing of inputting the acquired endoscope image into one learning model, executing a plurality of determination logics to acquire a plurality of pieces of output diagnosis support information, and outputting a plurality of pieces of the acquired diagnosis support information and information regarding each of the learning models in association with each other is executed.

Disclosed is a pain assessment method using a deep learning model, the pain assessment method including operations of receiving, by an analysis device, an image indicating activity in a specific brain area of a subject animal and allowing the analysis device to input images of regions of interest in the image into a neural network model and assess the pain of the subject animal according to a result output by the neural network model.

The present disclosure may provide a method for perfusion analysis. The method may include: obtaining a plurality of scan images corresponding to a plurality of time points; obtaining a plurality of time-density discrete points based on the plurality of scan images; determining an initial time-density curve based on the plurality of time-density discrete points, the initial time-density curve indicating a density variation of a contrast agent in an organ or tissue over time, the organ or tissue corresponding to a pixel or voxel in the plurality of scan images; obtaining a first perfusion model; determining a first perfusion parameter based on the first perfusion model and the initial time-density curve; obtaining a second perfusion model; and determining a second perfusion parameter based on the second perfusion model and the first perfusion parameter.

A nuclear medicine diagnosis apparatus according to an embodiment includes a processing circuit. The processing circuit is configured to obtain nuclear medicine data; to time-divide the nuclear medicine data into at least first nuclear medicine data and second nuclear medicine data; and to identify a biological accumulation region, on the basis of a temporal change in data values included in the first nuclear medicine data and the second nuclear medicine data.

Instruments and methods for wide-field polarized imaging of the skin to determine an outer lesion margin objectively in vivo to provide guidance to a surgeon. Quantitative characterization of collagen structures in the skin can be used to determine the outer lesion margin or monitor skin treatment.

A machine learning device includes: a generation unit generating a first shape model representing a shape of an object before deformation and a second shape model representing a shape of the object after the deformation based on measurement data before and after the deformation; and a learning unit learning a feature amount including a difference value between each micro region and another micro region that constitute the first shape model, and a relation providing a displacement from the each micro region of the first shape model to each corresponding micro region of the second shape model.

Provided is a method for processing a medical image. In the method, a processing result is acquired by processing, without acquiring a user operation, an acquired medical image of a target object, and subsequently, the processing result is output in response to acquiring a user operation triggered by a user for viewing a processed medical image.