Described herein are computer-implemented methods for analyzing an input image of a mouth region from a user to provide information regarding a disease or condition of the mouth region, a computing device configured to receive the input images from a user; and a trained machine learning system. In some embodiments, the computing device is configured to transmit an oral health score to the user.

The image processing apparatus acquires a plurality of types of candidate images based on an endoscope image, performs control of displaying, on a display, a display image based on at least one type of candidate image, performs a first analysis process on one or the plurality of types of candidate images set in advance, selects at least one type of candidate image from the plurality of types of candidate images as an optimum image based on a first analysis process result obtained through the first analysis process, and obtains a second analysis process result by performing a second analysis process on the optimum image.

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section (41) that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section (42) that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section (44) that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.

A diagnostic assistance apparatus according to an aspect of the present disclosure determines whether a body part of a target examinee captured in a target medical image is normal, by using a trained first classification model generated by unsupervised learning using a plurality of first learning medical images of normal cases and a trained second classification model generated by supervised learning using a plurality of learning data sets including normal cases and abnormal cases.

A method and system for detecting spinal stenosis is provided. The method may receive image data corresponding to a spine region of a patient. The method may also identify a spinal cord in the image data. The method may determine at least one compression of the spinal cord and may mark an anatomical element proximate to a location of the determined at least one compression to yield at least one marking. The method may generate a decompression plan based on the at least one marking.

A CPU acquires a distance image or a visible light image captured by a TOF camera or a visible light camera that has, as an imageable region, a region including an irradiation region which is a space in which a breast of a subject imaged by a mammography apparatus is irradiated with radiation emitted from a radiation source and detects whether or not a foreign object other than an object to be imaged is present in the irradiation region on the basis of the distance image or the visible light image.

A computer-implemented system and method for predicting female sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.

The invention relates to a method in the preparation of samples for microscopic examination onto which a coverslip is applied. The method is notable for the fact that the coverslipping quality is checked automatically and at least partly optically. The invention further relates to an apparatus for carrying out the method, and to an apparatus for checking the coverslipping quality of samples onto which a coverslip is applied.

A method for collection and dissemination of biologic data, comprising collecting at least one biologic sample by a testing device including thereon an alignment target and including a plurality of immunoassay test strips, wherein the at least one biologic sample contacts a sample pad on at least one of the plurality of immunoassay test strips, assigning correlative values as test results, wherein each test performed on the biologic sample is assigned a different correlative value, receiving the test results at a server disposed on a network, wherein the server has configured thereon a database, assigning a unique identification to the biologic sample, storing the unique identification in the database, storing the test results in the database in association with the unique identification of the biologic sample, and providing access to the database to healthcare organizations for analysis of the test results.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.