An endoscopic system includes a processor. The processor generates from a measured value of pressure in a lumen of a subject a first temporal change image of the pressure in the lumen, generates an observation image for observing an opened/closed state of a valve portion in the lumen of the subject in real time from an image pickup signal obtained by picking up an image of the opened/closed state of the valve portion in the lumen in real time using an endoscope, and generates a superposition image by superposing the first temporal change image and the observation image in a temporally synchronized manner.

A processor is configured to derive, with reference to a database in which a plurality of reference images are saved such that an interpretation result about an abnormal shadow included in each reference image of the plurality of reference images is associated with the reference image, a degree of similarity between a target image and each of the plurality of reference images; the processor is configured to analyze, for a similar reference image, among the plurality of reference images, for which the degree of similarity is greater than or equal to a predetermined threshold value, an interpretation result regarding the similar reference image to thereby derive progression information about an abnormal shadow included in the similar reference image; and the processor is configured to statistically analyze the progression information to thereby derive prediction information for predicting future progression of an abnormal shadow included in the target image.

A system is configured to obtain multiple images (83) of a plant and store a plurality of the multiple images of the plant with a grow protocol (71) for growing the plant. Each of the images is associated with a different capture moment. The system is further configured to select the grow protocol separately from the plant and render the plurality of images upon selection of the grow protocol. Each of the images is rendered along with one or more desired and/or measured conditions (74-76) of a growth stage (84). The growth stage corresponds to a capture moment of the respective image. The grow protocol comprises a plurality of growth stages and the one or more desired and/or measured conditions are included in the grow protocol.

The present disclosure deals with the quickening of MRI examinations. Subjects of the present disclosure are a method, a system, a computer program product, a use, a contrast agent for use and a kit.

A method of quantitatively evaluating a cognitive status and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive status and predict its future change based on the at least one feature obtained.

A neural network system leverages dual attention, specifically both spatial attention and channel attention, to jointly estimate heart rate and respiratory rate of a subject by processing images of the subject. A motion neural network receives images of the subject and estimates heart and breath rates of the subject using both spatial and channel domain attention masks to focus processing on particular feature data. An appearance neural network computes a spatial attention mask from the images of the subject and may indicate that features associated with the subject's face (as opposed to the subject's hair or shoulders) to accurately estimate the heart and/or breath rate. Channel-wise domain attention is learned during training and recalibrates channel-wise feature responses to select the most informative features for processing. The channel attention mask is learned during training and can be used for different subjects during deployment.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.

Methods and systems for identifying internal motion of a breast of a patient during an imaging procedure. The method may include compressing the breast of the patient in a mediolateral oblique (MLO) position. During compression of the breast, a first tomosynthesis MLO projection frame for a first angle with respect the breast is acquired and a second tomosynthesis MLO projection frame for a second angle with respect to the breast is acquired. Boundaries of the pectoral muscle are identified in the projection frames and boundary representations are generated. A difference between the first representation and the second representation is determined. A motion score is then generated based on at least the difference between the first representation and the second representation.

An effect of a treatment on an organ, e.g., a lung, is assessed by acquiring a first measurement for each of a plurality of regions of the organ, and then acquiring a second measurement for each of the plurality of regions of the organ, after acquisition of the first measurements. A regional change measurement is obtained for each of the plurality of regions of the organ based on the first measurement and the second measurement of the region. A treatment effect is then determined based the plurality of regional change measurements and treatment information of the treatment delivered to the organ.

The present disclosure provides methods and systems for predicting future disease states of a subject. An exemplary method comprises: (a) obtaining an image from an imaging device, wherein the image indicates a disease associated with the subject; (b) generating an annotation mask for a current disease state; (c) processing the image data and the annotation mask to generate a temporal sequence of images with corresponding predicted disease states; and (d) outputting the temporal sequence of images and disease analytics within a graphical user interface (GUI).