A diagnosis support system having a processor configured to acquire medical images; detect a medicine and/or equipment used when the medical images are captured, from the medical images by image recognition; detect a region of interest from the medical images by image recognition; assign, to the medical image from which the medicine and/or equipment is detected, first detection information indicating the detected medicine and/or equipment; and assign, to the medical image from which the region of interest is detected, second detection information indicating the detected region of interest, display, on a display device, the medical images in a list in a display form according to the first detection information and the second detection information.

An image capturing apparatus sets reference values for a plurality of evaluation indexes and captures images of affected regions for the evaluation indexes, based on user's operation. An image processing apparatus analyzes the captured images and determines the affected region(s) for the evaluation index(es) exceeding the associated reference value(s) set by the user. The image capturing apparatus causes a display unit to highlight the affected region(s) for the evaluation index(es) exceeding the associated reference value(s) and superposes (displays) the affected region(s) on the image of an affected region.

A medical image management apparatus comprising a first hardware processor that: acquires a first video obtained by radiographing a cyclic motion of a subject; acquires a second video obtained by radiographing a cyclic motion of a subject separately from the radiographing of the first video; extracts a first feature that is a feature of a cyclic change of the acquired first video; extracts a second feature that is a feature of a cyclic change of the acquired second video; adjusts at least one of the first video and the second video so that a cycle of change approaches the cycle of change of the other video, based on the extracted first feature and second feature; and outputs the acquired first video or the adjusted first video, and the acquired second video or the adjusted second video.

An eye disease diagnosis method using artificial intelligence may include: collecting, from a database, a first eyeground image of a myopic patient who is not diagnosed with an eye disease and a second eyeground image of a myopic patient who has been diagnosed with the eye disease; learning eyeball change information by degree of myopia based on the first eyeground image, using deep learning; comparing and analyzing the first eyeground image and the second eyeground image based on the eyeball change information by the degree of myopia, and learning eyeball change information by the eye disease using deep learning; and estimating determination criteria of an eyeground image for diagnosis of the eye disease, based on a difference between the eyeball change information by the degree of myopia and the eyeball change information by the eye disease.

A system for determining accuracy of a surgical procedure to implant an implant on a patient bone. The system including at least one computing device configured to perform the following steps. Receive preoperative patient data including preoperative images of the patient bone and planned implant position and orientation data. Receive postoperative patient data including postoperative images of the patient bone and an implant implanted on the patient bone. Segment the patient bone and the implant from the postoperative images of the patient bone and the implant. Register separately the patient bone and the implant from the postoperative images to the patient bone from the preoperative images. And compare an implanted position and orientation of the implant from the postoperative images relative to the patient bone from the preoperative images to the planned implant position and orientation data relative to the patient bone from the preoperative images.

A set of pre-operative images may be captured of an anatomical structure using an endoscopic camera. Each captured image is associated with a position and orientation of the camera at the moment of capture using image guided surgery (IGS) techniques. This image data and position data may be used to create a navigation map of captured images. During a surgical procedure on the anatomical structure, a real-time endoscopic view may be captured and displayed to a surgeon. The IGS navigation system may determine the position and orientation of the real-time image; and select an appropriate pre-operative image from the navigation map to display to the surgeon in addition to the real-time image.

The present invention relates to the field of medical monitoring, and, in particular, to non-contact detecting and monitoring of patient breathing. Systems, methods, and computer readable media are described for calculating a change in depth of a region of interest (ROI) on a patient and assigning one or more visual indicators to at least a portion of a graphic based on the calculated changes in depth and/or based on a tidal volume signal generated for the patient. In some embodiments, the systems, methods, and/or computer readable media can display the visual indicators overlaid onto at least the portion in real-time and/or can display the tidal volume signal in real-time. The systems, methods, and/or computer readable media can trigger an alert and/or an alarm when a breathing abnormality is detected.

An image registration method includes acquiring first image data for a target object that includes first coordinate information; acquiring second image data for the target object that includes second coordinate information, by using a probe; and registering the first image data with the second image data, using the first coordinate information and the second coordinate information. According to the image registration method, image registration between a plurality of pieces of volume data adjusted so that their coordinate axes correspond to each other is performed, whereby a high-quality registered image may be quickly and simply obtained.

A system and method for displaying images of internal anatomy includes an image processing device configured to provide high resolution images of the surgical field from low resolution scans during the procedure. The image processing device digitally manipulates a previously-obtained high resolution baseline image to produce many representative images based on permutations of movement of the baseline image. During the procedure a representative image is selected having an acceptable degree of correlation to the new low resolution image. The selected representative image and the new image are merged to provide a higher resolution image of the surgical field. The image processing device is also configured to provide interactive movement of the displayed image based on movement of the imaging device, and to permit placement of annotations on the displayed image to facilitate communication between the radiology technician and the surgeon.

Systems and methods for super-resolution ultrasound imaging of microvessels in a subject are described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. The region-of-interest may include, for instance, microvessels or other microvascuiature in the subject. By isolating, localizing, tracking, and accumulating the microbubbles in the ultrasound data, super-resolution images of the microvessels can be generated.