A medical information processing system according to an embodiment includes processing circuitry. The processing circuitry is configured to acquire information on an examination target, identify a reference image corresponding to the examination target on the basis of the information on the examination target, generate an edited image generated by editing the reference image, and transmit order information to which the edited image has been attached.

Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

A medical system includes a processor. The processor is configured to calculate a presence probability of a lesion in a not-yet-observed region inside a hollow organ of a patient, the not-yet-observed region being specified on the basis of an image that has been captured by an imaging sensor of an endoscope inside the hollow organ, and spatial disposition information of a distal end of an insertion part of the endoscope.

A medical image processing device a reference image that is a medical image with which boundary line information related to a boundary line that is a boundary between an abnormal region and a normal region and landmark information related to a landmark that is a characteristic structure of the subject are associated and a captured image that is the medical image captured in real time, detects the landmark from the captured image, calculates a ratio of match between the landmark included in the reference image and the landmark included in the captured image, estimates a correspondence relationship between the reference image and the captured image on the basis of the ratio of match and information regarding the landmarks included in the reference image and the captured image, and generates a superimposition image in which the boundary line associated with the reference image is superimposed on the captured image on the basis of the correspondence relationship.

A method for measuring objects in a digestive tract based on an imaging system is provided. The imaging system captures a detection image in the measurement stage. The depth distance z.sub.i from a target point P′ to a board is calculated, a correction factor is obtained according to the predicted brightness g.sup.−1(z.sub.i) of a reference point P in the detection image. The depth image z(x, y) from the actual position of each pixel to the board is calibrated by the correction factor. The scale r of each pixel is calculated according to the depth image z(x, y). The actual two-dimensional coordinates S.sub.i′ of each pixel are calculated by the scale r, and the actual three-dimensional coordinates (S.sub.i′, z(x, y)) of each pixel are obtained. The distance between any two pixels in the detection image or the area within any range are calculated.

Flow through tubular organs (e.g., the esophagus) is analyzed based on fluid mechanics analysis of medical images. Using computational fluid dynamics, a reduced-order model is constructed and implemented to predict flow rate and fluid pressure developed inside flexible tubular organs inside the body. As one non-limiting example, the constructed model can be applied to analyze esophageal transport using fluoroscopy image sequences to predict flow rate, pressure, esophagus wall stiffness, and active relaxation.

A method of data collection of stool data via a mobile device operable to enable monitoring of gastrointestinal function. A related method of long-term monitoring of patient gastrointestinal function, using one or more signal processing tools (e.g. machine learning algorithms) for automatically interpreting patient stool data, including real-time patient-assessments, in order to detect an adverse clinical event from patient stool data. A system for facilitating real-time monitoring the gastrointestinal function, the system comprising: a camera on a mobile device, a user interface that facilitates self-monitoring of stool characteristics, so as to create health-monitoring data; mobile device storage, server storage, and remote storage (with at least one communication link between them) for storing some or all of the health-monitoring data; and a processor for interpreting such health-monitoring data for clinical or other health-monitoring application.

An intraoral imaging apparatus, a medical apparatus, and a program capable of providing auxiliary data for determination regarding diseases having differences in intraoral findings are provided. The intraoral imaging apparatus includes: an imaging device that acquires an intraoral image; a light source that emits light to a subject of the imaging device; a storage apparatus that stores an algorithm for performing determination of a specific disease; and an arithmetic apparatus, in which the arithmetic apparatus executes: a determination process of determining a possibility of the predetermined disease based on the image and the algorithm; and an output process of outputting a result of the determination process.

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.

An oral cavity scanning device is provided in the invention. The oral cavity scanning device includes an image capturing unit, an IMU circuit and a processing unit. The image capturing unit obtains a first image and a second image. The IMU circuit obtains IMU information corresponding to the first image and the second image. The processing unit obtains a distance value between the first image and the second image. The processing unit uses a contour algorithm to obtain a first contour and a second contour. The processing unit obtains first sampling points according to the first contour and second sampling points according to the second contour. The processing unit uses a feature algorithm to find relative feature points between the first sampling points and the second sampling points. The processing unit uses a depth information algorithm to obtain the depth information of each feature point.