An endoscopic image observation system supports the observation of a plurality of images captured by a capsule endoscope. The endoscopic image observation system includes a distinguishing unit that outputs an accuracy score indicating the likelihood that each of the plurality of images represents an image of a region sought to be distinguished; a grouping unit that groups the plurality of images into a plurality of clusters in accordance with the accuracy score; and an identification unit that identifies a candidate image for a boundary of the region from among the plurality of images in accordance with the grouping into the plurality of clusters.

Provided is a medical arm device that avoids interference with a surgeon.

A medical arm device includes a first arm part including a tip part that supports a medical instrument such as an endoscope, and a second arm part that supports the first arm part, and includes at least two horizontal rotation axes and an inter-axis distance changing unit that changes a distance between the two horizontal rotation axes. The tip part includes a structure in which joint members corresponding to rotation axes of three degrees of freedom that determine an attitude of the medical instrument are directly connected, and a rotation axis around a longitudinal axis of the medical instrument, a yaw axis in a case where the rotation axis around the longitudinal axis is a roll axis, and a pitch axis are disposed in order from a most tip part.

At the time of automatic selection of a key image from among a large number of medical examination images, a medical imaging apparatus compares information extracted based on a sample image created in advance with information of an examination image, and performs common category determination and similarity degree determination. As a result of the comparison, the examination image selected to be close to the sample image is set as the key image, and storage processing is performed. The stored key image can be used for medical reports.

A portable endoscopic system comprising an imaging unit for an endoscopic procedure. The imaging unit has an imaging coupler for receiving imaging information from an imaging assembly of an endoscope; a display integrated into a housing of the imaging unit; an image processing unit for processing the received imaging information into images of a time series and to displaying the image in real-time; a motion sensor configured to detect a motion of the housing; and a detection processing unit. The detection processing unit is configured to classify at least one anatomical feature in each image of the time series based on an artificial intelligence classifier; determine a confidence metric of the classification; determine a motion vector based on the detected motion; and display, concurrently with the corresponding image, the classification of the at least one anatomical feature, the determined confidence metric, and the determined motion vector.

Improved fluorescent imaging and other sensor data imaging processes, including hyperspectral imaging, devices, and systems are provided to enhance endoscopes with multiple wavelength capabilities and providing sequential imaging and display. A first optical device is provided for endoscopy imaging in a white light and a fluoresced light mode with an imaging unit including one or more image sensors. A mechanism in the first optical device to automatically adjust the focus of the first optical device using one or more deformable, variable-focus lenses, wherein the automatic focus adjustment compensates for a chromatic focal difference between the light collected at distinct wavelength bands caused by the dispersive or diffractive properties of the optical materials or optical design employed in the construction of the first or second optical devices, or both. Further variable spectrum imaging is enhanced with the use of adjustable spectral filters.

An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Various embodiments of the present disclosure encompass manipulative endoscopic guidance device employing an endoscopic viewing controller (20) for controlling a display of an endoscopic view (11) of an anatomical structure, and a manipulative guidance controller (30) for controlling a display of one or more guided manipulation anchors (50-52) within the display of the endoscopic view (11) of the anatomical structure. A guided manipulation anchor (50-52) is representative of a location marking and/or a motion directive of a guided manipulation of the anatomical structure (e.g., grasping, pulling, pushing, sliding, reorienting, tilting, removing, or repositioning of the anatomical structure). The manipulative guidance controller (30) may generate an anchor by analyzing a correlation of the endoscopic view (11) of the anatomical structure with a knowledge base of image(s), model(s) and/or details corresponding to the anatomical structure and by deriving the anchor based on a degree of correlation of the endoscopic view (11) of the anatomical structure with the knowledge base.

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.

An assist device includes: a processor configured to generate a fluorescence image based on an imaging signal that is generated by capturing an image of fluorescence caused by excitation light that is applied to a living tissue, calculate a fluorescence intensity based on the fluorescence image, based on the fluorescence intensity, estimate a placement period during which a medical tool is placed in a lumen, and output placement period information on the placement period and an observation image obtained by capturing an image of the living tissue.

Some of the embodiments of the present disclosure are directed to a dental measurement method comprising: measuring an orientation of a dental measurement device includes a user interface; and displaying information on said user interface based on said orientation.