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.

A steerable endoscope is provided with active steering control. An endoscope includes a flexible tubular body with first and second articulating segments, and a camera. In an embodiment, the endoscope includes an orientation sensor. A controller for the endoscope performs an automated analysis of an alignment between the motion axis of the endoscope and the viewing axis of the camera, and actively steers the endoscope to improve the alignment.

An inspection analysis method includes an analysis step, a prediction step, and an output step. A processor analyzes inspection information related to an operation in an inspection and generates first operation data and second operation data indicating a content of the operation in the analysis step. The first operation data indicate a content of the operation in a first inspection. The second operation data indicate a content of the operation in a second inspection performed after the first inspection is completed. The processor compares the first operation data and the second operation data with each other and predicts a state of an inspection target in the prediction step. The processor outputs state information indicating the state to a reporting device in the output step.

Methods for evaluating micrometastases in a tissue region of a subject are described. The methods include administering to the subject a detectably effective amount of a tumor-targeted photoactivatable immunoconjugate; allowing a sufficient amount of time for the tumor-targeted photoactivatable immunoconjugate to enter micrometastases in the tissue region; illuminating the tumor-targeted photoactivatable immunoconjugate; obtaining an image of the tissue region of the subject using a fluorescent imaging device, and evaluating the micrometastases in the tissue region by conducting algorithmic analysis of the image. Methods of treating micrometastases in a tissue region of a subject are also described.

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 image acquisition unit sequentially acquires a medical image. A recognition processing unit applies a recognition process to the medical image. A recognition result display determination unit determines to display or not display the result of the recognition process on a display according to blur in the medical image. A display control unit controls the display of the recognition process according to the determination by the recognition result display determination unit.

A special observation image is obtained by performing an image pickup of an object to be observed illuminated with special light. Bs-image signals of the special observation image are assigned to brightness signals Y to generate a first observation image for display. Gs-image signals of the special observation image are assigned to brightness signals Y to generate a second observation image for display. The first observation image for display and the second observation image for display are automatically switched and displayed on a monitor.

A medical imaging device, comprising a illumination unit, can be configured to illuminate a tissue area with light from a first spectral range, comprising a range of visible wavelengths, and with light from a second spectral range, which is different from the first spectral range, an imaging unit, configured to detect light from the first spectral range and to generate a first image of the tissue area on the basis of the detected light from the first spectral range and furthermore configured to detect light from the second spectral range and to generate a second image of the tissue area on the basis of the detected light from the second spectral range, and a superposition unit, configured to generate a superimposed image on the basis of the first and second image in such a way that, in the superimposed image, on the basis of a visual highlighting, it is possible to identify whether and where the tissue area comprises highlight regions which are characterized by an increased emission of light from the second spectral range in comparison to other regions of the tissue area. The document furthermore relates to a method for image-based support for a medical intervention, and to a use of an imaging device in such a method.

An endoscope observation assistance apparatus includes an image information acquiring part to acquire and display a captured image of a luminal organ captured by the endoscope device on a display, a lesion information acquisition part to detect a predetermined lesion based on the captured image and to acquire lesion information regarding the lesion, a lesion disappearance determination part to track the lesion based on the captured image and the lesion information and to determine whether or not the lesion has disappeared from the captured image, and a notification part to notify a determination result when the lesion disappearance determination part determines that the lesion has disappeared from the captured image. Alternatively, the notification part may notify a lesion detection when a short time period passes after the lesion detection by the lesion information acquisition part.