There are provided a medical image processing device, an endoscope system, a medical image processing method, and a program which detect an optimal lesion region according to an in-vivo position of a captured image. Images at a plurality of in-vivo positions of a subject are acquired from medical equipment that sequentially captures and displays in real time the images; positional information indicating the in-vivo position of the acquired image is acquired; from among a plurality of region-of-interest detection units that detect a region of interest from an input image and correspond to the plurality of in-vivo positions, respectively, a region-of-interest detection unit corresponding to the position indicated by the positional information is selected; and the selected region-of-interest detection unit detects a region of interest from the acquired image.

A method for characterizing a state of an end effector of an ultrasonic device is disclosed. The ultrasonic device including an electromechanical ultrasonic system defined by a predetermined resonant frequency. The electromechanical ultrasonic system further including an ultrasonic transducer coupled to an ultrasonic blade. The method including applying, by an energy source, a power level to the ultrasonic transducer, measuring, by a control circuit coupled to a memory, an impedance value of the ultrasonic transducer, comparing, by the control circuit, the impedance value to a reference impedance value stored in the memory; classifying, by the control circuit, the impedance value based on the comparison; characterizing, by the control circuit, the state of the electromechanical ultrasonic system based on the classification of the impedance value; and adjusting, by the control circuit, the power level applied to the ultrasonic transducer based on the characterization of the state of the end effector.

In a case where a first specific scene recognized at a time of insertion of an endoscope is stored in a specific scene memory and a recognized scene recognized by a scene recognition unit is a scene on a side deeper than the first specific scene in a direction of movement of the endoscope, the recognized scene is output without being changed. In a case where the first specific scene is stored in the specific scene memory and the recognized scene is a scene on a side shallower than the first specific scene in the direction of movement of the endoscope, the recognized scene is changed and output.

A lip and cheek retractor for examining the oral cavity of a person, comprising: a U-shaped frame (1) comprising in its rear part on the right and on the left a support (8) and a mirror (3) being rotatable in the supports (8) of the frame (1)

Systems and methods are disclosed for medical devices which can operate within a person in connection a medical procedure. In aspects of the present disclosure, a system for assisting with a surgical procedure includes a swarm of medical devices sized to be wholly deployed within a surgical site of a patient where the swarm of medical devices is configured to operate concurrently within the patient to assist a surgeon to perform a surgical procedure in the patient. The swarm of medical devices includes a first medical device that includes an imaging system configured to capture a view of at least a portion of a surgical site and to communicate the captured view, and a second medical device that includes one or more of a retracting device, an irrigation device, a suction device, a clipping device, a therapy delivery device, or a cutting device.

A treatment system includes a treatment tool, an imaging apparatus, and a control device. The treatment tool is configured to perform treatment on a living tissue by applying treatment energy from an end effector to the living tissue based on supplied electric power. The imaging apparatus captures an image of the living tissue in a state in which the treatment energy is applied from the end effector to the living tissue. The control device includes a processor to control the imaging apparatus by acquiring the captured image, determining whether an abnormality has occurred in the end effector based on the acquired captured image, and executing an instruction to stop supply of the electric power to the treatment tool when determining that the abnormality has occurred in the end effector.

An image processing apparatus includes a processor. The processor receives endoscope images to be determined, in each of which an image of an inside of a body of a subject is picked up, determines whether or not an endoscope image for comparison concerning a predetermined inspection site, and the endoscope images to be determined are same or similar, determines whether or not an observation mode of each of the endoscope images to be determined is a condition suitable for automatic recording, and automatically records a predetermined number of the endoscope images to be determined in a recording unit, when the endoscope images to be determined and the endoscope image for comparison are determined to be the same or similar and when the observation mode is the condition suitable for automatic recording.

Described are colonoscopy systems and methods of using such systems. The colonoscopy systems may include an optical scanning system having at least one illuminator configured to produce spatially patterned light and solid light in at least one frame to illuminate tissue within the colon, and at least one camera configured to capture the at least one image of the illuminated tissue within the colon. Additionally, the optical scanning system may include at least one control system configured to construct at least one three dimensional point cloud representations of the tissue within the colon and detect at least one feature of interest using the at least one three dimensional point cloud and a pre-trained artificial intelligence engine.

The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to generate intraoperative fluorescence images of a subject (e.g., to aid a surgery, to aid diagnosis and treatment of diseases). The system can receive an intraoperative white light image of the subject, input the intraoperative white light image of the subject into a generator of a trained generative adversarial network (GAN) model trained. In some examples, the GAN model is trained using a plurality of training image pairs, and each training image pair comprises an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. The system can obtain, from the generator, the generated intraoperative fluorescence image of the subject and display, on a display, the generated intraoperative fluorescence image of the subject.

A medical endoscope that has a reusable portion to which either of two different single-use portions can be snapped in by hand to thereby form two different assembled endoscopes. When one of the single-use portions is assembled with the reusable portion, a motor in the reusable portion robotically rotates a cannula about a proximal end of the single use portion. When the other single-use portion is assembled with the reusable portion, the motor robotically angulates the distal end of the cannula. Another medical endoscope is single-use in its entirety and has motor-driven angulation of the cannula's distal end. Another has manually controlled angulation.