Provided are: an image diagnosis assistance apparatus capable of assisting diagnosis of an endoscopic image captured by an endoscopist; a data collection method; an image diagnosis assistance method; and an image diagnosis assistance program. The image diagnosis assistance apparatus is provided with: a lesion assessment unit that assesses, by a convolutional neural network, the denomination and the position of a lesion which is present in a digestive system endoscopic image of a patient captured by a digestive system endoscopic imaging device and information about accuracies thereof; and a display control unit that performs control for generating an analysis result image in which the denomination and the position of the lesion and the accuracies thereof are displayed and for displaying the image on the digestive system endoscopic image.

There are provided a medical image processing device, and endoscope system, a diagnosis support method, and a program which can support diagnosis by avoiding an inappropriate report in a case where any of site information of an observation target and lesion type information detected from a medical image is incorrect. The medical image processing device includes at least one processor. The at least one processor acquires the medical image, acquires the site information indicating a site of an observation target included in the medical image, in a human body, detects a lesion from the medical image to acquire the lesion type information indicating a lesion type, determines presence or absence of a contradiction between the site information and the lesion type information, and decides a report mode of the site information and the lesion type information on the basis of a determination result.

There are provided a medical image processing device, and endoscope system, a diagnosis support method, and a program which can support diagnosis by avoiding an inappropriate report in a case where any of site information of an observation target and lesion type information detected from a medical image is incorrect. The medical image processing device includes at least one processor. The at least one processor acquires the medical image, acquires the site information indicating a site of an observation target included in the medical image, in a human body, detects a lesion from the medical image to acquire the lesion type information indicating a lesion type, determines presence or absence of a contradiction between the site information and the lesion type information, and decides a report mode of the site information and the lesion type information on the basis of a determination result.

An endoscopic treatment tool, includes an elongated member having a distal end and a proximal end; a braid disposed between the distal end and the proximal end of the elongated member; a distal indicator disposed on the elongated member between a distal end and a proximal end of the braid, the distal indicator extending along a longitudinal axis of the elongated member; a proximal indicator disposed between the distal end and the proximal end of the braid on the elongated member at a more proximal side of the elongated member than the distal indicator, the proximal indicator extending along the longitudinal axis; and a pre-curved shape portion formed in a curved shape, wherein each of the distal indicator and the proximal indicator has a width less than half of an outer circumferential surface of the elongated member in a circumferential direction of the elongated member, respectively.

An endoscopic treatment tool, includes an elongated member having a distal end and a proximal end; a braid disposed between the distal end and the proximal end of the elongated member; a distal indicator disposed on the elongated member between a distal end and a proximal end of the braid, the distal indicator extending along a longitudinal axis of the elongated member; a proximal indicator disposed between the distal end and the proximal end of the braid on the elongated member at a more proximal side of the elongated member than the distal indicator, the proximal indicator extending along the longitudinal axis; and a pre-curved shape portion formed in a curved shape, wherein each of the distal indicator and the proximal indicator has a width less than half of an outer circumferential surface of the elongated member in a circumferential direction of the elongated member, respectively.

Fluorescent imaging systems for performing an endoscopic procedure, such as a retrograde cholangiopancreatography (ERCP) procedure may include a first light source for emitting light in the visible spectrum, or light in the near infrared (NIR) spectrum, or both. A light source bandpass filter may block the emitted light in the visible spectrum, or in the NIR spectrum, or both. A first sensor may be capable of detecting the light in the visible spectrum, or the light in the NIR spectrum, or both. A sensor bandpass filter may block the detected light in the visible spectrum, or in the NIR spectrum, or both. The first or a second light source, or the first or a second sensor, or combinations thereof, may be removably disposed on a duodenoscope.

A suturing apparatus comprises an invaginating device secured to a distal end of an endoscope for invaginating two parts of the interior of a wall of a vessel, for example, a stomach to be sutured together to form a gastric sleeve. The invaginating device includes a suction chamber for forming invaginated parts of the interior of the wall of the vessel. A suturing instrument extending through an instrument channel of the endoscope extends into the invaginating device, and is urged into or adjacent the suction chamber for inserting a suture into the invaginated part in the suction chamber. The next invaginated part is then formed, and the suture is inserted into it. The suture is then tightened to draw the two invaginated parts together.

Provided are a diagnostic imaging device, diagnostic imaging method, diagnostic imaging program, and learned model with which gastric cancer diagnosis can be carried out in real time during endoscopic examination performed using NBI in combination with a magnifying endoscope. The diagnostic imaging device comprises an endoscopic video image acquisition unit which emits narrow-band light at a subject's stomach and acquires an endoscopic video image captured while the stomach is in a state of magnified observation, and an estimation unit which uses a convolutional neural network, which has been caused to learn using gastric cancer images and non-gastric cancer images as training data, to estimate the presence of gastric cancer in the acquired endoscopic video image, and outputs estimation results.

An examination device includes a processor, and the processor detects, in swallowing videoendoscopy in which an inflow object is given to a subject and a swallowing action is observed, at least two timings among a swallowing instruction timing at which a swallowing instruction is given, an inflow timing of the inflow object into the pharynx during the swallowing action, and a swallowing reflex triggering timing in the subject.

An examination device includes a processor, and the processor detects, in swallowing videoendoscopy in which an inflow object is given to a subject and a swallowing action is observed, at least two timings among a swallowing instruction timing at which a swallowing instruction is given, an inflow timing of the inflow object into the pharynx during the swallowing action, and a swallowing reflex triggering timing in the subject.