A processor for an endoscope according to an aspect is characterized by including: a controller executing program code to perform: acquiring, by the controller, an endoscopic image captured using first system information; discriminating a part of a subject using a first learning model that outputs a discrimination result of discriminating the part of the subject in a case in which the acquired endoscopic image is input; acquiring, by the controller, a setting image associated with the discrimination result output by the first learning model; and outputting second system information using a second learning model that outputs the second system information in a case in which the acquired setting image and the part of the subject are input.

A processor for an endoscope according to an aspect is characterized by including: a controller executing program code to perform: acquiring, by the controller, an endoscopic image captured using first system information; calculating, by the controller, parameter on the basis of the endoscopic image acquired by the controller; discriminating a part of a subject using a first learning model that outputs a discrimination result of discriminating the part of the subject in a case in which the calculated parameter is input; outputting second system information using a second learning model that outputs the second system information in a case in which the parameter and the discriminated part of the subject are input; and determining, by the controller, a difference between the second system information output by the second learning model and the first system information.

A method for evaluating a gastrointestinal tract may include characterizing one or more disease parameters using objective measures obtained from imaging data of a gastrointestinal tract. The one or more disease parameters reflect a measure of at least one of lesions, ulcers, bleeding, stenosis, and vasculature. The method may also include using the one or more characterized disease parameters to classify a disease state.

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.

A method includes steps of: based on training sets of gastrointestinal images, using a predetermined machine learning algorithm to obtain a preliminary model; feeding preliminary validation sets of gastrointestinal images into the preliminary model to obtain estimation results; based on the estimation results, selecting, from the preliminary validation sets of gastrointestinal images, a series of successive images as a selected validation set of gastrointestinal images; based on the selected validation set of gastrointestinal images, tuning parameters of the preliminary model to result in a speed-determining model for determining a moving speed of an endoscope camera.

A processor acquires a first radiographic image and a second radiographic image obtained by irradiating a subject, into which a tracheal tube has been inserted, with radiation in different directions, derives a three-dimensional position of a distal end of the tracheal tube in the subject on the basis of a position of the distal end of the tracheal tube in each of the first radiographic image and the second radiographic image, derives a three-dimensional position of a bronchial bifurcation in the subject on the basis of a bronchial bifurcation position in each of the first radiographic image and the second radiographic image, and determines insertion of the tracheal tube into an esophagus on the basis of the three-dimensional position of the distal end of the tracheal tube and the three-dimensional position of the bronchial bifurcation.

An X-ray image processing apparatus of an embodiment includes processing circuitry. The processing circuitry acquires fluoroscopy-related information indicating at least one of a fluoroscopic image and a condition for collecting the fluoroscopic image. The processing circuitry evaluates the image quality of the fluoroscopic image based on the fluoroscopy-related information. The processing circuitry outputs identification information identifying whether to save the fluoroscopic image based on the evaluation result.

A method of examining digestive tract images is provided. The method of examining digestive tract images include: obtaining a basic image taken by a photographing apparatus; obtaining a set H of pixels with Hue in the range of D1 in the basic image; obtaining a set S of pixels with Saturation in the range of D2 in the basic image; obtaining a set of pixels with Value in the range of D3 in the basic image, and recording it as an effective pixel range; selecting all pixel sets of the set H and the set S in the effective pixel range to form a detection graph block; examining whether the basic image is an unclean image according to the detection graph block. The present invention further provides a method of examining the cleanliness of the digestive tract, a computer device, a computer-readable storage medium thereof.

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with a medical tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing a first graphical marker on the image that indicates an end of the medical tube or line, and displaying the combined image on the display.

A method and system for analyzing and/or estimating intestinal microflora of a subject. A digital image of a sample of feces of the subject is received by one or more processors. The digital image and/or one or more features extracted from the digital image is provided as input to a trained machine learning model which is configured to output a classification based on said input digital image and/or one or more features extracted from the digital image. Data indicative of one or more properties of the intestinal microflora of the subject based on the output image classification is determined by the one or more processors.