Ai Medical Service Inc.

Japanese Foundation for Cancer Research

Tomohiro Tada

Kazuharu Aoyama

Tsuyoshi Ozawa

Yuma Endo

Hiroaki Saito

Atsuo Yamada

Kentaro Nakagawa

Youichi Kumagai

Toshiaki Hirasawa

Hirotoshi Takiyama

Toshiyuki Yoshio

Syuntaro Inoue

Ryu Ishihara

Satoki Shichijyo

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network (CNN). A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with a CNN trains the CNN using a first endoscopic image of the digestive organ and at least one final diagnosis result on positivity or negativity to the disease in the digestive organ, a past disease, a severity level, and information corresponding to a site where an image is captured, the final diagnosis result corresponding to the first endoscopic image, and the trained CNN outputs at least one of a probability of the positivity and/or the negativity to the disease, a probability of the past disease, a severity level of the disease, an invasion depth of the disease, and a probability corresponding to the site where the image is captured.

Diagnostic assistance method, diagnostic assistance system, diagnostic assistance program, and computer-readable recording medium storing therein diagnostic assistance program for disease based on endoscopic image of digestive organ

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.

Image diagnosis assistance apparatus, data collection method, image diagnosis assistance method, and image diagnosis assistance program

Provided is a disease diagnosis support method employing endoscopic images of a digestive organ using a neural network, and the like. The disease diagnosis support method employing endoscopic images of a digestive organ using a neural network trains the neural network by using first endoscopic images of the digestive organ, and corresponding to the first endoscopic images, at least one of definitive diagnosis result of being positive or negative for the disease of the digestive organ, a past disease, a severity level, and information corresponding to an imaged region. The trained neural network outputs, based on second endoscopic images of the digestive organ, at least one of a probability of being positive and/or negative for the disease of the digestive organ, a probability of a past disease, a severity level of the disease, and the information corresponding to the imaged region.

Disease diagnosis support method employing endoscopic images of a digestive organ, a diagnosis support system, a diagnosis support program and a computer-readable recording medium having the diagnosis support program stored therein

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Tokyo, Japan

Tomohiro Tada