Abstract: Provided are a diagnostic imaging device, a diagnostic imaging method, a diagnostic imaging program and a learned model, which can improve the diagnostic accuracy for esophagus cancer in esophagogastroduodenoscopy. This diagnostic imaging device comprises: an endoscopic image acquisition unit for acquiring an endoscopic moving image showing the esophagus of a person being tested; an estimation unit for estimating the location of esophagus cancer that is present in the acquired endoscopic moving image, using a convolutional neural network that has learned from esophagus cancer images as training data, the esophagus cancer images having been obtained from esophaguses affected by esophagus cancer; and a display control unit that superimposes on the endoscopic moving image an estimated location of the esophagus cancer and a certainty factor serving as an index for the probability that esophagus cancer is present at that location.

Abstract: 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.

Abstract: 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.

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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present invention herein provides a compound which promotes drug-permeation into nails. A penetrating promoter comprises, for instance, the following formulas. R1, R2 and R3 are alkyl groups having a carbon number of 1 to 15 and have OH groups at their ends.

Abstract: The present invention provides an antifungal agent for topical use and remedy for athlete's foot that comprises a new combination of medicines and has a strong antifungal activity against both genus Trichophyton and genus Candida. The antifungal agent for topical use and remedy for athlete's foot contain amorolfine with butenafine or terbinafine.