Abstract: One or more processors calculate a first score value indicating likelihood of being a type of the identification target drug with respect to each of a plurality of types of drugs from the image by using a first trained model trained to identify a type of an identification target drug from an image obtained by capturing the identification target drug, identify one or more appearance attributes of the identification target drug from the image, acquire information on drug types matching the attributes of the identification target drug from the drug master file including information on one or more appearance attributes regarding each of a plurality of types of drugs identifiable by the first trained model, calculate a second score value by using the first score value and a value being set for the attribute; and present a candidate for the type of the identification target drug based on second score value.

Abstract: A region of a drug to be identified is detected from a captured image generated by imaging the drug to be identified that is imparted with engraved mark and/or print. The region of the drug to be identified in the captured image is processed to acquire an engraved mark and print extraction image that is an extracted image of the engraved mark and/or print of the drug to be identified. The engraved mark and print extraction image is input, and a drug type of the drug to be identified is inferred to acquire a candidate of the drug type of the drug to be identified.

Abstract: An image acquiring unit of acquires a captured image in which two or more medicines of a plurality of objects (medicines) are in point-contact or line-contact with one another. A first recognizer receives the captured image and generates an edge image indicating only a part where medicines are in point-contact or line-contact with one another, in the captured image. A second recognizer receives the captured image and the edge image, recognizes each of the plurality of medicines from the captured image, and outputs a recognition result. Since the second recognizer receives edge image indicating only the part where medicines are in point-contact or line-contact useful for separating the areas of the medicines, even if two or more medicines of the plurality of medicines are in point-contact or line-contact with one another, it is possible to accurately separate and recognize the areas of the plurality of medicines from the captured image.

Abstract: It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.

Abstract: An image processing apparatus and method that can enhance visibility of medicines are provided. An image obtained by imaging medicines is acquired, the medicines are detected from the acquired image, and images of the detected medicines are cropped to generate individual medicine images. It is determined whether visibility of each generated individual medicine image is good or poor, and an individual medicine image with poor visibility is subjected to processing to enhance visibility and displayed on a display device.

Abstract: A medical information registration support device according to a first aspect of the present invention includes an input accepting unit configured to accept input of medical information, a determining unit configured to determine whether the accepted medical information and registration information match each other, a candidate generating unit configured to generate a candidate for the registration information for the accepted medical information by using a trained model that is constructed through machine learning, standardizes input medical information according to a type of the medical information and outputs the standardized medical information when the accepted medical information and the registration information do not match each other, and a registering unit for registering, as the registration information, medical information selected from the candidate or medical information obtained by correcting the candidate in association with the accepted medical information.

Abstract: It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.