Abstract: Disclosed are an apparatus and method for assisting the diagnosis of a medical image by an automated system. A computing system includes a memory or database that stores a plurality of medical image diagnosis algorithms each having a medical image diagnosis function. A processor inside the computing system extracts diagnosis requirements for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms to be applied to the diagnose of the medical image from among a plurality of medical image diagnosis algorithms based on the diagnosis requirements, and generates display information including diagnosis results for the image frame by applying the plurality of selected diagnosis application algorithms to the image frame.

Abstract: A gastroscopic image diagnosis supporting system is configured to analyze a video frame of the gastroscopic image using a first medical image analysis model of the medical image analysis models; classify a gastrointestinal anatomical position of the video frame by identifying a part corresponding to a preset gastrointestinal anatomical position class in the video frame; and store information about the gastrointestinal anatomical position of the video frame as index information of the finding information together with finding information when a user captures and stores the video frame as the finding information about a gastrointestinal lesion.

Abstract: A system and method for assisting colonoscopic image diagnosis based on artificial intelligence include a processor, which is configured to analyze each video frame of a colonoscopic image using at least one medical image analysis algorithm and detects a finding suspected of being a lesion in the video frame. The processor calculates the coordinates of the location of the finding suspected of being a lesion. The processor generates display information, including whether the finding suspected of being a lesion is present and the coordinates of the location of the finding suspected of being a lesion.

Abstract: A system and method assist gastroscopic image diagnosis based on artificial intelligence. The processor in the system analyzes each video frame of a gastroscopic image using at least one medical image analysis algorithm and detects whether a finding suspected of being a lesion is present in the video frame. When the finding suspected of being a lesion is present in the video frame, the processor calculates the coordinates of the location of the finding suspected of being a lesion. The processor generates display information, including whether the finding suspected of being a lesion is present and the coordinates of the location of the finding suspected of being a lesion.

Abstract: Disclosed are a medical image diagnosis assistant apparatus and method. The diagnosis assistant apparatus includes a computing system includes a processor. The computing system stores a plurality of medical image diagnosis algorithms each having the diagnostic function of a medical image in memory or a database. The processor extracts a diagnosis requirement for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms based on the diagnosis requirement to analyze the medical image from among the plurality of medical image diagnosis algorithms, stores a plurality of diagnosis results for the medical image associated with each of the plurality of diagnosis application algorithms in the memory or database inside the computing system, and generates evaluation scores for the plurality of medical image diagnosis algorithms.

Abstract: Disclosed are an apparatus and method for assisting the diagnosis of a medical image by an automated system. A computing system includes a memory or database that stores a plurality of medical image diagnosis algorithms each having a medical image diagnosis function. A processor inside the computing system extracts diagnosis requirements for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms to be applied to the diagnose of the medical image from among a plurality of medical image diagnosis algorithms based on the diagnosis requirements, and generates display information including diagnosis results for the image frame by applying the plurality of selected diagnosis application algorithms to the image frame.

Abstract: Disclosed are a medical image diagnosis assistant apparatus and method. The diagnosis assistant apparatus includes a computing system includes a processor. The computing system stores a plurality of medical image diagnosis algorithms each having the diagnostic function of a medical image in memory or a database. The processor extracts a diagnosis requirement for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms based on the diagnosis requirement to analyze the medical image from among the plurality of medical image diagnosis algorithms, stores a plurality of diagnosis results for the medical image associated with each of the plurality of diagnosis application algorithms in the memory or database inside the computing system, and generates evaluation scores for the plurality of medical image diagnosis algorithms.

Abstract: An ink composition and method of printing naturally formed patterns on the surface of various materials, such as paper, glass, ceramic, metal or plastics. The printing method uses a coating liquid of a composition comprising a principal component and a hardener component, applying the component system to a surface through the use of any of a plurality of application methods, and drying the applied surface in two stages which includes a primary drying stage, partly at room temperature, and a forced air drying stage, at elevated temperatures. The process results in a decorative lightly textured surface of a crackled appearance. The principal component of the coating composition consists of a polyol resin, ethylene glycol acetate, ethyl acetate, toluene, filler and pigment. The hardener component consists of blocked isocyanate, ethyl acetate and toluene.