Abstract: Disclosed herein is a medical image reading assistant apparatus. The medical image reading assistant apparatus includes a computing system. The computing system includes: a receiver interface configured to receive a first medical image and a second medical image following the first medical image; and at least one processor configured to: generate follow-up information between first findings on the first medical image and second findings on the second medical image by comparing the locations of the first findings and the locations of the second findings; select at least some of the first findings as first valid findings based on a first threshold of diagnostic assistant information for each of the first findings; set a second threshold of diagnostic assistant information for each of the second findings based on the follow-up information; and select at least some of the second findings as second valid findings based on the second threshold.

Abstract: Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

Abstract: Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

Abstract: Disclosed herein is a medical image reading assistant apparatus. The medical image reading assistant apparatus includes a computing system. The computing system includes: a receiver interface configured to receive a first medical image and a second medical image following the first medical image; and at least one processor configured to: generate follow-up information between first findings on the first medical image and second findings on the second medical image by comparing the locations of the first findings and the locations of the second findings; select at least some of the first findings as first valid findings based on a first threshold of diagnostic assistant information for each of the first findings; set a second threshold of diagnostic assistant information for each of the second findings based on the follow-up information; and select at least some of the second findings as second valid findings based on the second threshold.

Abstract: Disclosed herein is a system for an interventional procedure using medical images. The system includes: a master device configured to perform control so that a medical tool of a robot arm follows intervention trajectories; an image processing device configured to register plan images, in which a surgical plan including the intervention trajectories of the medical tool is set up, and preoperative (pre-op) images acquired at a surgical site before surgery so that the surgical plan is transferred to the pre-op images and register the pre-op images and intraoperative (intra-op) images acquired at the surgical site during the surgery; and a user interface device configured to visualize the predicted arrival location of the tip of the medical tool for a target point when the robot arm operates according to the surgical plan by using the pre-op images and the intra-op images in conjunction with the image processing device.

Abstract: Disclosed herein is an artificial neural network-based medical image diagnosis assistance apparatus for assisting in diagnosing a medical image based on a medical artificial neural network. A medical image diagnosis assistance apparatus according to an embodiment of the present invention includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network about a first medical image, to detect user feedback on the first analysis result input by a user, to determine whether the first analysis result and the user feedback satisfy training conditions, and to transfer the first analysis result and the user feedback satisfying the training conditions to a style learning model so that the style learning model is trained on the first analysis result and the user feedback.

Abstract: Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

Abstract: Disclosed herein are a computer assistance method for the reduction of a fracture and a computing system for assisting the reduction of a fracture. The computer assistance method includes: generating a first model of a first bone, which is a treatment target, based on a pre-operative medical image; generating a second model, assumed to be obtained after the restoration of the first bone, based on the pre-operative medical image; acquiring a third model in which the spatial information of the first model has been updated by incorporating, into the spatial information, the movement of the first bone occurring during treatment after the installation of markers onto the first bone; and generating overlay visual information based on the second and third models registered onto the same space.

Abstract: Disclosed herein are a computer assistance method for the reduction of a fracture and a computing system for assisting the reduction of a fracture. The computer assistance method includes: generating a first model of a first bone, which is a treatment target, based on a pre-operative medical image; generating a second model, assumed to be obtained after the restoration of the first bone, based on the pre-operative medical image; acquiring a third model in which the spatial information of the first model has been updated by incorporating, into the spatial information, the movement of the first bone occurring during treatment after the installation of markers onto the first bone; and generating overlay visual information based on the second and third models registered onto the same space.

Abstract: A system and method for simulating the reconstructive surgery of a cruciate ligament using medical images are disclosed herein. The system includes a processor, the processor is configured to acquire a first image of a knee region of a subject, and a 3D model including the knee region, identify the locations of reference points, on the 3D model, acquire a second image corresponding to the changes in the movement of the knee region, and track changes in the locations of the reference points using the acquired second image, and simulate changes in the length of a virtual ligament attributable to the changes in the movement of the knee region based on the tracked changes in the locations of the reference points and the location of the insertion of the virtual ligament received from a user.