Disclosed herein are an apparatus for AI-based automatic ultrasound diagnosis of liver steatosis and a remote medical diagnosis method using the same applied in the field of ultrasound image processing. The apparatus for AI-based automatic ultrasound diagnosis of liver steatosis can automatically determine a grade of liver steatosis, which is difficult to determine visually, through extraction from an image acquired by imaging medical examination using a deep learning trained artificial neural network.

An image processing apparatus includes an acquisition unit configured to acquire image data, a calculation unit configured to calculate, for each unit region of the image data, a confidence that the unit region is an extraction subject, the confidence as the extraction subject being calculated for each unit region of the image data by inputting the image data into a trained model of a neural network that has been trained using images of an existing extraction subject or a region of interest as training data, a modification unit configured to modify a reference value of the confidence, which is used to extract a region of interest, an extraction unit configured to extract the region of interest on the basis of the calculated confidence of each unit region and the modified reference value, and a display unit configured to display the extracted region of interest.

A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

The invention relates to a method for producing variables of interest relating to human or animal hepatic tissue from a digital representation of a histological section. Such a method is intended to be implemented by a unit for processing a medical imaging system to automatically and quickly provide diagnosis assistance, in particular for NASH, to healthcare personnel. The variables of interest respectively describe a level of steatosis of the hepatic tissue, a level of fibrosis in the portal, central and perisinusoidal areas of the hepatic lobule and a level of inflammation of the hepatic tissue. A method according to the invention further provides for producing a multiparametric indicator in the form of graphical representations arranged to be displayed by an output human-machine interface of the medical imaging system.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

A method of finding a set of corresponding points in images to be registered. According to the method, an input unit of the system receives a first user input, indicative of a reference point in an intraoperative image. A processing unit sets a reference area surrounding the reference point and converts the image data points in the reference area to a intraoperative point cloud. The input unit receives a second user input, indicative of a candidate point in a preoperative image. The processing unit sets a search area surrounding the reference point and converts the image data points in the reference area to a preoperative point cloud. By comparing geometric feature descriptors of the image data points, the processing unit finds a target point in the preoperative image corresponding to the reference point and defines the points as set of corresponding points.

In one embodiment, a computer-implemented method provides a prediction of a medical target variable. The computer-implemented method includes receiving medical imaging data of an examination area, the examination area including a plurality of lesions of an anatomical structure, wherein each lesion of the plurality of lesions of the anatomical structure spaced apart from any other lesion of the plurality of lesions of the anatomical structure; calculating a spread parameter based on the medical imaging data, the spread parameter being indicative of a spread of a spatial distribution of the plurality of lesions of the anatomical structure; calculating the prediction of the medical target variable based on the spread parameter; and providing the prediction of the medical target variable.

A method of displaying an area of interest within a surgical site includes modeling a patient's lungs and identifying a location of an area of interest within the model of the patient's lungs. The topography of the surface of the patient's lungs is determined using an endoscope having a first camera, a light source, and a structured light pattern source. Real-time images of the patient's lungs are displayed on a monitor and the real-time images are registered to the model of the patient's lungs using the determined topography of the patient's lungs. A marker indicative of the location of the area of interest is superimposed over the real-time images of the patient's lungs. If the marker falls outside of the field-of view of the endoscope, an arrow is superimposed over the real-time images to indicate the direction in which the marker is located relative to the field of view.

A medical support apparatus has one processor or more, and the processor is configured to: acquire a medical image; and based on the medical image, generate a guidance display that indicates a boundary between segments whose recommendation levels for medical treatment are different in the medical image and that is to be superimposed on the medical image.