A medical care support device includes: an acquisition unit that acquires medical information including medical image data representing a medical image obtained by capturing a lung of a subject, breed information representing a breed of the subject, and age information representing an age of the subject when the medical image is captured; and a derivation unit that derives a degree of calcification of the lung of the subject based on the medical information acquired by the acquisition unit and a learned model learned in advance using a plurality of pieces of learning medical information including medical image data representing a medical image in which a label is assigned to a calcified portion of the lung, the breed information, and the age information.

A method and imaging system for contouring medical images is described. The method comprising: receiving at least one input 2D image slice, from a set of two-dimensional (2D) image slices constituting the 3D image, and at least one set of data representing an input contour identifying one or more structures of interest in the 3D image within the at least one input 2D image slice; receiving at least one selected target image slice, from the set of the 2D image slices; and predicting target contour data for the selected target image slice that identifies at least one of the same one or more structures of interest within the target image slice, based on one or more of the received input 2D image slices and the data representing an input contours.

Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

A method of reading a medical image by a computing device operated by at least one processor is provided. The method includes obtaining an abnormality score of the input image using an abnormality prediction model, filtering the input image so as not to be subsequently analyzed when the abnormality score is less than or equal to a cut-off score based on the cut-off score which makes a specific reading sensitivity; and obtaining an analysis result of the input image using a classification model that distinguishes the input image into classification classes when the abnormality score is greater than the cut-off score.

A system is configured to perform operations includes accessing a set of model points of a model of an anatomic structure of a patient, the model points being associated with a model space. A set of measured points of the anatomic structure of the patient are collected, the measured points being associated with a patient space. The set of model points are registered to the set of measured points using a first set of initial parameters to generate a first transformation. One or more sets of perturbed initial parameters are generated based on the first set of initial parameters. One or more perturbed registration processes are performed to register the set of model points to the set of measured points using the one or more sets of perturbed initial parameters respectively to generate corresponding perturbed transformations. A registration quality indicator is generated based on the first transformation and the one or more perturbed transformations.

Provided is a dynamic analysis apparatus that predicts a respiratory function value based on frame images showing dynamics of chest. The dynamic analysis apparatus includes a hardware processor that obtains a first lung size value of a removal target site and a second lung size value of a left or right lung field including the removal target site, calculates a proportion between the first and second lung size values as a size proportion, calculates a first feature amount concerning respiratory function of the left or right lung field including the removal target site and a second feature amount concerning respiratory function of the lung fields as a whole, calculates a proportion between the first and second feature amounts as a feature amount proportion, and calculates an expected rate of the respiratory function without the removal target site, based on a product of the size proportion and the feature amount proportion.

A method, device and system for automated processing of medical images to output alerts for detected dissimilarities in the medical images is provided. In one aspect, the method comprises receiving a first medical image of an anatomical object of a patient, the first medical image being acquired at a first instance of time; receiving a second medical image of the anatomical object of the patient, the second medical image being acquired at a second instance of time; determining an image similarity between image data of the first medical image and image data of the second medical image; determining a dissimilarity between the first medical image and the second medical image based on the image similarity; and outputting an alert for the dissimilarity.

A processor is configured to derive, with reference to a database in which a plurality of reference images are saved such that an interpretation result about an abnormal shadow included in each reference image of the plurality of reference images is associated with the reference image, a degree of similarity between a target image and each of the plurality of reference images; the processor is configured to analyze, for a similar reference image, among the plurality of reference images, for which the degree of similarity is greater than or equal to a predetermined threshold value, an interpretation result regarding the similar reference image to thereby derive progression information about an abnormal shadow included in the similar reference image; and the processor is configured to statistically analyze the progression information to thereby derive prediction information for predicting future progression of an abnormal shadow included in the target image.

This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.

Systems and methods are provided for robust intensity ranges for automatic lesion volume measurement. Imaging data obtained during medical imaging examination of a patient may be processed, with the imaging data corresponding to a particular medical imaging technique. One or more parameters pertinent to identifying particular features in at least one organ or body structure may be automatically determined. At least one medical image may be generated and displayed based on processing of the imaging data. The displaying may include identifying, based on the one or more parameters, one or more areas in the at least one organ or body structure and providing within displayed at least one medical image visual feedback relating to the identified one or more areas in the at least one organ or body structure.