Abstract: A method for generating an intraoperative 3D brain model while a patient is operated. Before an opening in a patient's skull is made, the method includes: providing a preoperative 3D brain model of a patient's brain and converting it to a preoperative 3D brain point cloud; providing a preoperative 3D face model of a patient's face and converting it to a preoperative 3D face point cloud. After the opening in the patient's skull is made, the method includes: matching the intraoperative 3D face point cloud with the preoperative 3D face point cloud to find a face point transformation; transforming the intraoperative 3D brain point cloud based on said face point cloud transformation; comparing the intraoperative 3D brain point cloud with the preoperative 3D brain point cloud to determine a brain shift; and converting the preoperative 3D brain model to generate an intraoperative 3D brain model based on said brain shift.

Abstract: A method that includes: receiving a medical image of an examined patient, the medical image covering an area or volume of the examined patient's anatomy; inputting the medical image to a classifying neural network to generate descriptors; receiving additional data of the examined patient; providing an other patients history database comprising other patients' records, the records including the descriptors, the additional data and a clinical outcome of individual patients; determining a patient from the other patient's history database being a closest match to the examined patients in terms of features of the descriptors to be a digital twin patient; presenting the clinical outcome of the digital twin patient.

Abstract: A method for determining cognitive capabilities of a person includes: receiving a brain Magnetic Resonance Imaging (MRI) volume that represents a brain; segmenting the brain MRI volume into white matter, grey matter and cerebrospinal fluid; selecting white matter and/or grey matter from the segmented volume; determining a convex hull shape; computing the contour of the white matter and/or the grey matter shape and the contour of the convex hull shape; and computing a gyrification index based on a comparison of voxels that constitute the contour of the white matter and/or the grey matter and the contour of the convex hull.

Abstract: A nucleic acid molecule composed of a duplex and loop, in which one of the duplex strands, the guide strand, comprises a sequence chosen from SEQ ID NO. 1-4, and the other strand of the duplex, the passenger strand, is at least 80% complementary to the guide strand, wherein the nucleic acid molecule forms a hairpin structure in a cell.

Abstract: A method for cerebral vessel calcification detection and classification includes the steps of: receiving a set of input Computed Tomography (CT) images representing consecutive slices of a 3D volume of cerebral vessels; performing a region of interest regression to determine a ROI within the input CT images that is a cuboid that contains a circle of Willis; performing calcification detection based on the ROI within the input CT images, by means of: a segmentation procedure and/or an anomaly detection procedure; and performing quantification of the predicted locations of vessel calcifications to indicate at least one of: a volume or intensity of individual calcifications.

Abstract: A computer-implemented method for autonomous reconstruction of vessels on computed tomography images, includes: providing a reconstruction convolutional neural network (CNN); receiving an input 3D model of a vessel to be reconstructed; defining a region of interest (ROI) and a movement step, wherein the ROI is a 3D volume that covers an area to be processed; defining a starting position and positioning the ROI at the starting position; reconstructing a shape of the input 3D model within the ROI by inputting the fragment of the input 3D model within the ROI to the reconstruction convolutional neural network (CNN) and receiving the reconstructed 3D model fragment; moving the ROI by the movement step along a scanning path; repeating the reconstruction and moving steps to reconstruct a desired portion of the input 3D model at consecutive ROI positions; and combining the reconstructed 3D model fragments.

Abstract: A computer-implemented method for modelling blood vessels to support assessment of probability of rupture or damage to the plaque. The method includes steps of: obtaining medical imaging data of the blood vessels; generating a three-dimensional model of the blood vessels, based on the medical imaging data including identifying one or more pathological plaques; performing pre-simulation of the three-dimensional model, establishing boundary conditions and initial conditions for both models for a steady flow of blood and a transient flow of blood; performing a numerical simulation of the transient flow of blood; performing a numerical simulation of the steady flow of blood; and for a selected plaque, identifying geometrical parameters of a surface of the plaque, including shape, curvature, curvature of the major surface, and/or Gauss curvature of the plaque surface. The method may include calculation of Reference Dynamic Pressure (RDP) and Degree of Stenosis (DS).

Abstract: A computer-implemented method for fully-autonomous level identification of anatomical structures within a three-dimensional medical imagery, includes: receiving a set of medical scan images of the anatomical structures; processing the set to perform an autonomous semantic segmentation of anatomical components and to store segmentation results; processing segmentation results by removing the false positives, and smoothing 3D surfaces of the generated anatomical components; determining morphological and spatial relationships of the anatomical components; grouping the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing the set using a convolutional neural network to autonomously assign an initial level type; assigning the determined level type to each group of anatomical components by combining the determined morphological and spatial relationships with the determined initial level type; assigning an ordinal identifier to eac

Abstract: A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (200); training the CNN (200) to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN (200) to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.

Abstract: A method for creating a personalized stent or stent graft for a blood vessel with a saccular aneurysm includes: receiving a 3D model of the blood vessel with the saccular aneurysm; and generating a model of a personalized stent or stent graft that comprises a net shaped to fit along internal walls of the blood vessel and a covering positioned with respect to the net such as to cover an ostium of the aneurysm.

Abstract: A method for generating an intraoperative 3D brain model while a patient is operated. Before an opening in a patient's skull is made, the method includes: providing a preoperative 3D brain model of a patient's brain and converting it to a preoperative 3D brain point cloud; providing a preoperative 3D face model of a patient's face and converting it to a preoperative 3D face point cloud. After the opening in the patient's skull is made, the method includes: matching the intraoperative 3D face point cloud with the preoperative 3D face point cloud to find a face point transformation; transforming the intraoperative 3D brain point cloud based on said face point cloud transformation; comparing the intraoperative 3D brain point cloud with the preoperative 3D brain point cloud to determine a brain shift; and converting the preoperative 3D brain model to generate an intraoperative 3D brain model based on said brain shift.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving (101) an x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing (102) the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation (103) by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.

Abstract: A computer-implemented method for modelling blood vessels, that includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels; generating a three-dimensional reconstructed model of the blood vessels that reflects a state of healthy blood vessels that lack lesions; performing a pre-simulation of the reconstructed model; determining absolute or relative indexes of blood flow as a function that compares at least on of pressure, velocity or energy flow between the personalized model and the reconstructed model.

Abstract: A computer-implemented method for autonomous reconstruction of vessels on computed tomography images, includes: providing a reconstruction convolutional neural network (CNN); receiving an input 3D model of a vessel to be reconstructed; defining a region of interest (ROI) and a movement step, wherein the ROI is a 3D volume that covers an area to be processed; defining a starting position and positioning the ROI at the starting position; reconstructing a shape of the input 3D model within the ROI by inputting the fragment of the input 3D model within the ROI to the reconstruction convolutional neural network (CNN) and receiving the reconstructed 3D model fragment; moving the ROI by the movement step along a scanning path; repeating the reconstruction and moving steps to reconstruct a desired portion of the input 3D model at consecutive ROI positions; and combining the reconstructed 3D model fragments.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; dividing the CT scan volume into a first set of subvolumes; extracting a region of interest by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to a second set of 3D subvolumes; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; converting the CT scan volume to three sets of two-dimensional slices; extracting a region of interest (ROI) by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks (CNN), each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to three groups of sets of two-dimensional masked slices; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Abstract: A communicating device for placing within a blood vessel. The communicating device has a proximal end part, a terminal end part and a wired connector that communicatively connects the proximal end part with the terminal end part. The proximal end part has a battery connected to a wireless charging terminal and a signal processing circuit connected to a wireless communication terminal. The terminal end part has a plurality of sensors, electrodes and microfilaments to be deployed through the walls of the vessel.

Abstract: A method for determining a significance of a stenosis in a currently examined blood vessel, the method comprising: providing a pre-trained reasoning module (130) that has been trained to output a value of a stenosis significance parameter by means of a training data set comprising a plurality of records of prior clinically examined stenosis cases, each training record comprising data related to dimensional parameters, blood flow parameters and clinical measurement parameters of the prior clinically examined blood vessel containing the stenosis; inputting, to the pre-trained reasoning module (130), an examination record comprising data related to the dimensional parameters of the currently examined blood vessel containing the stenosis and instructing the reasoning module (130) to output the value of the stenosis significance parameter based on the examination record.

Abstract: A computer-implemented method for predicting neurological treatment for a patient. The method includes analyzing a pre-stored brain image of the patient by means of a Convolutional Neural Network to determine brain image analysis result including at least one of: a presence of a tumor or lesion, brain age, brain health, gyrification coefficient; receiving additional data, including at least one of: voice recognition index, additional symptom checks, blood work results, genetic sequencing results; and combining the brain image analysis result with the additional data to determine a score related to a probability that the patient may have a particular disease.

Abstract: A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (200); training the CNN (200) to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN (200) to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.