Abstract: Provided are computer-implemented methods and systems for classifying a medical image data set. In particular, a method is provided comprising the steps of receiving the medical image dataset of a patient, of providing a first classification stage configured to classify the medical image dataset as normal or not-normal, of providing a second classification stage different than the second classification stage and configured to classify the medical image dataset as normal or not-normal, and of subjecting the medical image dataset to the first classification stage to classify the medical image dataset as normal or not-normal. Further, the method comprises subjecting the medical image dataset to the second classification stage to classify the medical image dataset as normal or not-normal, if the medical image dataset is classified as normal in the first classification stage.

Abstract: Systems and methods for managing patient diagnostic and therapy workflows in a hospital and/or radiology centers. A radiology recommendation agent is trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time.

Abstract: Systems and methods that leverage the power of artificial intelligence (AI) to enhance the process of whole-body MRI scanning. AI models optimize the acquisition protocol, resulting in shorter and more patient-friendly scan durations. Furthermore, AI models aid in the automatic interpretation of the imaging data, highlighting potential areas of concern and streamlining the diagnostic process.

Abstract: Systems and methods for generating pseudo-masks are provided. 1) a first and a second medical image of an anatomical structure of a patient and 2) a mask of the anatomical structure in the second medical image are received. The second medical image is aligned to the first medical image according to a deformation field. The mask of the anatomical structure in the second medical image is modified based on the deformation field to generate a pseudo-mask of the anatomical structure in the first medical image. The pseudo-mask of the anatomical structure in the first medical image is output.

Abstract: For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Abstract: Systems and methods for AI-powered histological fingerprinting in magnetic resonance imaging. MR signal data of an object is acquired using a high sensitivity scanner. Ground truth tissue microstructure data is acquired for the object. A forward model is learned using machine learning. The forward model is used to generate a dictionary or to train a model to map the signals to the histological parameters including the tissue microstructure of a scanner object. A signal-to-signal translation model is also provided to provide signals with improved sensitivity.

Abstract: Systems and methods for performing one or more medical imaging analysis tasks are provided. A sequence of medical images is received. One or more patches are extracted from each image of the sequence of medical images. Spatio-temporal features are extracted from the one or more extracted patches using a machine learning based encoder network. One or more medical imaging analysis tasks are performed based on the extracted spatio-temporal features. Results of the one or more medical imaging analysis tasks are output. The machine learning based encoder network is trained by receiving a sequence of training medical images. Patches of a first set of images of the sequence of training medical images are masked according to a first masking strategy. Patches of a second set of images of the sequence of training medical images are masked according to a second masking strategy.

Abstract: Systems and methods for performing a medical imaging analysis task using a foundation model are provided. One or more 3D (three-dimensional) medical images each comprising a plurality of 2D (two-dimensional) slices are received. A first set of features is extracted from the plurality of 2D slices of the one or more 3D medical images using a machine learning based encoding network. Each respective feature of the first set of features is resampled based on a spatial location of one or more pixels of the 2D slices from which the respective feature was extracted. The resampled first set of features is encoded into a second set of features. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task using a universal foundation model are provided. 1) one or more input medical images each in a domain and 2) a domain code for each of the one or more input medical images identifying its domain are provided. For each respective one of the domain codes, one or more weights are determined based on the respective domain code. One or more parameters of a dynamic convolutional layer are updated based on the one or more weights. A first set of features is extracted from the one or more input medical images using the dynamic convolutional layer with the one or more updated parameters. The first set of features are encoded into a second set of features using a machine learning based encoder. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task conditioned on multi-domain medical images with missing modalities are provided. 1) one or more medical images each in a different domain and 2) a domain code defining a presence of the different domains in a set of predefined domains are received. One or more weights are determined based on the domain code. One or more parameters of a machine learning based encoder are updated based on the one or more weights. Features are extracted from the one or more medical images using the machine learning based encoder with the one or more updated parameters. A medical imaging analysis task is performed based on the extracted features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions.

Abstract: Systems and methods for performing a quality assessment of a medical imaging analysis task are provided. At least one low-field MRI (magnetic resonance imaging) quality assurance imaging data of the patient is received. A quality assessment of a medical imaging analysis task is performed based on the at least one low-field MRI quality assurance imaging data using one or more machine learning based networks. Results of the quality assessment are output.

Abstract: Systems and methods for performing a medical imaging analysis task based on pixelwise positionally encoded features are provided. One or more input medical images are received. One or more pixelwise positional embedding images are generated for the one or more input medical images using a spatially varying function. Patches are extracted from the one or more input medical images and the one or more pixelwise positional embedding images. The patches extracted from the one or more input medical images are encoded with corresponding ones of the patches extracted from the one or more pixelwise positional embedding images into pixelwise positionally encoded features. A medical imaging analysis task is performed using a machine learning based network based on the pixelwise positionally encoded features. Results of the medical imaging analysis task are output.

Abstract: An AI algorithm may be used in a clinical setting to perform one or more tasks to assist medical personnel. The results produced by the AI algorithm may affect not only patient care, but also the cost of the care. The AI algorithm may be trained on auxiliary data to incorporate the impacts on patient care and cost.

Abstract: Systems and methods for automatic interaction between a user and one or more clinical information systems and one or more medical applications are provided. First text-based instructions are received from a user. 1) Second text-based instructions for retrieving clinical information and 2) third text-based instructions for performing a medical analysis task are generated by an interface AI agent based on the first text-based instructions. One or more actions for retrieving the clinical information from one or more clinical information systems are determined by a data AI agent based on the second text-based instructions. One or more actions for executing on one or more medical applications to perform the medical analysis task are determined by a task AI agent based on the third text-based instructions. The clinical information and results of the medical analysis task are output.

Abstract: Systems and methods for automatically performing one or more actions on one or more medical applications are provided. Text-based instructions are received. The text-based instructions are encoded into text features using a machine learning based text encoder network. One or more instructions for performing by one or more medical applications are determined using a policy module based on the text features. The one or more instructions are performed by the one or more medical applications to generate a response to the text-based instructions. The response to the text-based instructions is output.

Abstract: Systems and methods for advanced pulmonary nodule analysis with malignancy-weighted detection and customized visualization are provided. One or more medical images of a patient are received. One or more candidate nodules are detected in the one or more medical images. Each of the one or more candidate nodules are associated with a nodule detection score. A malignancy score is determined for each of the one or more candidate nodules. The nodule detection score is weighted for each of the one or more candidate nodules based on its malignancy score. The weighted nodule detection scores are output.

Abstract: Systems and methods for determining a correction between an input text-based query and one or more input medical images are provided. An input text-based query and one or more input medical images are received. The input text-based query is encoded into text features using a machine learning based text encoder network. The one or more input medical images are encoded into a spatial hierarchy of image features using a machine learning based image encoder network. A correlation is determined between the text features and the image features from a top-down of the spatial hierarchy of image features using the machine learning based text encoder network. The correlation between the text features and the image features is output.

Abstract: Systems and methods for AI-powered histological fingerprinting in magnetic resonance imaging. MR signal data of an object is acquired using a high sensitivity scanner. Ground truth tissue microstructure data is acquired for the object. A forward model is learned using machine learning. The forward model is used to generate a dictionary or to train a model to map the signals to the histological parameters including the tissue microstructure of a scanner object. A signal-to-signal translation model is also provided to provide signals with improved sensitivity.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. A pre-contrast medical image and a plurality of post-contrast medical images of an anatomical object of a patient are received. A mask of the anatomical object is generated based on at least one of 1) the pre-contrast medical image or 2) one or more of the plurality of post-contrast medical images. One or more processed post-contrast medical images each associated with a respective feature are generated based on the plurality of post-contrast medical images and the mask of the anatomical object. A medical imaging analysis task is performed using a machine learning based network based on the one or more processed post-contrast medical images and the pre-contrast medical image masked with the mask of the anatomical object. Results of the medical imaging analysis task are output.