Abstract: Computed tomography (CT) measurement data of the patient is acquired using a CT device having a quantum counting X-ray detector, and the CT measurement data is processed to generate result data, considering a specific information content of the CT measurement data resulting from the use of the quantum counting X-ray detector in acquiring the CT measurement data. The result data is suitable for use in the planning of irradiation of the patient. The result data is provisioned to an interface such that the result data is usable for planning the irradiation of the patient.

Abstract: A computer-implemented method of adjudicating an imaged lesion, comprising: receiving a diagnostic image showing a lesion; processing the diagnostic image in a machine learning algorithm previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel suited to adjudicate the lesion; and outputting the proposed blood test panel to a user.

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: A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

Abstract: One or more example embodiments of the present invention relates to a computer-implemented method for providing radiological visualization data. The method includes receiving radiological imaging data which relates to an examination region, calculating confidence data based on the radiological imaging data, the confidence data relating to a confidence score with which an abnormality of the examination region can be automatically excluded, calculating the radiological visualization data which relates to the examination region, wherein a data reduction of the radiological visualization data relative to the radiological imaging data is effected as a function of the confidence data, and providing the radiological visualization data.

Abstract: Computed tomography (CT) measurement data of the patient is acquired using a CT device having a quantum counting X-ray detector, and the CT measurement data is processed to generate result data, considering a specific information content of the CT measurement data resulting from the use of the quantum counting X-ray detector in acquiring the CT measurement data. The result data is suitable for use in the planning of irradiation of the patient. The result data is provisioned to an interface such that the result data is usable for planning the irradiation of the patient.

Abstract: Computed tomography (CT) measurement data of the patient is acquired using a CT device having a quantum counting X-ray detector, and the CT measurement data is processed to generate result data, considering a specific information content of the CT measurement data resulting from the use of the quantum counting X-ray detector in acquiring the CT measurement data. The result data is suitable for use in the planning of irradiation of the patient. The result data is provisioned to an interface such that the result data is usable for planning the irradiation of the patient.

Abstract: A computer-implemented method is provided for classifying a malignancy risk of a kidney, in particular a human kidney. Imaging data of an anatomy of a subject patient at least partially includes a representation of a kidney of the subject patient. A first neural network segments at least one region of the kidney representation based on the imaging data. A second neural network detects one or more suspected lesions of the segmented kidney representation. A third neural network classifies the detected suspected lesion with a malignancy risk. The third neural network is a deep profiler.

Abstract: A method is for adapting method steps for finding a result in a CT-based decision support method to the evaluation of LDCT image datasets. In an embodiment of the method, a plurality of reference image datasets are acquired from a plurality of patients. A reference image dataset features at least one CT image dataset from one of the plurality of patients and an LDCT dataset from the patient. Furthermore, method steps for establishing result data are applied to the different image datasets of the reference image datasets. The result data is compared with one another and the method steps for establishing result data are adapted based upon a result of the comparison to the establishing of result data with reference to an LDCT image dataset. An LDCT-based decision support method is also described. Moreover an adaptation device is described. A system for LDCT-based decision support is further described.

Abstract: One or more example embodiments describes a method of performing lung nodule assessment, which method comprises the steps of obtaining a lung scan for a patient from an imaging modality; obtaining a blood panel for that patient from a blood analysis modality; and processing the lung scan and the blood panel in a classifier, which classifier is trained to assess a lung nodule based on the lung scan and the blood panel. The invention further describes a method of training such a classifier, and a lung nodule assessment arrangement.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.

Abstract: A method for exchanging data between an external data source for annotations and an integrated medical data display system, comprises: determining information displayed on a screen of the integrated medical data display system by capturing the screen; and performing at least one of (i) selecting data from the external data source assigned to the determined information and displaying the selected data complementary, or (ii) extracting annotations from the integrated medical data display system based on the determined information and appending the extracted annotations to the external data source for annotations.

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: A framework for personalized recommendation. An image content profile for a current case is generated. One or more auxiliary information representations associated with the current case are further generated. Affinity scores for radiology service providers are then determined by applying service profiles of the radiology service providers, the image content profile and the one or more auxiliary information representations to a trained recommendation engine. The current case is then assigned to one of the radiology service providers based on the affinity scores.

Abstract: A computer-implemented method for providing a second result dataset comprises: receiving and/or determining a first result dataset, wherein the first result dataset is the output of an image-processing system processing a first medical image of a patient; receiving a modified first result dataset, wherein the modified first result dataset is based on a user modification of the first result dataset; receiving a second medical image of the patient, wherein the first medical image and the second medical image are of the same type; determining a second result dataset based on a comparison of the first result dataset and the modified first result dataset, and based on processing the second medical image with the image-processing system; providing the second result dataset.

Abstract: A method for setting a medical imaging protocol includes providing an information data set assigned to a patient. The information data set includes an information about a provisional diagnostic finding regarding the patient. In an embodiment, the method further includes assigning a probability value for a positive finding of the provisional diagnostic finding to the information data set; and automatically setting the medical imaging protocol. The medical imaging protocol is adapted to the provisional diagnostic finding such that an analysis of a result of the medical imaging protocol changes the probability value.

Abstract: Systems and methods for a reduced interaction CT scanning workflow. A sensor is used to capture an image of a patient on the table. Scan parameters are automatically set. A full CT scan is performed without a scout scan. During the full CT scan, the scan parameters are adjusted based on the raw CT measurements from the full CT scan. A radiology report is automatically generated from the results of the full CT scan.

Abstract: Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.

Abstract: A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.