An XR-based system (virtual reality, augmented reality, or mixed reality system), is provided to visualize and resolve at least one condition of a subject. A dynamic virtual representation of the subject's body is generated based on the captured physical traits and movement of the subject's body is captured by at least one motion tracking device, and rendered in the extended reality environment. The dynamic virtual representation is synchronized with the movement of the body of the subject, generating a virtual representation of at least one condition of the subject in response to one or more inputs, overlaying or rendering the virtual representation of the condition of the subject on the virtual representation of the body of the subject, and receiving and processing one or more inputs representing one or more attributes of the condition to adjust the virtual representation of the condition of the subject in the extended reality environment.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

A method includes, receiving: (i) a selected three-dimensional (3D) section that has been ablated in a patient organ in accordance with a specified contour, and (ii) a dataset, which is indicative of a set of lesions formed during ablation of the selected 3D section. The selected 3D section is transformed into a two-dimensional (2D) map, and checking, on the 2D map, whether the set of lesions covers the specified contour.

A method includes, receiving: (i) a selected three-dimensional (3D) section that has been ablated in a patient organ in accordance with a specified contour, and (ii) a dataset, which is indicative of a set of lesions formed during ablation of the selected 3D section. The selected 3D section is transformed into a two-dimensional (2D) map, and checking, on the 2D map, whether the set of lesions covers the specified contour.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

Systems and methods of epidemiological modeling using machine learning are provided, and can include receiving values for an occurrence of the infectious disease during a first time period, generating, from a model trained by a machine learning system, predictions for the occurrence of the infectious disease over a second time period, performing, by a simulator using the predictions, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period, and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.

Systems and methods of epidemiological modeling using machine learning are provided, and can include receiving values for an occurrence of the infectious disease during a first time period, generating, from a model trained by a machine learning system, predictions for the occurrence of the infectious disease over a second time period, performing, by a simulator using the predictions, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period, and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.