Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.

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.

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.

A system and method for determining a health outcome, comprising: receiving first and second images or videos of a wound of a patient; comparing the images or videos to detect a characteristic of the wound, the characteristic including an identification of a change in the wound; receiving at least one non-image or non-video data input that includes data about the patient; executing a machine learning algorithm comprising a dataset of images or videos to analyze the identified change in the wound and to correlate at least one first image or video and at least one second image or video with the at least one non-image or non-video data input and to train the machine learning algorithm with the identification of a change in the wound; and generating a medical outcome prediction regarding a status and recovery of the patient in response to correlating the at least one additional input with the first and second images or videos.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

A computer apparatus and methods generate multi-parametric color composite images from multi-echo chemical shift encoded (CSE) MRI. Some embodiments use inherently co-registered images (i.e., image maps) that are combined into a single intuitive composite color image. The color (e.g., brightness, hue, and/or saturation) reflects in part the water and fat content, and other properties, particularly T2* relaxation (related to magnetic susceptibility) of the tissue.

A computer apparatus and methods generate multi-parametric color composite images from multi-echo chemical shift encoded (CSE) MRI. Some embodiments use inherently co-registered images (i.e., image maps) that are combined into a single intuitive composite color image. The color (e.g., brightness, hue, and/or saturation) reflects in part the water and fat content, and other properties, particularly T2* relaxation (related to magnetic susceptibility) of the tissue.

System and method for generating personalized nutrition prescriptions and modifying the personalized nutrition prescriptions based on feedback. Exemplary implementations may: receive user input defining biometric information, physique goals associated with individual users, and/or other information; provide the nutrition prescriptions to the individual users based on the biometric information and the physique goals; provide the nutrition prescriptions to the individual users; obtain feedback from the individual users; modify the nutrition prescriptions based on comparison between the obtained feedback and projected progress towards the physique goals associated with the individual users; provide the modified nutrition prescriptions to the individual users; and/or other exemplary implementations.