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.

Methods and systems for training a model using machine learning for automatically distributing medical imaging studies to radiologists. One method includes receiving one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image. The method further includes receiving radiologist metadata for each one of the plurality of radiologists, generating a state representation of the image metadata and the radiologist metadata, and providing the state representation to the model. The method further includes assigning, with the model, at least one of the one or more medical images to one of the plurality of radiologists, calculating feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, and adjusting the model based on the feedback.

Methods and systems for training a model using machine learning for automatically distributing medical imaging studies to radiologists. One method includes receiving one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image. The method further includes receiving radiologist metadata for each one of the plurality of radiologists, generating a state representation of the image metadata and the radiologist metadata, and providing the state representation to the model. The method further includes assigning, with the model, at least one of the one or more medical images to one of the plurality of radiologists, calculating feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, and adjusting the model based on the feedback.

Computer implemented method of generating a dental design, comprising: a) capturing a facial image comprising a head of a patient and a smile; b) displaying it as a first image; c) capturing a 3D intraoral scan; d) aligning the 3D scan to the head; e) determining bounding boxes in the 3D scan, each comprising a single tooth; f) showing a view of the 3D scan and the bounding boxes as a second image; g) showing the bounding boxes as overlay on the first image; i) allowing the bounding boxes to be resized/repositioned; ii) defining a limited set of parameters to characterize the tooth inside the bounding box, and searching a number of candidate matching teeth from a 3D digital library of teeth, and proposing a candidate matching tooth; iii) overlaying the first image with a digital representation of the proposed candidate matching tooth from the digital library.

Computer implemented method of generating a dental design, comprising: a) capturing a facial image comprising a head of a patient and a smile; b) displaying it as a first image; c) capturing a 3D intraoral scan; d) aligning the 3D scan to the head; e) determining bounding boxes in the 3D scan, each comprising a single tooth; f) showing a view of the 3D scan and the bounding boxes as a second image; g) showing the bounding boxes as overlay on the first image; i) allowing the bounding boxes to be resized/repositioned; ii) defining a limited set of parameters to characterize the tooth inside the bounding box, and searching a number of candidate matching teeth from a 3D digital library of teeth, and proposing a candidate matching tooth; iii) overlaying the first image with a digital representation of the proposed candidate matching tooth from the digital library.

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.

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.

Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.

Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.