Abstract: A method is described herein. The method is implemented by an optimization engine executed by a processor. The optimization engine receives data that includes performance metrics of mapping and ablation procedures. In turn, the optimization generates procedure expected outcomes for the mapping and ablation procedures based on the data and success predictions for a current ablation procedure utilizing the procedure expected outcomes. The optimization engine, also, outputs an ablation recommendation based on the success predictions.

Abstract: A system and method for aiding a physician in locating the origin of an arrythmia for patients with non-sustained tachycardia are disclosed. The system and method includes receiving data at a machine, from at least one device, the data including information relating to a desired location for performing an ablation, generating, by the machine, an optimal location for performing the ablation based upon the data and inputs, and providing an optimal location for performing the ablation output by the model. The output may include a certainty score for the origins of the arrythmia. The output includes an option to obtain new origins during the ablation procedure.

Abstract: A method is provided. The method includes receiving, by an optimization engine, inputs from previous ablation procedures. The method also includes training, by the optimization engine, a machine learning algorithm of the optimization engine to learn scenarios for the previous ablation procedures. The method also includes generating, by the optimization engine, ablation indices for the scenarios.

Abstract: Systems and methods are disclosed for generating a pace-mapping prediction model. Techniques are provided that utilize a training dataset associated with biometrics of patients' hearts, including electrophysiological data associated with cardiac arrhythmia, pace-mapping datasets, and correlation data measuring the degree of correlation between the pace-mapping datasets and the electrophysiological data. Based on the training dataset, the pace-mapping prediction model is trained to predict a degree of correlation between a patient's electrophysiological data and a pace-mapping dataset. Based on the predicted degree of correlation, a cardiac location in the heart of a patient is predicted as the location for the next pace-mapping. Further systems and methods are disclosed for generating a pacing maneuver prediction model. The pacing maneuver prediction model is trained to predict interval measurement based on a pacing maneuver obtained during cardiac pace-mapping.

Abstract: A system that includes a memory and a processor is provided. The memory stores processor executable program instructions of a mapping engine. The processor executes the program instructions of the mapping engine to cause the system to receive information with respect to initiating a medical procedure for a patient. The information includes health demographics and biometric data of the patient. The processor executes the program instructions of the mapping engine to also cause the system to predict workflow preferences for users performing the medical procedure from the information by employing a model of the mapping engine and to generate, using the workflow preferences, image display options to the users for presentation by the system.

Abstract: A method and apparatus for implementing scar tissue identification using a processor coupled to a memory is disclosed. The method and apparatus receive a first modality and a second modality. The first modality is of a first type. The second modality is of a second type, which is different from the first type. Each of the first modality and the second modality respectively describe organic tissue of a patient according to the first and second types. The method and apparatus cross reference the first modality and the second modality and generates improved image data for the first modality based on the cross referencing. The image data includes enhanced accuracy over or higher resolution than original data of the first modality.