Systems and methods for recommending ablation lines
AssigneeBiosense Webster (Israel) Ltd.
InventorsMatityahu Amit, Yariv Avraham Amos, Liat Tsoref, Stanislav Goldberg
Life of this patent
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Abstract
A system for improving a cardiac ablation procedure includes a recommendation unit configured to provide an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient. The system displays the at least one proposed ablation line on an anatomical map of the anatomy. The recommendation unit comprises a first and a second trained machine-learning model. Both the first and second trained machine-learning models have the same structure.
Claims18
A system for improving a cardiac ablation procedure, the system comprising:
- a recommendation unit configured to provide an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient, said system displaying said at least one proposed ablation line on an anatomical map of said anatomy;
- wherein the recommendation unit comprises:
- a first trained machine-learning model configured to segment the anatomical map;
- a second trained machine-learning model configured to propose the at least one proposed ablation line based, at least in part, on one or more segments output from the first trained machine-learning model;
- and
- a key point filterer configured to compare actual ablation points of a training case to intersection points or lines of neighboring parts on a segmented map of said training case and to select the actual ablation points closest to said intersection points or lines as key points,
- wherein both said first and second trained machine-learning models have a same structure.
A computer-implemented method for improving a cardiac ablation procedure, the method comprising:
- providing an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient, comprising displaying said at least one proposed ablation line on an anatomical map of said anatomy;
- wherein the initial recommendation utilizes:
- a first trained machine-learning model configured to segment the anatomical map;
- a second trained machine-learning model configured to propose the at least one proposed ablation line based, at least in part, on one or more segments output from the first trained machine-learning model;
- and
- a key point filterer configured to compare actual ablation points of a training case to intersection points or lines of neighboring parts on a segmented map of said training case and to select the actual ablation points closest to said intersection points or lines as key points,
- wherein both said first and second trained machine-learning models have a same structure.
Dependencies are inferred from the claim text.