The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias. | J Physiol;602(18): 4645-4647, 2024 Sep. | MEDLINE

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The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias.

Amini, Negin.

Affiliation

Amini N; Physiology Department, McGill University, Canada.

J Physiol ; 602(18): 4645-4647, 2024 Sep.

Article in En | MEDLINE | ID: mdl-39252427

Subject(s)

Arrhythmias, Cardiac; Deep Learning; Humans; Arrhythmias, Cardiac/physiopathology; Risk Assessment/methods

Key words

CECT; CNN; automation; deep learning; ventricular ablation; ventricular tachycardia

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Arrhythmias, Cardiac / Deep Learning Limits: Humans Language: En Journal: J Physiol Year: 2024 Document type: Article

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Arrhythmias, Cardiac / Deep Learning Limits: Humans Language: En Journal: J Physiol Year: 2024 Document type: Article