This review surveys deep learning applications in cardiology, covering structured data, signal, and imaging modalities.
Structured data: Deep learning models, particularly RNNs and AEs, have been used to predict cardiovascular disease risk, diagnose heart failure, and estimate vital signs like blood pressure from electronic health records. These models can capture complex temporal patterns and learn robust feature representations from structured data.
Signals: Deep learning, mainly CNNs and AEs, has been extensively applied to electrocardiogram (ECG) analysis for arrhythmia detection, atrial fibrillation classification, and other cardiac signal processing tasks. Techniques like spectrogram conversion and transfer learning have enabled effective feature extraction from ECG and other biosignals.
Imaging: Deep learning, especially CNNs, has demonstrated state-of-the-art performance in segmenting cardiac structures like the left and right ventricles from medical images like MRI and CT. These models can accurately delineate anatomical boundaries and quantify functional parameters, aiding diagnosis and treatment planning.
The review also discusses the specific advantages and limitations of deep learning in cardiology, highlighting its potential to transform medical practice from an art to a data-driven science. Key directions for future research include improving model interpretability, leveraging multimodal data, and deploying deep learning systems in real-world clinical settings.
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by Paschalis Bi... at arxiv.org 04-05-2024
https://arxiv.org/pdf/1902.11122.pdfDeeper Inquiries