What is the clinical evidence from this study?

Study design: Review. Population: Heart disease. Intervention: Deep learning algorithms.

What question did this study set out to answer?

To comprehensively review heart-related data modalities and deep learning algorithms for heart disease anomaly detection while evaluating performance disparities between clinical and wearable devices.

July 7, 2026Artificial Intelligence ReviewOpen Access

Deep learning for heart disease anomaly detection: performance factors and algorithms

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Key result

Deep learning advances cardiac anomaly detection, though data rarity and inconsistent evaluation constrain performance.

Why the study?

Heart disease remains a leading cause of mortality, making effective detection methods essential as techniques evolve from clinical settings to wearable and remote sensing technologies.

Key Points

To comprehensively review heart-related data modalities and deep learning algorithms for heart disease anomaly detection while evaluating performance disparities between clinical and wearable devices.
Categorized heart signal modalities, clinical scopes, and deep learning architectures including convolutional neural networks and recurrent neural networks.
Conducted comparative experiments evaluating deep learning model performance across continuous physiological signals derived from clinical and wearable device datasets.
Identified algorithmic evolution in anomaly detection transitioning from manual feature engineering to end-to-end deep learning and hybrid architectures.
Demonstrated performance disparities between clinical-grade signals and wearable sensor data, outlining key challenges in signal quality, data heterogeneity, and model evaluation.

PICO

Population

Heart disease

Intervention / Comparator

Deep learning algorithms

This comprehensive review provides a structured taxonomy of deep learning tasks for heart disease anomaly detection and highlights the critical performance gaps between clinical and wearable sensor data.

Limitations

Data rarity and lack of diversity in data collection sources
Annotation reliability and inter-expert disagreement
Label ambiguity
Publicly accessible datasets remain scarce, often suffering from limited diversity and annotation inconsistencies
Algorithm design must balance computational cost, explainability and performance
Inconsistencies in evaluation strategies and the lack of standardised metrics hinder fair comparisons between models

Implication

Barriers to reliable anomaly detection may constrain wearable integration into practice; leaves open the value of standardized benchmarks for advancing deep learning in cardiology.

Abstract

Heart disease is a prevalent concern for individuals in every age group, as it significantly impacts their health and remains a leading cause of mortality today. An effective heart disease detection method is essential for people to assess their heart conditions accurately. Over the past decades, heart disease detection techniques, whether based on machine learning or deep learning, have evolved considerably—from relying on handcrafted features to automatically learned features, from using single models to hybrid models and from applications confined to clinical settings to deployment in daily life through wearable and remote sensing technologies. This paper provides a comprehensive review of various types of heart-related data and popular deep learning algorithms employed for heart disease detection. In addition, we conduct a comparative experiment that showcases the existing gaps between clinical and wearable signals and highlights the potential for further research on wearable devices. This paper also identifies the key challenges, emphasises the primary factors affecting detection performance and discusses potential solutions.

Discussion

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Cite This Study

Zhou et al. (2026) conducted a review in Heart disease. Deep learning algorithms was evaluated. Deep learning algorithms for heart disease anomaly detection have evolved considerably, though performance remains constrained by data rarity, annotation reliability, and evaluation inconsistencies.

synapsesocial.com/papers/6a4c9754331bc25c9e5f453c https://doi.org/10.1007/s10462-026-11633-3

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Implication

Barriers to reliable anomaly detection may constrain wearable integration into practice; leaves open the value of standardized benchmarks for advancing deep learning in cardiology.

Deep learning for heart disease anomaly detection: performance factors and algorithms

Key Points

PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

Deep learning for heart disease anomaly detection: performance factors and algorithms

Key Points

PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

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