EchoCardiography Benchmark (ECBench)

• ECBench is a comprehensive benchmark suite that standardizes datasets, evaluation protocols, and methodologies for ML applications in echocardiography.
• It encompasses tasks such as view recognition, segmentation, regression, and motion analysis using diverse architectures from CNNs to transformers.
• The benchmark supports clinical translation with robust evaluation metrics and synthetic data, ensuring reproducible results and handling real-world image variability.

The EchoCardiography Benchmark (ECBench) defines the standards, datasets, and methodologies for comprehensive quantitative and qualitative evaluation of machine learning models in echocardiography. ECBench encompasses classic classification and segmentation benchmarks, unsupervised and self-supervised objectives, view recognition, cardiac motion quantification, phase detection, and clinically interpretable diagnostic endpoints. The benchmark suite reflects the unique multi-view, multi-modality, and real-world variability in echocardiographic imaging, supporting reproducible comparisons and establishing baselines for both model development and clinical translation.

1. Dataset Composition and Scope

The foundation of ECBench is the curation and release of large, heterogenous, and expertly annotated datasets that represent the breadth of echocardiographic practice. Early implementations leveraged proprietary archives: one prominent dataset consists of 834,267 transthoracic echocardiogram images from 267 patients (20–96 years; 51% female; 26% obese), acquired over nearly two decades and covering diverse manufacturers, imaging settings, and clinical pathologies (Madani et al., 2017). These data span a wide range of technical parameters (zoom, depth, sector width, gain, Doppler/color/strain, 3D modes) and clinical variation (valve disease, LV hypertrophy, ejection fraction, systole/diastole). Subsequent public datasets such as HMC-QU focus on MI detection and segmentation, offering videos and ground-truth masks even from low-quality images (Degerli et al., 2020, Degerli et al., 2021), and newer synthetic datasets provide fully characterized motion fields via FE-based simulation (Mukherjee et al., 6 Sep 2024).

Recent unified suites, exemplified by CardioBench, aggregate up to eight public datasets (EchoNet-Dynamic, EchoNet-Pediatric, EchoNet-LVH, SegRWMA, CardiacNet, CAMUS, HMC-QU, TMED-2) spanning regression, classification, segmentation, and view recognition tasks (Taratynova et al., 1 Oct 2025). Benchmarking thus extends from clinical endpoint prediction (ejection fraction, structural measurements, MI/stroke risk) to technical robustness under acquisition shift and interpretability.

2. Model Architectures and Baseline Methods

ECBench enables systematic comparison across diverse model architectures, ranging from classic convolutional neural networks (CNNs) to graph-based and transformer-based models, and, more recently, to multimodal and cross-modal foundation models. The original view classification benchmark used a VGG16-inspired CNN with six sequential 3×3 convolutional layers, max pooling, batch normalization, dropout, and two large fully connected layers culminating in a softmax output (for 12–15 standard views). The network is expressed for a convolutional layer as $y = \mathrm{ReLU}(W * x + b)$, with all Conv/FC activations ReLU except the terminal softmax (Madani et al., 2017).

Benchmark advances include:

• Segmentation and EF Quantification: Graph Convolutional Networks (GCNs) for anatomical keypoint detection and direct EF regression, outperforming semantic segmentation pipelines in efficiency and robustness (Dice ≈ 92%, mean keypoint error ≈ 2.2%) (Thomas et al., 2022).
• Multi-View and Feature Engineering: Active Polynomial-based segment tracking with ML classifiers fusing A4C and A2C views for MI detection (Sensitivity ≈ 91%) (Degerli et al., 2021).
• Unsupervised Latent Motion Modeling: Self-supervised architectures decomposing videos into static anatomy and low-dimensional dynamic motion trajectories, enabling annotation-free ED/ES detection with frame-level MAE ≈ 2–3 frames (Yang et al., 7 Jul 2025).
• Synthetic Data for Motion Validation: Finite element biomechanics simulations used to generate benchmark synthetic images, enabling validation of algorithmic displacement and strain tracking against known ground-truth fields (Mukherjee et al., 6 Sep 2024).
• Foundation and Multimodal Models: Vision Transformers pre-trained via masked reconstruction (e.g., EchoAI), contrastive vision-LLMs (EchoCLIP, PanEcho), and cross-modal ECG–ECHO models employing probabilistic student-teacher training (EchoingECG) for ECHO endpoint stratification by ECG (Dahlan et al., 2023, Gao et al., 30 Sep 2025, Taratynova et al., 1 Oct 2025).

3. Benchmark Tasks and Evaluation Protocols

ECBench covers a suite of tasks encompassing both classical and modern metrics:

Task Domain	Example Targets/Endpoints	Representative Metrics
Classification	View recognition (15 views), MI, STEMI, ASD, PAH	Accuracy, F-score, AUC
Segmentation	LV wall, RWMA, Doppler envelopes	Dice, Mean Keypoint Error, IoU
Regression	Ejection fraction, IVSd, LVIDd, LVPWd	MAE, MAPE, RMSE, R² for EF, Pearson r for alignment
Motion/Phase	Cardiac phase detection (ED/ES), Strain	MAE (frames/ms), RMS error, Jacobian determinant
Quality Assessment	Image clarity, depth-gain, foreshortening	MAE (vs. expert ratings), regression accuracy (%)

Benchmarks such as CardioBench provide zero-shot, probing (linear, kNN), and alignment protocols: for zero-shot tasks, text and video embeddings are compared via cosine similarity for class or numeric axis prediction; probes quantify how linearly accessible clinical signal is from representations; and alignment measures (UMAP, CCA, CKA, ARI) assess physiological and semantic structure capture (Taratynova et al., 1 Oct 2025, Jeon et al., 2023).

Model interpretability is baked into many benchmarks: saliency maps, occlusion/testing of clinically relevant regions, and t-SNE clustering of features that visually correspond to clinical view separations are used to ensure decision-making aligns with clinical reasoning (Madani et al., 2017).

4. Clinical and Technical Relevance

High-performance benchmarks are established for real-world clinical relevance. For example, a deep CNN for view recognition surpassed board-certified echocardiographers (91.7% vs. 70.2–83.5% on still images, AUC ≈ 0.996), with aggregated video classification reaching 97.8% accuracy (Madani et al., 2017). Segmentation and regression methods (EchoGraphs) achieve state-of-the-art EF estimation (MAE ≈ 4.0, R² ≈ 0.81), while maintaining low inference latency, critical for point-of-care systems (Thomas et al., 2022).

The benchmarks stress generalizability under real-world variation by ensuring test splits are independent (e.g., no paper overlap), multi-institution inclusion, and robustness against acquisition variability and low-quality images. Synthetic data benchmarks validate algorithmic reproducibility against known biomechanical truths, addressing vendor and pipeline variability (Mukherjee et al., 6 Sep 2024). Image quality metrics (anatomical visibility, cavity clarity, depth-gain, foreshortening) are defined to automate and standardize acquisition feedback in clinical routines (Labs et al., 2022).

Multimodal benchmarks now allow evaluation of cross-modal prediction (e.g., ECG→ECHO) using probabilistic frameworks modeling epistemic uncertainty, with performance stratified by uncertainty levels (Gao et al., 30 Sep 2025). Multimodal LLM frameworks (Med-RwR) demonstrate reasoning-under-retrieval paradigms that directly incorporate external evidence at inference, achieving substantial domain-adaptive gains (8.8% accuracy gain on ECBench from proactive retrieval in underrepresented domains) (Wang et al., 21 Oct 2025).

5. Limitations, Generalizability, and Future Directions

ECBench benchmarks acknowledge residual challenges. Even state-of-the-art models can misclassify clinically similar views (for example, A2C vs. A3C), motivating finer-granularity labels or structured sub-category incorporation (Madani et al., 2017). The scalability of benchmark construction is addressed with pseudo labeling and self-supervised/unsupervised paradigms that remove annotation bottlenecks while providing annotation-free phase detection that rivals supervised methods (Yang et al., 7 Jul 2025).

A recognized limitation is the need for increased public, multi-institutional, multi-modal datasets reflecting pediatric, rare disease, and low-resource environments. The expansion into synthetic data, multimodal cross-supervision, and integration of external retrieval resources in MLLMs points toward a future where benchmarks capture both rare and common clinical scenarios, as well as the alignment between imaging, language, and other biosignal domains.

Emerging directions include robust evaluation pipelines supporting zero-shot and probing protocols, explicitly quantifying temporal and anatomic structure, multi-label diagnosis, model uncertainty, and alignment with physiologic axes. The integration of multimodal foundation models and benchmarking of model adaptability, label efficiency, and calibration in small-sample clinical settings are increasingly prioritized (Taratynova et al., 1 Oct 2025, Al-Masud et al., 29 Sep 2025, Wang et al., 21 Oct 2025).

A plausible implication is that future iterations of ECBench will incorporate adaptive retrieval and agentic reasoning frameworks, more granular weak supervision, and domain adaptation techniques—potentially setting the agenda for comprehensive, reliably generalizable, and clinically validated echocardiography AI benchmark suites.