Detection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require laborious rule readaptation for adapting to unseen morphologies. This work explores the adaptation of the the U-Net, a deep learning (DL) network employed for image segmentation, to electrocardiographic data. The model was trained using PhysioNet’s QT database, a small dataset of 105 2-lead ambulatory recordings, while being independently tested for many architectural variations, comprising changes in the model’s capacity (depth, width) and inference strategy (single- and multi-lead) in a fivefold cross-validation manner. This work features several regularization techniques to alleviate data scarcity, such as semi-supervised pre-training with low-quality data labels, performing ECG-based data augmentation and applying in-built model regularizers. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, a U-Net based approach demonstrates to be a viable alternative for this task.

Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fréchet Inception Distance (FID) by 88.6. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively train AI models.

Recently, the risk of thrombus formation in the left atrium (LA) has been assessed through patient-specific computational fluid dynamic (CFD) simulations, characterizing the complex 4D nature of blood flow in the left atrial appendage (LAA). Nevertheless, the vast computational resources and long computing times required by traditional CFD methods prevents its embedding in the clinical workflow of time-sensitive applications. In this study, two distinct deep learning (DL) architectures have been developed to receive the patient-specific LAA geometry as an input and predict the endothelial cell activation potential (ECAP), which is linked to the risk of thrombosis. The first network is based on a simple fully-connected network, while the latter also performs a dimensionality reduction of the variables. Both models have been trained with a synthetic dataset of 210 LAA geometries being able to accurately predict the ECAP distributions with an average error of 4.72% for the fully-connected approach and 5.75% for its counterpart. Most importantly, the obtention of the ECAP predictions was quasi-instantaneous, orders of magnitude faster than conventional CFD.