The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

Lung ultrasound is an increasingly utilized non-invasive imaging modality for assessing lung condition but interpreting it can be challenging and depends on the operator’s experience. To address these challenges, this work proposes an approach that combines artificial intelligence (AI) with feature-based signal processing algorithms. We introduce a specialized deep learning model designed and trained to facilitate the analysis and interpretation of lung ultrasound images by automating the detection and location of pulmonary features, including the pleura, A-lines, B-lines, and consolidations. Employing Convolutional Neural Networks (CNNs) trained on a semi-automatically annotated dataset, the model delineates these pulmonary patterns with the objective of enhancing diagnostic precision. Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second with accuracy levels of 89% for consolidation, 92% for B-lines, 66% for A-lines, and 92% for detecting normal lungs compared with an expert opinion.

Lung ultrasound is an increasingly utilized non-invasive imaging modality for assessing the lung condition, but interpreting it can be challenging and depends on the operator experience. To address these challenges, this work proposes an approach that combines artificial intelligence (AI) with feature-based signal processing algorithms. We introduce a specialized deep learning model designed and trained to facilitate the analysis and interpretation of lung ultrasound images, by automating the detection and location of pulmonary features, including the pleura, A-lines, B-lines and consolidations. Employing Convolutional Neural Networks (CNNs) trained on a semi-automatically annotated dataset, the model delineates these pulmonary patterns, with the objective of enhancing diagnostic precision. Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second withl accuracies of 89% for consolidation, 92% for B-lines and 66% in case of A-lines compared with an expert opinion.

Ultrasound array imaging frequently employs a coupling medium to facilitate wave transmission from the transducer to the target component. Surface echoes, identified by their high-amplitude peaks, are crucial for determining the Time of Flight (TOF) in each channel, which is essential for deriving imaging focal laws. Accurate TOF measurement is vital in numerous applications, such as Non-Destructive Testing (NDT) and medical imaging. Conventional methods, such as threshold crossing and peak search, are highly sensitive to noise and spurious signals, therefore, more robust estimation techniques are needed. This study explores the application of a deep 3D Convolutional Neural Network (CNN) to detect surface echoes in Full Matrix Capture (FMC) ultrasound data. The CNN was trained on signals obtained with a matrix array and a set of reference components, utilizing a robotic arm setup to ensure precise probe positioning. Theoretical TOFs were computed based on the setup geometry to generate labeled training data. Test results indicated that the CNN model, which we have called DeepEcho3D, closely aligned with the ground truth and significantly reduced TOF estimation outliers (up to 98%) compared to traditional methods, demonstrating its potential for improved accuracy in surface echo detection.