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.