Work

Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis

Year: 2024

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app142411930

Authors: Mario Muñoz, Adrián Rubio, Guillermo Cosarinsky, Jorge F.Cruza, Jorge Camacho

Institutions: Consejo Superior de Investigaciones Científicas, Universidad de Alcalá, Instituto de Tecnologías Físicas y de la Información “Leonardo Torres Quevedo”

Language: English

Topic: Ultrasound in Clinical Applications

Subfield: Critical Care and Intensive Care Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,728