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

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-...

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Bibliographic Details
Published in:Applied sciences Vol. 14; no. 24; p. 11930
Main Authors: Muñoz, Mario, Rubio, Adrián, Cosarinsky, Guillermo, Cruza, Jorge F., Camacho, Jorge
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2024
Subjects:
Acoustics
Algorithms
Artificial intelligence
artificial intelligence (AI)
B-line
Classification
convolutional neural network
COVID-19
Deep learning
Dengue fever
lung ultrasound (LUS)
Machine learning
Medical imaging equipment
Neural networks
Pandemics
pleura
Pneumonia
Signal processing
Skin cancer
Ultrasonic imaging
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary: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.
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content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app142411930