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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11930 |
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| author | Mario Muñoz Adrián Rubio Guillermo Cosarinsky Jorge F. Cruza Jorge Camacho |
| author_facet | Mario Muñoz Adrián Rubio Guillermo Cosarinsky Jorge F. Cruza Jorge Camacho |
| author_sort | Mario Muñoz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6a8a948927c1496bbf658134e5ddc42d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6a8a948927c1496bbf658134e5ddc42d2025-08-20T02:57:12ZengMDPI AGApplied Sciences2076-34172024-12-0114241193010.3390/app142411930Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted DiagnosisMario Muñoz0Adrián Rubio1Guillermo Cosarinsky2Jorge F. Cruza3Jorge Camacho4Institute for Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, SpainInstitute for Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, SpainInstitute for Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, SpainInstitute for Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, SpainInstitute for Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, SpainLung 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.https://www.mdpi.com/2076-3417/14/24/11930lung ultrasound (LUS)artificial intelligence (AI)convolutional neural networkdeep learningpleuraB-line |
| spellingShingle | Mario Muñoz Adrián Rubio Guillermo Cosarinsky Jorge F. Cruza Jorge Camacho Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis Applied Sciences lung ultrasound (LUS) artificial intelligence (AI) convolutional neural network deep learning pleura B-line |
| title | Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis |
| title_full | Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis |
| title_fullStr | Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis |
| title_full_unstemmed | Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis |
| title_short | Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis |
| title_sort | deep learning based algorithms for real time lung ultrasound assisted diagnosis |
| topic | lung ultrasound (LUS) artificial intelligence (AI) convolutional neural network deep learning pleura B-line |
| url | https://www.mdpi.com/2076-3417/14/24/11930 |
| work_keys_str_mv | AT mariomunoz deeplearningbasedalgorithmsforrealtimelungultrasoundassisteddiagnosis AT adrianrubio deeplearningbasedalgorithmsforrealtimelungultrasoundassisteddiagnosis AT guillermocosarinsky deeplearningbasedalgorithmsforrealtimelungultrasoundassisteddiagnosis AT jorgefcruza deeplearningbasedalgorithmsforrealtimelungultrasoundassisteddiagnosis AT jorgecamacho deeplearningbasedalgorithmsforrealtimelungultrasoundassisteddiagnosis |