ArticleComputers in biology and medicine2021
Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 84 citations in OpenAlex.
- MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique.Journal of imaging informatics in medicine · 2026Article
- Lung Imaging in Acute Hypoxemic Respiratory Failure: From Physics to Bedside Applications.Journal of clinical medicine · 2026Review
- Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.Journal of ultrasound · 2025Article
- Spinal tissue identification using a Forward-oriented endoscopic ultrasound technique.Biomedical engineering letters · 2025Article
- Cross-domain lung opacity detection via adversarial learning and box fusion.Scientific reports · 2024Article
- Thoracic Ultrasound in Others Scenarios: An Expanding Tool.Open respiratory archives · 2024Review
- Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.Diagnostics (Basel, Switzerland) · 2024Article
- Classification of lung pathologies in neonates using dual-tree complex wavelet transform.Biomedical engineering online · 2023Article
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Perceptive SARS-CoV-2 End-To-End Ultrasound Video Classification through X3D and Key-Frames Selection.Bioengineering (Basel, Switzerland) · 2023Article
- SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.International journal of machine learning and cybernetics · 2023Article
- Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.Diagnostics (Basel, Switzerland) · 2023Article
- State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.Ultrasound in medicine & biology · 2022Review
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia.Scientific reports · 2022Article
- Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery.Journal of personalized medicine · 2022Article
- Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images.Sensors (Basel, Switzerland) · 2022Article
- Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus.Journal of imaging · 2022Article
- Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.BMC medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be useful for diagnosis using both computed tomography (CT) scans and chest X-rays (CXR), whereby the former typically yields more accurate results. However, the pivoting function of a CT scan during the pandemic presents several drawbacks, including high cost and cross-contamination problems. Radiation-free lung ultrasound (LUS) imaging, which requires high expertise and is thus being underutilised, has demonstrated a strong correlation with CT scan results and a high reliability in pneumonia detection even in the early stages. In this study, we developed a system based on modern DL methodologies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. Using a reliable dataset comprising ultrasound clips originating from linear and convex probes in 2908 frames from 450 hospitalised patients, we conducted an investigation into detecting Covid-19 patterns and ranking them considering two severity scales. This study differs from other research projects by its novel approach involving four and seven classes. Patients admitted to the ED underwent 12 LUS examinations in different chest parts, each evaluated according to standardised severity scales. We adopted residual convolutional neural networks (CNNs), transfer learning, and data augmentation techniques. Hence, employing methodological hyperparameter tuning, we produced state-of-the-art results meeting F1 score levels, averaged over the number of classes considered, exceeding 98%, and thereby manifesting stable measurements over precision and recall.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.