ArticleComputers in biology and medicine2021
An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound.
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 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- 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
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- New lung ultrasound system for rapid triage of pulmonary disease without a radiologist or sonographer.BMC pulmonary medicine · 2025Article
- Revolutionary hybrid ensembled deep learning model for accurate and robust side-channel attack detection in cloud computing.Scientific reports · 2025Article
- A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics.International journal of environmental research and public health · 2024Article
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- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?-Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction.Neural computing & applications · 2023Article
- A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.Frontiers in plant science · 2023Article
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7-12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.
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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.