ArticleDiagnostic and interventional radiology (Ankara, Turkey)2021
Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
- COVID-19 pneumonia: lessons learned, challenges, and preparing for the future.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.Scientific reports · 2022Article
- CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image.Neural computing & applications · 2022Article
- Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.Computational intelligence and neuroscience · 2022Article
- Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.IEEE access : practical innovations, open solutions · 2022Article
- Breathing, speaking, coughing or sneezing: What drives transmission of SARS-CoV-2?Journal of internal medicine · 2021Review
Corrections and comments
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Authors and funding
19 authors at 4 institutions in 1 country.
Funding
Abstract
purposeChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.
methodsA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.
resultsFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.
conclusionChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.
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