ArticleRadiology. Artificial intelligence2021
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs.BMC medical informatics and decision making · 2025Article
- Non-Inferiority Analysis of Electrocardiography Analysis Application vs. Point-of-Care Ultrasound for Screening Left Ventricular Dysfunction.Yonsei medical journal · 2025Article
- Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.Scientific reports · 2024Article
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.Radiology. Artificial intelligence · 2024Article
- Explainable AI to identify radiographic features of pulmonary edema.Radiology advances · 2024Article
- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.Medical image learning with limited and noisy data : second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MILLanD (Workshop) : (2nd : 2023 : Vancouver, B... · 2023Article
- A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays.Critical care (London, England) · 2023Article
- A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in critical illness and its impact on patient care: a comprehensive review.Frontiers in medicine · 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
- Article
- Artificial Intelligence in Critical Care Medicine.Critical care (London, England) · 2022Review
- Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.Scientific reports · 2021Article
- Quantifying Pulmonary Edema on Chest Radiographs.Radiology. Artificial intelligence · 2021Article
- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Multimodal Representation Learning via Maximization of Local Mutual Information.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
- Imaging for the detection and evaluation of pulmonary edema: state of the art.Radiologia brasileiraArticle
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6 authors.
Funding
Abstract
purposeTo develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. MATERIALS AND
methodsIn this retrospective study, 369 071 chest radiographs and associated radiology reports from 64 581 patients (mean age, 51.71 years; 54.51% women) from the MIMIC-CXR chest radiograph dataset were included. This dataset was split into patients with and without congestive heart failure (CHF). Pulmonary edema severity labels from the associated radiology reports were extracted from patients with CHF as four different ordinal levels: 0, no edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema. Deep learning models were developed using two approaches: a semisupervised model using a variational autoencoder and a pretrained supervised learning model using a dense neural network. Receiver operating characteristic curve analysis was performed on both models.
resultsThe area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0.99 for the semisupervised model and 0.87 for the pretrained models. Performance of the algorithm was inversely related to the difficulty in categorizing milder states of pulmonary edema (shown as AUCs for semisupervised model and pretrained model, respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus 1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and 3 versus 2, 0.88 and 0.63.
conclusionDeep learning models were trained on a large chest radiograph dataset and could grade the severity of pulmonary edema on chest radiographs with high performance.
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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.