808 research outputs found
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Purpose: To develop a machine learning model to classify the severity grades
of pulmonary edema on chest radiographs.
Materials and Methods: In this retrospective study, 369,071 chest radiographs
and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women)
patients 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 semi-supervised
model using a variational autoencoder and a pre-trained supervised learning
model using a dense neural network. Receiver operating characteristic curve
analysis was performed on both models.
Results: The area under the receiver operating characteristic curve (AUC) for
differentiating alveolar edema from no edema was 0.99 for the semi-supervised
model and 0.87 for the pre-trained models. Performance of the algorithm was
inversely related to the difficulty in categorizing milder states of pulmonary
edema (shown as AUCs for semi-supervised model and pre-trained 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.
Conclusion: Deep learning models were trained on a large chest radiograph
dataset and could grade the severity of pulmonary edema on chest radiographs
with high performance.Comment: The two first authors contributed equall
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
We propose and demonstrate a novel machine learning algorithm that assesses
pulmonary edema severity from chest radiographs. While large publicly available
datasets of chest radiographs and free-text radiology reports exist, only
limited numerical edema severity labels can be extracted from radiology
reports. This is a significant challenge in learning such models for image
classification. To take advantage of the rich information present in the
radiology reports, we develop a neural network model that is trained on both
images and free-text to assess pulmonary edema severity from chest radiographs
at inference time. Our experimental results suggest that the joint image-text
representation learning improves the performance of pulmonary edema assessment
compared to a supervised model trained on images only. We also show the use of
the text for explaining the image classification by the joint model. To the
best of our knowledge, our approach is the first to leverage free-text
radiology reports for improving the image model performance in this
application. Our code is available at
https://github.com/RayRuizhiLiao/joint_chestxray.Comment: The two first authors contributed equally. To be published in the
proceedings of MICCAI 202
Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios
Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs
New International Guidelines and Consensus on the Use of Lung Ultrasound
Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements
Chest X-Rays Image Classification in Medical Image Analysis
Chest X-Rays image classification is an active research area in medical image analysis as well as computer-aided diagnosis for radiology. The main goal is to improve the quality and productivity of radiologists’ task by providing a computer system for detecting and classifying diseases. A few studies have been conducted in applying machine learning methods to produce a high-quality chest X-ray image classification approach. Some review papers have been published in discussing different aspects of medical image analysis and computer-aided diagnosis for radiology. This paper aims to complement the existing surveys by targeting on the chest X-ray image classification approaches base on the use of machine learning methods. The review begins with a background information of data mining, and the fundamental knowledge of medical image analysis, chest radiography, and machine learning
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