Anatomic Point–Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs

2021 ◽  
Author(s):  
Feng Li ◽  
Samuel G. Armato ◽  
Roger Engelmann ◽  
Thomas Rhines ◽  
Jennie Crosby ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chest Radiographs ◽  
Lung Region

scholarly journals Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies

2021 ◽  
Vol 6 ◽  
pp. 309
Author(s):  
Paul Mwaniki ◽  
Timothy Kamanu ◽  
Samuel Akech ◽  
M. J. C Eijkemans
Keyword(s):  
Machine Learning ◽  
Epidemiological Studies ◽  
Chest Radiographs ◽  
World Health ◽  
Data Sets ◽  
Learning Models ◽  
Middle Income ◽  
Training Models ◽  
Model Training ◽  
Machine Learning Models

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.

scholarly journals Comparative Performance Analysis of Machine Learning Classifiers in Detection of Childhood Pneumonia Using Chest Radiographs

2013 ◽  
Vol 18 ◽  
pp. 2579-2582 ◽  
Author(s):  
Rafael T. Sousa ◽  
Oge Marques ◽  
Fabrizzio Alphonsus A.M.N. Soares ◽  
Iwens I.G. Sene ◽  
Leandro L.G. de Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Chest Radiographs ◽  
Comparative Performance ◽  
Childhood Pneumonia ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Chest radiographs and machine learning – Past, present and future

2021 ◽  
Author(s):  
Catherine M Jones ◽  
Quinlan D Buchlak ◽  
Luke Oakden‐Rayner ◽  
Michael Milne ◽  
Jarrel Seah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chest Radiographs

scholarly journals Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units

2021 ◽  
Vol 8 ◽  
Author(s):  
Kumiko Tanaka ◽  
Taka-aki Nakada ◽  
Nozomi Takahashi ◽  
Takahiro Dozono ◽  
Yuichiro Yoshimura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Pleural Effusion ◽  
Diagnostic Accuracy ◽  
Intensive Care Units ◽  
Area Under The Curve ◽  
Chest Radiographs ◽  
Data Set ◽  
Portable Chest ◽  
Icu Physicians

Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting.Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians.Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47).Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians.

scholarly journals Image and structured data analysis for prognostication of health outcomes in patients presenting to the Emergency Department during the COVID-19 pandemic

2021 ◽  
Author(s):  
Liam Butler ◽  
Ibrahim Karabayir ◽  
Mohammad Samie Tootooni ◽  
Majid Afshar ◽  
Ari Goldberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Intensive Care ◽  
Deep Learning ◽  
Clinical Data ◽  
Chest Radiographs ◽  
Gradient Boosting ◽  
Learning Models ◽  
Icu Admission ◽  
Light Gradient

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also lead to death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, need for intensive care unit (ICU) admission as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. Purpose: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to adverse outcomes following ED admission. Materials and Methods: Light Gradient Boosting Machines (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using 15 the most important variables to increase applicability of the models in clinical settings. To predict risk of the aforementioned health outcome events, transfer learning from the CheXNet model was implemented on our data as well. This research utilized clinical data and chest radiographs of 3571 patients 18 years and older admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. Main Findings: Our research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)) and predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), ICU admission (AUC = 0.675 (0.620-0.713)), and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data. Principal Conclusions: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

scholarly journals Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

2021 ◽  
Vol 3 (3) ◽  
pp. 199-217 ◽  
Author(s):  
Michael Roberts ◽  
◽  
Derek Driggs ◽  
Matthew Thorpe ◽  
Julian Gilbey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Model Development ◽  
Standard Of Care ◽  
Ct Images ◽  
Chest Radiographs ◽  
Great Promise ◽  
Quality Model ◽  
Potential Clinical Utility ◽  
New Machine

AbstractMachine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

scholarly journals COVID-19 Detection From Chest Radiographs Using Machine Learning and Convolutional Neural Networks

2020 ◽  
Author(s):  
Andrew C. Li ◽  
David T. Lee ◽  
Kristoff K. Misquitta ◽  
Kaiji Uno ◽  
Sasha Wald
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
False Negative ◽  
False Negative Rate ◽  
Turnaround Time ◽  
Chest Radiographs ◽  
Machine Learning Model ◽  
High False Negative Rate ◽  
Machine Learning Approach ◽  
Polymerase Chain

Accurate and efficient diagnosis of potential COVID-19 patients is vital in the fight against the current pandemic. However, even the gold-standard COVID-19 test, reverse transcription polymerase chain reaction, suffers from a high false negative rate and a turnaround time of up to one week, preventing the infected from accessing the timely care they require, and impeding efforts to isolate positive cases. To address these shortcomings, this study develops a machine learning model based on the DenseNet-201 deep convolutional neural network, that can classify COVID-19 from chest radiographs in less than one minute and far more accurately than conventional tests (F1-score: 0.96; precision: 0.95; recall: 0.98). It uses a significantly larger dataset and more control classes than previously published models, demonstrating the promise of a machine learning approach for accurate and efficient COVID-19 screening. A live web application of the trained model can be accessed at https://cov2d19-classifier.herokuapp.com/.

scholarly journals Automated Classification of Tuberculosis by PSO based Machine Learning using Chest Radiographs

2016 ◽  
Vol V5 (10) ◽  
Author(s):  
S. Pushparani ◽  
Dr. A. Chandra Sekar ◽  
Dr. V. Vallinayagam ◽  
Dr. Jani Anbarasi ◽  
Keyword(s):  
Machine Learning ◽  
Chest Radiographs ◽  
Automated Classification
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