scholarly journals A chest radiography-based artificial intelligence deep-learning model to predict severe Covid-19 patient outcomes: the CAPE (Covid-19 AI Predictive Engine) Model

2020 ◽  
Author(s):  
Charlene Liew ◽  
Jessica Quah ◽  
Han Leong Goh ◽  
Narayan Venkataraman
Keyword(s):  
Deep Learning ◽  
Patient Outcomes ◽  
Chest Radiography ◽  
Scoring Systems ◽  
Learning Model ◽  
Chest Radiographs ◽  
Icu Admission ◽  
Risk Scoring ◽  
Deep Learning Model ◽  
Severe Patient

AbstractBackgroundChest radiography may be used together with deep-learning models to prognosticate COVID-19 patient outcomesPurposeT o evaluate the performance of a deep-learning model for the prediction of severe patient outcomes from COVID-19 pneumonia on chest radiographs.MethodsA deep-learning model (CAPE: Covid-19 AI Predictive Engine) was trained on 2337 CXR images including 2103 used only for validation while training. The prospective test set consisted of CXR images (n=70) obtained from RT-PCR confirmed COVID-19 pneumonia patients between 1 January and 30 April 2020 in a single center. The radiographs were analyzed by the AI model. Model performance was obtained by receiver operating characteristic curve analysis.ResultsIn the prospective test set, the mean age of the patients was 46 (+/-16.2) years (84.2% male). The deep-learning model accurately predicted outcomes of ICU admission/mortality from COVID-19 pneumonia with an AUC of 0.79 (95% CI 0.79-0.96). Compared to traditional risk scoring systems for pneumonia based upon laboratory and clinical parameters, the model matched the EWS and MulBTSA risk scoring systems and outperformed CURB-65.ConclusionsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 on chest radiographs.Key ResultsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 from chest radiographs with an AUC of 0.79, which is comparable to traditional risk scoring systems for pneumonia.Summary StatementThis is a chest radiography-based AI model to prognosticate the risk of severe COVID-19 pneumonia outcomes.

scholarly journals Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002683 ◽  
Author(s):  
John R. Zech ◽  
Marcus A. Badgeley ◽  
Manway Liu ◽  
Anthony B. Costa ◽  
Joseph J. Titano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Cross Sectional Study ◽  
Chest Radiographs ◽  
Sectional Study ◽  
Cross Sectional ◽  
Generalization Performance ◽  
Deep Learning Model

scholarly journals Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1182
Author(s):  
Cheng-Yi Kao ◽  
Chiao-Yun Lin ◽  
Cheng-Chen Chao ◽  
Han-Sheng Huang ◽  
Hsing-Yu Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Medical Center ◽  
Characteristic Curve ◽  
Learning Model ◽  
Chest Radiographs ◽  
Alert System ◽  
Before And After ◽  
Set Up ◽  
Deep Learning Model

We aimed to set up an Automated Radiology Alert System (ARAS) for the detection of pneumothorax in chest radiographs by a deep learning model, and to compare its efficiency and diagnostic performance with the existing Manual Radiology Alert System (MRAS) at the tertiary medical center. This study retrospectively collected 1235 chest radiographs with pneumothorax labeling from 2013 to 2019, and 337 chest radiographs with negative findings in 2019 were separated into training and validation datasets for the deep learning model of ARAS. The efficiency before and after using the model was compared in terms of alert time and report time. During parallel running of the two systems from September to October 2020, chest radiographs prospectively acquired in the emergency department with age more than 6 years served as the testing dataset for comparison of diagnostic performance. The efficiency was improved after using the model, with mean alert time improving from 8.45 min to 0.69 min and the mean report time from 2.81 days to 1.59 days. The comparison of the diagnostic performance of both systems using 3739 chest radiographs acquired during parallel running showed that the ARAS was better than the MRAS as assessed in terms of sensitivity (recall), area under receiver operating characteristic curve, and F1 score (0.837 vs. 0.256, 0.914 vs. 0.628, and 0.754 vs. 0.407, respectively), but worse in terms of positive predictive value (PPV) (precision) (0.686 vs. 1.000). This study had successfully designed a deep learning model for pneumothorax detection on chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.

Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs

2019 ◽  
Vol 34 (2) ◽  
pp. 86-91 ◽  
Author(s):  
Min Jae Cha ◽  
Myung Jin Chung ◽  
Jeong Hyun Lee ◽  
Kyung Soo Lee
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Chest Radiographs ◽  
Deep Learning Model

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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