Hybrid Deep Learning Model For Diagnosis Of Covid-19 Using Ct Scans And Clinical/Demographic Data

Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal.Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists.Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05).Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.

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Development and validation of a deep learning model to assess tumor progression to immunotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e20601 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e20601-e20601 ◽

Cited By ~ 1

Author(s):

Yi Yang ◽

Jiancheng Yang ◽

Yuxiang Ye ◽

Tian Xia ◽

Shun Lu

Keyword(s):

Deep Learning ◽

Tumor Progression ◽

Tumor Volume ◽

Tumor Response ◽

Learning Model ◽

Ct Scans ◽

Tumor Diameter ◽

Data Set ◽

Senior Clinician ◽

Deep Learning Model

e20601 Background: Manual application of length-based tumor response criteria is the standard-of-care for assessing metastatic tumor response. It is technically challenging, time-consuming and associated with low reproducibility. In this study, we presented a novel automatic Deep Neural Networks (DNNs) based segmentation method for assessing tumor progression to immunotherapy. Next stage, AI will assist Physicians assessing pseudo-progression. Methods: A data set of 39 lung cancer patients with 156 computed tomography (CT) scans was used for model training and validation. A 3D segmentation DNN DenseSharp, was trained with an input size of on CT scans of tumor with manual delineated volume of interest (VOI) as ground truth. The trained model was subsequently used to estimate the volumes of target lesions via 16 sliding windows. We referred the progression-free survival (PFS) only considering tumor size as PFS-T. PFS-Ts assessed by longest tumor diameter (PFS-Tdiam), by tumor volume (PFS-Tvol), and by predicted tumor volume (PFS-Tpred-vol) were compared with standard PFS (as assessed by one junior and one senior clinician). Tumor progression was defined as > 20% increase in the longest tumor diameter or > 50% increase in tumor volume. Effective treatment was defined as a PFS of > 60 days after immunotherapy. Results: In a 4-fold cross-validation test, the DenseSharp segmentation neural network achieved a mean per-class intersection over union (mIoU) of 80.1%. The effectiveness rates of immunotherapy assessed using PFS-Tdiam (32 / 39, 82.1%), PFS-Tvol (33/39, 84.6%) and PFS-T pred-vol (32/39, 82.1%) were the same as standard PFS. The agreement between PFS-Tvol, and PFS-Tpred-vol was 97.4% (38/39). Evaluation time with deep learning model implemented with PyTorch 0.4.1 on GTX 1080 GPU was hundred-fold faster than manual evaluation (1.42s vs. 5-10 min per patient). Conclusions: In this study, DNN based model demonstrated fast and stable performance for tumor progression evaluation. Automatic volumetric measurement of tumor lesion enabled by deep learning provides the potential for a more efficient, objective and sensitive measurement than linear measurement by clinicians.

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Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

Nature Communications ◽

10.1038/s41467-020-20657-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Nathalie Lassau ◽

Samy Ammari ◽

Emilie Chouzenoux ◽

Hugo Gortais ◽

Paul Herent ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Learning Model ◽

Ct Scans ◽

Clinical Variables ◽

Biological Variables ◽

Severity Scores ◽

Scan Data ◽

Deep Learning Model ◽

Chest Ct Scan

AbstractThe SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

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Deep Learning Model Selection of Suboptimal Complexity

Автоматика и телемеханика ◽

10.31857/s000523100001252-1 ◽

2018 ◽

pp. 129-147

Author(s):

Oleg Bakhteev ◽

◽

Vadim Strijov ◽

Keyword(s):

Deep Learning ◽

Model Selection ◽

Learning Model ◽

Deep Learning Model ◽

Selection Of

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

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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