scholarly journals AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

2020 ◽  
Author(s):  
Maheshwar Kuchana ◽  
Amritesh Srivastava ◽  
Ronald Das ◽  
Justin Mathew ◽  
Atul Mishra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans

scholarly journals A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 ◽  
Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Approach

Automatic Distinction between COVID-19 and Common Pneumonia Using Ensemble Deep Learning on Small Number of Chest CT Scans

2020 ◽  
Author(s):  
Tao Yan ◽  
Hao Ren ◽  
Pak Kin Wong ◽  
Huaqiao Wang ◽  
Jiangtao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans

scholarly journals DABC-Net for robust pneumonia segmentation and prediction of COVID-19 progression on chest CT scans

2020 ◽  
Author(s):  
Xiao-Yong Zhang ◽  
Ziqi Yu ◽  
Xiaoyang Han ◽  
Botao Zhao ◽  
Yaoyao Zhuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
State Of The Art ◽  
Chest Ct ◽  
Ct Scans ◽  
Patient Specific ◽  
Specific Prediction ◽  
Lstm Network ◽  
Progression Prediction ◽  
Ct Slices

Abstract Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic.

scholarly journals A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Author(s):  
Feng Pan ◽  
Lin Li ◽  
Bo Liu ◽  
Tianhe Ye ◽  
Lingli Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Quantitative Computed Tomography ◽  
Pulmonary Involvement ◽  
Chest Ct ◽  
Estimation Methods ◽  
Ct Scans ◽  
Severe Group ◽  
Conventional Ct ◽  
Spearman’S Correlation

Abstract Objectives: This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. Materials and Methods: 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: 1. Correlation between these two estimations; 2. Exploring the dynamic patterns using these two estimations between moderate and severe groups.Results: The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p<0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. Conclusions: The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

scholarly journals COVID-19 Diagnosis from Chest CT Scans: A Weakly Supervised CNN-LSTM Approach

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 330-341
Author(s):  
Mustafa Kara ◽  
Zeynep Öztürk ◽  
Sergin Akpek ◽  
Ayşegül Turupcu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
High Sensitivity ◽  
High Specificity ◽  
Chest Ct ◽  
Ct Scans ◽  
Imaging Data ◽  
Weakly Supervised

Advancements in deep learning and availability of medical imaging data have led to the use of CNN-based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction-based tests in COVID-19 diagnosis, CT images offer an applicable supplement with their high sensitivity rates. Here, we study the classification of COVID-19 pneumonia and non-COVID-19 pneumonia in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks and bidirectional Long Short Term Memory architectures. Our study achieved high specificity (COVID-19 pneumonia: 98.3%, non-COVID-19 pneumonia: 96.2% Healthy: 89.3%) and high sensitivity (COVID-19 pneumonia: 84.0%, non-COVID-19 pneumonia: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the Convolutional Neural Network predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities, indicators of COVID-19 pneumonia disease, were captured by our convolutional neural network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

scholarly journals Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Hossein Mohammad-Rahimi ◽  
Mohadeseh Nadimi ◽  
Azadeh Ghalyanchi-Langeroudi ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Chest Ct ◽  
Accurate Diagnosis ◽  
Ct Scans ◽  
Rt Pcr ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Chest X Ray

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

scholarly journals ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

2020 ◽  
Author(s):  
M. Yousefzadeh ◽  
P. Esfahanian ◽  
S. M. S. Movahed ◽  
S. Gorgin ◽  
R. Lashgari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
The Other ◽  
Ct Scans ◽  
Lower Sensitivity ◽  
Imaging Features ◽  
Learning Framework ◽  
Experienced Radiologist ◽  
Chest Ct Scan

AbstractBackgroundWith the global outbreak of COVID-19 epidemic since early 2020, there has been considerable attention on CT-based diagnosis as an effective and reliable method. Recently, the advent of deep learning in medical diagnosis has been well proven. Convolutional Neural Networks (CNN) can be used to detect the COVID-19 infection imaging features in a chest CT scan. We introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using the chest CT scans.MethodOur dataset comprises 2121 cases of axial spiral chest CT scans in three classes; COVID-19 abnormal, non COVID-19 abnormal, and normal, from which 1764 cases were used for training and 357 cases for validation. The training set was annotated using the reports of two experienced radiologists. The COVID-19 abnormal class validation set was annotated using the general consensus of a collective of criteria that indicate COVID-19 infection. Moreover, the validation sets for the non COVID-19 abnormal and the normal classes were annotated by a different experienced radiologist. ai-corona constitutes a CNN-based feature extractor conjoined with an average pooling and a fully-connected layer to classify a given chest CT scan into the three aforementioned classes.ResultsWe compare the diagnosis performance of ai-corona, radiologists, and model-assisted radiologists for six combinations of distinguishing between the three mentioned classes, including COVID-19 abnormal vs. others, COVID-19 abnormal vs. normal, COVID-19 abnormal vs. non COVID-19 abnormal, non COVID-19 abnormal vs. others, normal vs. others, and normal vs. abnormal. ai-corona achieves an AUC score of 0.989 (95% CI: 0.984, 0.994), 0.997 (95% CI: 0.995, 0.999), 0.986 (95% CI: 0.981, 0.991), 0.959 (95% CI: 0.944, 0.974), 0.978 (95% CI: 0.968, 0.988), and 0.961 (95% CI: 0.951, 0.971) in each combination, respectively. By employing Bayesian statistics to calculate the accuracies at a 95% confidence interval, ai-corona surpasses the radiologists in distinguishing between the COVID-19 abnormal class and the other two classes (especially the non COVID-19 abnormal class). Our results show that radiologists’ diagnosis performance improves when incorporating ai-corona’s prediction. In addition, we also show that RT-PCR’s diagnosis has a much lower sensitivity compared to all the other methods.Conclusionai-corona is a radiologist-assistant deep learning framework for fast and accurate COVID-19 diagnosis in chest CT scans. Our results ascertain that our framework, as a reliable detection tool, also improves experts’ diagnosis performance and helps especially in diagnosing non-typical COVID-19 cases or non COVID-19 abnormal cases that manifest COVID-19 imaging features in chest CT scan. Our framework is available at: ai-corona.com

scholarly journals Correction: ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257119
Author(s):  
Mehdi Yousefzadeh ◽  
Parsa Esfahanian ◽  
Seyed Mohammad Sadegh Movahed ◽  
Saeid Gorgin ◽  
Dara Rahmati ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Framework
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