scholarly journals Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans

2021 ◽  
Vol 11 (10) ◽  
pp. 1008
Author(s):  
Muhammad Owais ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
State Of The Art ◽  
Area Under The Curve ◽  
Ground Glass Opacity ◽  
Visual Assessment ◽  
Ct Scans ◽  
Execution Speed ◽  
Ct Data ◽  
Personalized Diagnosis

Background: Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists. Method: A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients). Results: Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods. Conclusions: These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.

scholarly journals Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242759
Author(s):  
Se Bum Jang ◽  
Suk Hee Lee ◽  
Dong Eun Lee ◽  
Sin-Youl Park ◽  
Jong Kun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Retrospective Study ◽  
Image Interpretation ◽  
Area Under The Curve ◽  
Ground Glass Opacity ◽  
Community Treatment ◽  
Ct Scans ◽  
Treatment Center ◽  
Automatic Interpretation ◽  
Radiology Reports

The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians in chest radiography (CR) diagnoses with Coronavirus disease 2019 (COVID-19) pneumonia, the automatic interpretation of the CR with DL algorithms can significantly reduce the burden on clinicians and radiologists in sudden surges of suspected COVID-19 patients. The aim of this study was to evaluate the efficacy of the DL algorithm for detecting COVID-19 pneumonia on CR compared with formal radiology reports. This is a retrospective study of adult patients that were diagnosed as positive COVID-19 cases based on the reverse transcription polymerase chain reaction among all the patients who were admitted to five emergency departments and one community treatment center in Korea from February 18, 2020 to May 1, 2020. The CR images were evaluated with a publicly available DL algorithm. For reference, CR images without chest computed tomography (CT) scans classified as positive for COVID-19 pneumonia were used given that the radiologist identified ground-glass opacity, consolidation, or other infiltration in retrospectively reviewed CR images. Patients with evidence of pneumonia on chest CT scans were also classified as COVID-19 pneumonia positive outcomes. The overall sensitivity and specificity of the DL algorithm for detecting COVID-19 pneumonia on CR were 95.6%, and 88.7%, respectively. The area under the curve value of the DL algorithm for the detection of COVID-19 with pneumonia was 0.921. The DL algorithm demonstrated a satisfactory diagnostic performance comparable with that of formal radiology reports in the CR-based diagnosis of pneumonia in COVID-19 patients. The DL algorithm may offer fast and reliable examinations that can facilitate patient screening and isolation decisions, which can reduce the medical staff workload during COVID-19 pandemic situations.

scholarly journals Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans

2020 ◽  
Author(s):  
Xuehai He ◽  
Xingyi Yang ◽  
Shanghang Zhang ◽  
Jinyu Zhao ◽  
Yichen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Superior Performance ◽  
Ct Scans ◽  
Learning To Learn ◽  
Learning Methods ◽  
Feature Representations ◽  
Medical Tests ◽  
Diagnosis Accuracy ◽  
Ct Data

AbstractCoronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose a Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

scholarly journals Diagnostic value of lung ultrasound versus chest CT in COVID-19

2020 ◽  
Vol 11 (2) ◽  
pp. 5-18
Author(s):  
S. S. Petrikov ◽  
I. E. Popova ◽  
V. M. Abuchina ◽  
R. Sh. Muslimov ◽  
L. T. Khamidova ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Lung Diseases ◽  
Structural Changes ◽  
Lung Ultrasound ◽  
Ground Glass Opacity ◽  
Diagnostic Value ◽  
Chest Ct ◽  
Ct Scans ◽  
Chest Computed Tomography ◽  
Ct Data

Lung ultrasound demonstrates a high diagnostic value in the assessment of lung diseases.Aim. To determine the diagnostic accuracy of lung ultrasound compared to chest computed tomography (CT) in the diagnosis of lung changes in COVID-19. Materials and methods. The retrospective study included 45 patients (28 men) aged 37 to 90 years who underwent polypositional lung ultrasound with an assessment of 14 zones. The study compared lung echograms with chest CT data in assessing the prevalence of the process and the nature of structural changes. The diagnostic accuracy, sensitivity, and specificity of lung ultrasound in comparison with CT scans were determined, 95% confidence intervals (CI) were calculated.Results. In 44 patients (98%), CT revealed pathological changes with subpleural localization in both lungs. Of these, in 30 cases, the inflammation was limited only to the subpleural parts, and in 14 cases, the changes spread to the basal parts of the lungs, while ultrasound revealed changes at the depth of the lesion no more than 4 cm. The lesion of 10–11 zones according to lung ultrasound corresponds to CT 1–2 degrees, the lesion of 13–14 zones — CT 3–4 degrees. The sensitivity of ultrasound to detect lung changes of various types was ≥ 92%. The highest sensitivity of 97.9% (95% CI: 92.8–99.8%) was determined for small consolidations on the background of interstitial changes (degree 1A+, 1B+), which corresponded to “crazy-paving” pattern on CT. The specificity depended on the nature of the changes and varied from 46.7 to 70.0%. Diagnostic accuracy was ≥ 81%, the maximum values of 90.6% (95% CI: 85.6–94.2%) were obtained for moderate interstitial changes (grade 1A) corresponding to ground-glass opacity (type one) according to CT data.Conclusion. The sensitivity of ultrasound to detect lung changes in COVID-19 is more than 90%. Lung ultrasound has some limitations: inability to determine the prevalence of the process clearly and identify centrally located areas of changes in the lung tissue.

scholarly journals Magnetic resonance imaging–based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept

2021 ◽  
Vol 50 (1) ◽  
pp. E13
Author(s):  
Victor E. Staartjes ◽  
Peter R. Seevinck ◽  
W. Peter Vandertop ◽  
Marijn van Stralen ◽  
Marc L. Schröder
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Lumbar Spine ◽  
Surgical Planning ◽  
Visual Inspection ◽  
Ct Scans ◽  
Lumbar Spine Surgery ◽  
Proof Of Concept ◽  
Acquisition Protocol ◽  
Synthetic Ct

OBJECTIVEComputed tomography scanning of the lumbar spine incurs a radiation dose ranging from 3.5 mSv to 19.5 mSv as well as relevant costs and is commonly necessary for spinal neuronavigation. Mitigation of the need for treatment-planning CT scans in the presence of MRI facilitated by MRI-based synthetic CT (sCT) would revolutionize navigated lumbar spine surgery. The authors aim to demonstrate, as a proof of concept, the capability of deep learning–based generation of sCT scans from MRI of the lumbar spine in 3 cases and to evaluate the potential of sCT for surgical planning.METHODSSynthetic CT reconstructions were made using a prototype version of the “BoneMRI” software. This deep learning–based image synthesis method relies on a convolutional neural network trained on paired MRI-CT data. A specific but generally available 4-minute 3D radiofrequency-spoiled T1-weighted multiple gradient echo MRI sequence was supplemented to a 1.5T lumbar spine MRI acquisition protocol.RESULTSIn the 3 presented cases, the prototype sCT method allowed voxel-wise radiodensity estimation from MRI, resulting in qualitatively adequate CT images of the lumbar spine based on visual inspection. Normal as well as pathological structures were reliably visualized. In the first case, in which a spiral CT scan was available as a control, a volume CT dose index (CTDIvol) of 12.9 mGy could thus have been avoided. Pedicle screw trajectories and screw thickness were estimable based on sCT findings.CONCLUSIONSThe evaluated prototype BoneMRI method enables generation of sCT scans from MRI images with only minor changes in the acquisition protocol, with a potential to reduce workflow complexity, radiation exposure, and costs. The quality of the generated CT scans was adequate based on visual inspection and could potentially be used for surgical planning, intraoperative neuronavigation, or for diagnostic purposes in an adjunctive manner.

scholarly journals COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans

2021 ◽  
Vol 4 ◽  
Author(s):  
Shahin Heidarian ◽  
Parnian Afshar ◽  
Nastaran Enshaei ◽  
Farnoosh Naderkhani ◽  
Moezedin Javad Rafiee ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Data Augmentation ◽  
Spatial Information ◽  
Area Under The Curve ◽  
Spatial Relations ◽  
Building Blocks ◽  
Ct Scans ◽  
Rapid Separation

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the “COVID-FACT”. COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82%, a sensitivity of 94.55%, a specificity of 86.04%, and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

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 Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Musa Sani Musa ◽  
Mubarak Taiwo Mustapha ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Chest Radiography ◽  
Area Under The Curve ◽  
Chest Ct ◽  
Chest Computed Tomography ◽  
Classification Tasks ◽  
Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

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 Weakly-supervised lesion analysis with a CNN-based framework for COVID-19

2021 ◽  
Author(s):  
Kaichao Wu ◽  
Beth Jelfs ◽  
Xiangyuan Ma ◽  
Ruitian Ke ◽  
Xuerui Tan ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Lesion Detection ◽  
Ct Image ◽  
Medical College ◽  
Lesion Analysis ◽  
Ct Data ◽  
Weakly Supervised

Abstract Lesions of COVID-19 can be visualized clearly by chest CT images, therefore, providing valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task, requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions. Specifically, this framework employs a deep learning-based diagnosis branch for the classification of the CT image and then leverages a lesion identification branch to capture multiple types of lesions. We verify our framework on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

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