scholarly journals Deep convolutional neural networks to predict cardiovascular risk from computed tomography

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Roman Zeleznik ◽  
Borek Foldyna ◽  
Parastou Eslami ◽  
Jakob Weiss ◽  
Ivanov Alexander ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Cardiovascular Events ◽  
Strong Predictor ◽  
Unmet Need ◽  
Acute Chest Pain ◽  
Learning System ◽  
Imaging Biomarkers ◽  
Clinical Value ◽  
Deep Convolutional Neural Networks

AbstractCoronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.

scholarly journals A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

2020 ◽  
Vol 56 (2) ◽  
pp. 2000775 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
Medical Resource ◽  
Early Prevention ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

scholarly journals A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

2020 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
High Risk Patients ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic ◽  
Worse Prognosis

AbstractCoronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms.Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.

A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

2021 ◽  
pp. 103865
Author(s):  
Eman Shaheen ◽  
André Leite ◽  
Khalid Ayidh Alqahtani ◽  
Andreas Smolders ◽  
Adriaan Van Gerven ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Cone Beam Computed Tomography ◽  
Validation Study ◽  
Learning System ◽  
Cone Beam ◽  
Tooth Segmentation

Auxiliary diagnosis of pulmonary nodules using liquid biopsy and deep learning/techniques.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13154-e13154
Author(s):  
Li Bai ◽  
Yanqing Zhou ◽  
Yaru Chen ◽  
Quanxing Liu ◽  
Dong Zhou ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Cancer Patients ◽  
Liquid Biopsy ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Learning System ◽  
Benign Tumors ◽  
Deep Convolutional Neural Networks

e13154 Background: Many people harbor pulmonary nodules. Such nodules can be detected by low-dose computed tomography (LDCT) during regular physical examinations. If a pulmonary nodule is small (i.e. < 10mm), it is very difficult to diagnose whether it is benign or malignant using CT images alone. To address this problem, we developed a method based on liquid biopsy and deep learning to improve diagnostic accuracy of pulmonary nodules. Methods: Thirty-eight patientsharboring one or more small pulmonary nodules were enrolled in this study. Twenty-nine patients were diagnosed as having cancer (stage I = 21, stage II = 1, stage III = 3, stage IV = 4) using tissue biopsy, while the other 9 patients were diagnosed as having benign tumors or lung diseases other than cancer. For each patient, a blood sample was obtained prior to biopsy, and the cell free DNA (cfDNA) was sequenced using a 451-gene panel to a depth of 20,000×. The unique molecular identifiers (UMI) technique was applied to reduce false positives. Seventeen patients also had full-resolution CT images available. A deep learning system primarily based on deep convolutional neural networks (CNN) was used to analyze these CT images. Results: Sequence analysis of blood samples revealed that 75.8% (22/29) of cancer patients had detectable cancer related mutations, and only 1 of 9 (11.1%) non-cancer patient was found to carry a TP53 mutation. The most frequent mutations seen in cancer patients involved genes TP53 (N = 11), EGFR (N = 7), and KRAS (N = 3) with mutant allele fractions varying from 0.08% to 74.77%. Deep learning analysis of the 17 available CT images correctly identified cancers in 88.2% (15/17) of patients. However, by combining the liquid biopsy and image analysis results, all 17 patients were correctly diagnosed. Conclusions: Deep learning-based analysis of CT images can be applied to early diagnosis of lung cancers; but the accuracy of image analysis, when used alone, is only moderate. Diagnostic accuracy can be greatly improved using liquid biopsy as an auxiliary method in patients with pulmonary nodules.

scholarly journals Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography

2020 ◽  
Vol 21 (1) ◽  
pp. 88 ◽  
Author(s):  
Hyo Jung Park ◽  
Yongbin Shin ◽  
Jisuk Park ◽  
Hyosang Kim ◽  
In Seob Lee ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Abdominal Muscle ◽  
Learning System ◽  
Development And Validation

scholarly journals Associations and outcomes of cardiovascular calcifications by computed tomography in atrial fibrillation patients

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
T K M Wang ◽  
N Chan ◽  
P C Cremer ◽  
M Kanj ◽  
B Baranowski ◽  
...  
Keyword(s):  
Risk Factors ◽  
Computed Tomography ◽  
Atrial Fibrillation ◽  
Cardiovascular Events ◽  
Imaging Biomarkers ◽  
Major Adverse Cardiovascular Events ◽  
Funding Sources ◽  
National Heart Foundation ◽  
Multivariable Regression Models ◽  
Cardiovascular Calcifications

Abstract Background Coronary (CAC), mitral annular (MAC), aortic valve (AVC) and thoracic aortic (TAC) calcifications are known imaging biomarkers for cardiovascular risk in the general population. Despite this, their prognostic value are not well established in atrial fibrillation (AF) patients who have elevated risk for cardiovascular events. Purpose We assessed the associated factors and outcomes of cardiovascular calcifications detected by computed tomography (CT) in AF patients undergoing pulmonary vein isolation (PVI). Methods Consecutive AF patients enrolled in a prospective PVI registry during 2014–2018 with CT performed within 1-year of their procedure were reviewed for the presence of cardiovascular calcifications on CT. Risk factors and outcomes for each type of cardiovascular calcifications were analyzed in univariable and multivariable regression models. Results Amongst 3604 AF patients, there were 2238 (62.1%), 308 (8.6%), 572 (15.9%) and 1048 (29.1%) patients with CAC, MAC, AVC and TAC respectively. Factors independently associated with these cardiovascular calcifications are listed in Table 1. During mean follow-up of 2.8±1.6 years, there were 97 (2.7%) all-cause deaths, and 158 (4.4%) major adverse cardiovascular events (MACE). Forest plots of unadjusted and adjusted hazards ratios of cardiovascular calcifications at predicting these outcomes are shown in Figure 1. Conclusion Cardiovascular calcifications especially CAC are prevalent in AF patients, and share common risk factors with cardiovascular events, except for female being protective for CAC and AVC but having higher risk of MAC and TAC. Although all cardiovascular calcifications were associated with death and MACE in unadjusted analyses, only CAC remained so for both outcomes in adjusted analyses. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): National Heart Foundation of New Zealand Table 1 Figure 1

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