Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning DOI Creative Commons
Yanda Meng, Joshua Bridge,

Cliff Addison

et al.

Medical Image Analysis, Journal Year: 2022, Volume and Issue: 84, P. 102722 - 102722

Published: Dec. 15, 2022

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using new bilateral adaptive graph-based (BA-GCN) model that can use both 2D 3D discriminative information CT volumes with arbitrary number slices. Given the importance lung segmentation for this task, have created largest manual annotation dataset so far 7,768 slices from patients, used it train segment lungs individual mask as regions interest subsequent analyses. We then UC-MIL estimate uncertainty each prediction consensus between predictions slice automatically select fixed reliable reasoning. Finally, adaptively constructed BA-GCN vertices different granularity levels (2D 3D) aggregate multi-level features final diagnosis benefits graph convolution network's superiority tackle cross-granularity relationships. Experimental results three datasets demonstrated our produce accurate any slices, which outperforms existing approaches terms generalisation ability. To promote reproducible research, made datasets, including annotations cleaned dataset, well implementation code, available at https://doi.org/10.5281/zenodo.6361963.

Language: Английский

Applications of artificial intelligence in battling against covid-19: A literature review DOI Open Access

Mohammad-H. Tayarani N.

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 142, P. 110338 - 110338

Published: Oct. 3, 2020

Language: Английский

Citations

196

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review DOI Open Access
Muzammil Khan, Muhammad Taqi Mehran, Zeeshan Haq

et al.

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 185, P. 115695 - 115695

Published: Aug. 4, 2021

Language: Английский

Citations

166

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia DOI Creative Commons
Guillaume Chassagnon,

Maria Vakalopoulou,

Enzo Battistella

et al.

Medical Image Analysis, Journal Year: 2020, Volume and Issue: 67, P. 101860 - 101860

Published: Oct. 15, 2020

Language: Английский

Citations

144

Artificial Intelligence for COVID-19: A Systematic Review DOI Creative Commons

Lian Wang,

Yonggang Zhang, Dongguang Wang

et al.

Frontiers in Medicine, Journal Year: 2021, Volume and Issue: 8

Published: Sept. 30, 2021

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation epidemic trends, prognosis, exploration effective safe drugs vaccines; discusses potential limitations. Methods: We report this systematic review following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. searched PubMed, Embase Cochrane Library from inception 19 September 2020 published studies AI applications COVID-19. used PROBAST (prediction model risk bias assessment tool) assess quality literature related diagnosis prognosis registered protocol (PROSPERO CRD42020211555). Results: included 78 studies: 46 articles discussed AI-assisted COVID-19 with total accuracy 70.00 99.92%, sensitivity 73.00 100.00%, specificity 25 area under curve 0.732 1.000. Fourteen evaluated based on clinical characteristics at hospital admission, such as clinical, laboratory radiological characteristics, reaching 74.4 95.20%, 72.8 98.00%, 55 96.87% AUC 0.66 0.997 predicting critical Nine models predict peak, infection rate, number infected cases, transmission laws, development trend. Eight explore drugs, primarily through drug repurposing development. Finally, 1 article predicted vaccine targets that have develop vaccines. Conclusions: In review, we shown achieved high performance evaluation, prediction discovery enhance significantly existing medical healthcare system efficiency during pandemic.

Language: Английский

Citations

134

An overview of deep learning methods for multimodal medical data mining DOI
Fatemeh Behrad, Mohammad Saniee Abadeh

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 200, P. 117006 - 117006

Published: April 4, 2022

Language: Английский

Citations

102

Artificial intelligence-based methods for fusion of electronic health records and imaging data DOI Creative Commons
Farida Mohsen, Hazrat Ali, Nady El Hajj

et al.

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Oct. 26, 2022

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal sources contributes to a better understanding of human provides optimal personalized healthcare. The most important question when using is how fuse them-a field growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion different modalities provide insights. To this end, scoping review, we focus on synthesizing analyzing literature that uses AI techniques for clinical applications. More specifically, studies only fused EHR with imaging develop various methods We present comprehensive analysis strategies, diseases outcomes which was used, ML algorithms used perform each application, available datasets. followed PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses Extension Scoping Reviews) guidelines. searched Embase, PubMed, Scopus, Google Scholar retrieve relevant studies. After pre-processing screening, extracted from 34 fulfilled inclusion criteria. found fusing increasing doubling 2020 2021. In our analysis, typical workflow observed: feeding raw data, by applying conventional (ML) or deep (DL) algorithms, finally, evaluating through outcome predictions. Specifically, early technique applications (22 out studies). multimodality models outperformed traditional single-modality same task. Disease diagnosis prediction were common (reported 20 10 studies, respectively) perspective. Neurological disorders dominant category (16 From an perspective, (19 studies), DL Multimodal included mostly private repositories (21 Through offer new insights researchers interested knowing current state knowledge within research field.

Language: Английский

Citations

90

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data DOI Creative Commons
Matteo Chieregato,

Fabio Frangiamore,

Mauro Morassi

et al.

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: March 14, 2022

COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic paucisymptomatic cases to acute respiratory distress syndrome multi-organ involvement. We developed a hybrid machine learning/deep learning model classify patients in two outcome categories, non-ICU ICU (intensive care admission or death), using 558 admitted northern Italy hospital February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory data, fed for selection Boruta algorithm SHAP game theoretical values. built the reduced space CatBoost gradient boosting reaching probabilistic AUC 0.949 holdout test set. The aims provide decision support medical doctors, probability score belonging an class case-based interpretation features importance.

Language: Английский

Citations

84

Elevated D-dimer levels on admission are associated with severity and increased risk of mortality in COVID-19: A systematic review and meta-analysis DOI Open Access
Barış Güngör, Adem Atıcı, Ömer Faruk Baycan

et al.

The American Journal of Emergency Medicine, Journal Year: 2020, Volume and Issue: 39, P. 173 - 179

Published: Sept. 14, 2020

Language: Английский

Citations

99

Prognostic Factors for 30-Day Mortality in Critically Ill Patients With Coronavirus Disease 2019: An Observational Cohort Study DOI Open Access
Paloma Ferrando-Vivas, James Doidge, Karen Thomas

et al.

Critical Care Medicine, Journal Year: 2020, Volume and Issue: 49(1), P. 102 - 111

Published: Oct. 28, 2020

OBJECTIVES: To identify characteristics that predict 30-day mortality among patients critically ill with coronavirus disease 2019 in England, Wales, and Northern Ireland. DESIGN: Observational cohort study. SETTING: A total of 258 adult critical care units. PATIENTS: 10,362 confirmed a start between March 1, 2020, June 22, whom 9,990 were eligible (excluding duration less than 24 hr or missing core variables). MEASUREMENTS AND MAIN RESULTS: The main outcome measure was time to death within 30 days the care. Of (median age 60 yr, 70% male), 3,933 died As July 189 still receiving further 446 acute hospital. Data for 0.1% 7.2% across prognostic factors. We imputed data ten-fold, using fully conditional specification continuous variables modeled restricted cubic splines. Associations candidate factors determined after adjustment multiple Cox proportional hazards modeling. Significant associations identified age, ethnicity, deprivation, body mass index, prior dependency, immunocompromise, lowest systolic blood pressure, highest heart rate, respiratory Pa o 2 /F io ratio (and interaction mechanical ventilation), lactate concentration, serum urea, platelet count over first hours Nonsignificant found sex, sedation, temperature, hemoglobin concentration. CONCLUSIONS: patient an increased likelihood 2019. These findings may support development prediction model benchmarking providers.

Language: Английский

Citations

80

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review DOI Creative Commons
Eleni S. Adamidi,

Konstantinos Mitsis,

Konstantina S. Nikita

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2021, Volume and Issue: 19, P. 2833 - 2850

Published: Jan. 1, 2021

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques optimize these on clinical settings terms quality, accuracy most importantly time. objective study is conduct systematic literature review published preprint reports models developed validated for coronavirus 2019. We included 101 studies, from January 1st, 2020 December 30th, 2020, that AI prediction which can be applied setting. identified total 14 38 diagnostic detecting 50 prognostic predicting ICU need, ventilator mortality risk, severity assessment or hospital length stay. Moreover, 43 were based medical imaging 58 use parameters, laboratory results demographic features. Several heterogeneous predictors derived multimodal data identified. Analysis data, captured various sources, prominence each category was performed. Finally, Risk Bias (RoB) analysis also examine applicability setting assist providers, guideline developers, policymakers.

Language: Английский

Citations

80