ArticleDiagnostics (Basel, Switzerland)2020
Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04750330 (Mitochondrial DNA and Nuclear SNPs to Predict Severity of COVID-19 Infection), which is not on this map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Mitochondrial DNA and Nuclear SNPs to Predict Severity of COVID-19 Infection
Who cites it
30 citing papers in PubMed, 49 citations in OpenAlex.
- Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.Computational and structural biotechnology journal · 2024Review
- Central artery pulse pressure, not central arterial stiffness impact on all-cause mortality in patients with viral pneumonia infection.BMC infectious diseases · 2024Article
- Long Term Evaluation of Quantitative Cumulative Irradiation in Patients Suffering from ILDs.Diagnostics (Basel, Switzerland) · 2024Article
- Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs' Parenchymal Involvement Quantification in COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Utility of bronchoalveolar lavage for COVID-19: a perspective from the Dragon consortium.Frontiers in medicine · 2024Review
- Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection.Journal of clinical medicine · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.Journal of medical and biological engineering · 2023Article
- Developing medical imaging AI for emerging infectious diseases.Nature communications · 2022Article
- Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.European radiology · 2022Article
- COVID-19 Diagnosis on Chest Radiographs with Enhanced Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs.Diagnostics (Basel, Switzerland) · 2022Article
- The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave.Diagnostics (Basel, Switzerland) · 2022Article
- An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.Medical physics · 2022Article
- A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.Diagnostics (Basel, Switzerland) · 2022Review
- Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity.Frontiers in medicine · 2022Article
- Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.Scientific reports · 2021Article
- Artificial intelligence-driven assessment of radiological images for COVID-19.Computers in biology and medicine · 2021Review
Corrections and comments
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Authors and funding
21 authors at 2 institutions in 2 countries.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.