ArticleLife (Basel, Switzerland)2021
COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis.
Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Interdisciplinary Perspectives on Dentistry and Sleep Medicine: A Narrative Review of Sleep Apnea and Oral Health.Journal of clinical medicine · 2025Review
- Modulating the JAK/STAT pathway with natural products: potential and challenges in cancer therapy.Discover oncology · 2025Review
- Coinfections in Tuberculosis in Low- and Middle-Income Countries: Epidemiology, Clinical Implications, Diagnostic Challenges, and Management Strategies-A Narrative Review.Journal of clinical medicine · 2025Review
- Impact of vitamin C deficiency on imaging patterns and ventilatory function in pulmonary tuberculosis.Frontiers in medicine · 2025Article
- Vitamin C and Tuberculosis: Examining the Relationship Between Antioxidant Defense and Disease Severity-Preliminary Findings from a Southwestern Romanian Study.Journal of clinical medicine · 2024Article
- Moscatilin, a potential therapeutic agent for cancer treatment: insights into molecular mechanisms and clinical prospects.Medical oncology (Northwood, London, England) · 2024Review
- Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.Frontiers in public health · 2024Article
- Evaluating Serum Calcium and Magnesium Levels as Predictive Biomarkers for Tuberculosis and COVID-19 Severity: A Romanian Prospective Study.International journal of molecular sciences · 2023Article
- Discovering associations between radiological features and COVID-19 patients' deterioration.Health science reports · 2023Article
- Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends.Healthcare (Basel, Switzerland) · 2023Review
- Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.Computers in biology and medicine · 2022Article
- Pediatric Functional Abdominal Pain Disorders following COVID-19.Life (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.Journal of personalized medicine · 2022Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis' severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis' severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis' severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients.
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