首页|University Magna Graecia of Catanzaro Reports Findings in Artificial Intelligenc e [Artificial intelligence (AI)-assisted chest computer tomog raphy (CT) insights: a study on intensive care unit (ICU) admittance trends in 7 8 coronavirus disease ...]
University Magna Graecia of Catanzaro Reports Findings in Artificial Intelligenc e [Artificial intelligence (AI)-assisted chest computer tomog raphy (CT) insights: a study on intensive care unit (ICU) admittance trends in 7 8 coronavirus disease ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Catanzaro,Ital y,by NewsRx correspondents,research stated,"The global coronavirus disease 20 19 (COVID-19) pandemic has posed substantial challenges for healthcare systems,notably the increased demand for chest computed tomography (CT) scans,which lac k automated analysis. Our study addresses this by utilizing artificial intellige nce-supported automated computer analysis to investigate lung involvement distri bution and extent in COVID-19 patients." Our news journalists obtained a quote from the research from the University Magn a Graecia of Catanzaro,"Additionally,we explore the association between lung i nvolvement and intensive care unit (ICU) admission,while also comparing compute r analysis performance with expert radiologists' assessments. A total of 81 pati ents from an open-source COVID database with confirmed COVID-19 infection were i ncluded in the study. Three patients were excluded. Lung involvement was assesse d in 78 patients using CT scans,and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally,the computer analysi s of COVID-19 involvement was compared against a human rating provided by radiol ogical experts. The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P <0.05). N o significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvemen t compared to the right lower lobes (P <0.05). When examini ng the regions,significantly more COVID-19 involvement was found when comparing the posterior. the anterior halves and the lower. the upper half of the lungs. who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis,c ompared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high c orrelation was observed between computer detection of COVID-19 affections and th e rating by radiological experts. The findings suggest that the extent of lung i nvolvement,particularly in the lower lobes,dorsal lungs,and lower half of the lungs,may be associated with the need for ICU admission in patients with COVID -19. Computer analysis showed a high correlation with expert rating,highlightin g its potential utility in clinical settings for assessing lung involvement. Thi s information may help guide clinical decision-making and resource allocation du ring ongoing or future pandemics."
CatanzaroItalyEuropeArtificial Int elligenceCOVID-19ComputersCoronaviridaeCoronavirusEmerging Technologie sMachine LearningNidoviralesRNA VirusesRisk and PreventionSARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2ViralVirology
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