ArticleJapanese journal of radiology2021
Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The possibilities of prospective assessment of SARS-CoV-2 infection risk based on an artificial neural network model - a cross-sectional andArchives of medical science : AMS · 2025Article
- Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.BMC gastroenterology · 2024Article
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
- Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates.Clinical and experimental medicine · 2023Observational
- Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.Journal of medical ultrasonics (2001) · 2023Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan.PloS one · 2023Article
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
- [Diagnostic accuracy of the automatic quantification software in patients with suspected COVID-19 of the Instituto Nacional de Cardiology Ignacio Chávez].Archivos de cardiologia de Mexico · 2022Article
- Reply to the Letter to the Editor: Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.Japanese journal of radiology · 2021Article
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10 authors.
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Abstract
purposeTo evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia. MATERIALS AND
methodsThis retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal-Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity.
resultsAll CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, - 801 HU and 77 ml/min/1.73 m
conclusionThe mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.
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