Charlotte Biebau, Adriana Dubbeldam, Lesley Cockmartin, Walter Coudyze, Johan Coolen, Johny Verschakelen, Walter De Wever
{"title":"比较 COVID-19 患者肺损伤的目视评分与基于人工智能的量化评分。","authors":"Charlotte Biebau, Adriana Dubbeldam, Lesley Cockmartin, Walter Coudyze, Johan Coolen, Johny Verschakelen, Walter De Wever","doi":"10.5334/jbsr.2330","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients.</p><p><strong>Materials and methods: </strong>In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five.</p><p><strong>Results: </strong>The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 ± 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (<i>p</i> < 0.001) between the visual and the AI-based estimates of the severity of lung injury.</p><p><strong>Conclusion: </strong>The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19.</p>","PeriodicalId":56282,"journal":{"name":"Journal of the Belgian Society of Radiology","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034398/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19.\",\"authors\":\"Charlotte Biebau, Adriana Dubbeldam, Lesley Cockmartin, Walter Coudyze, Johan Coolen, Johny Verschakelen, Walter De Wever\",\"doi\":\"10.5334/jbsr.2330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients.</p><p><strong>Materials and methods: </strong>In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five.</p><p><strong>Results: </strong>The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 ± 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (<i>p</i> < 0.001) between the visual and the AI-based estimates of the severity of lung injury.</p><p><strong>Conclusion: </strong>The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19.</p>\",\"PeriodicalId\":56282,\"journal\":{\"name\":\"Journal of the Belgian Society of Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2021-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034398/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Belgian Society of Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5334/jbsr.2330\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Belgian Society of Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5334/jbsr.2330","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19.
Objectives: Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients.
Materials and methods: In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five.
Results: The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 ± 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (p < 0.001) between the visual and the AI-based estimates of the severity of lung injury.
Conclusion: The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19.
期刊介绍：
The purpose of the Journal of the Belgian Society of Radiology is the publication of articles dealing with diagnostic and interventional radiology, related imaging techniques, allied sciences, and continuing education.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
