Svensson Ann Mari, Kistner Anna, Kairaitis Kristina, Prisk G Kim, Farrow Catherine, Amis Terence, Wagner Peter D, Malhotra Atul, Harbut Piotr
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, 171 76, Sweden.
Department of Radiology, Solna, Karolinska University Hospital, Stockholm, 171 76, Sweden.
BJR Open. 2025 Apr 29;7(1):tzaf008. doi: 10.1093/bjro/tzaf008. eCollection 2025 Jan.
Artificial intelligence (AI) deep learning algorithms trained on non-contrast CT scans effectively detect and quantify acute COVID-19 lung involvement. Our study explored whether radiological contrast affects the accuracy of AI-measured lung opacities, potentially impacting clinical decisions. We compared lung opacity measurements from AI software with visual assessments by radiologists using CT pulmonary angiography (CTPA) images of early-stage COVID-19 patients.
This prospective single-centre study included 18 COVID-19 patients who underwent CTPA due to suspected pulmonary embolism. Patient demographics, clinical data, and 30-day and 90-day mortality were recorded. AI tool (Pulmonary Density Plug-in, AI-Rad Companion Chest CT, SyngoVia; Siemens Healthineers, Forchheim, Germany) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by 2 radiologists.
There was a positive correlation between radiologist estimations ( = 0.57) and between the AI data and the mean of the radiologists' estimations ( = 0.70). Bland-Altman plot analysis showed a mean bias of +3.06% between radiologists and -1.32% between the mean radiologist vs AI, with no outliers outside 2×SD for respective comparison.
The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from 2 independent radiologists, demonstrating its potential as a complementary tool in clinical practice.
In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists.
The Pulmonary Density Plug-in enables quantitative analysis of lung opacities in COVID-19 patients using contrast-enhanced CT images, potentially streamlining clinical workflows and supporting timely decision-making.
在非增强CT扫描上训练的人工智能（AI）深度学习算法可有效检测和量化新型冠状病毒肺炎（COVID-19）急性肺受累情况。我们的研究探讨了放射学对比剂是否会影响AI测量肺实质密度的准确性，这可能会影响临床决策。我们使用早期COVID-19患者的CT肺血管造影（CTPA）图像，将AI软件测量的肺实质密度与放射科医生的视觉评估进行了比较。
这项前瞻性单中心研究纳入了18例因疑似肺栓塞而接受CTPA检查的COVID-19患者。记录患者的人口统计学信息、临床数据以及30天和90天死亡率。使用AI工具（肺密度插件，AI-Rad Companion Chest CT，SyngoVia；德国福希海姆西门子医疗公司）估计肺实质密度。由2名放射科医生独立进行视觉定量评估。
放射科医生的评估之间存在正相关（=0.57），AI数据与放射科医生评估的平均值之间也存在正相关（=0.70）。Bland-Altman图分析显示，放射科医生之间的平均偏差为+3.06%，放射科医生平均值与AI之间的平均偏差为-1.32%，各自比较时均无超出2倍标准差的异常值。
AI方案有助于对肺实质密度进行定量评估，并与2名独立放射科医生获得的数据显示出很强的相关性，证明了其作为临床实践中辅助工具的潜力。
在评估CTPA图像中的COVID-19肺实质密度时，在非增强图像上训练的AI工具提供的结果与放射科医生的视觉评估相当。
肺密度插件能够使用增强CT图像对COVID-19患者的肺实质密度进行定量分析，可能会简化临床工作流程并支持及时决策。
