Purpose: to optimize a pipeline of clinical data gathering and CT images processing implemented during the COVID-19 pandemic crisis and to develop artificial intelligence model for different of viral pneumonia. Methods: 1028 chest CT image of patients with positive swab were segmented automatically for lung extraction. A Gaussian model developed in Python language was applied to calculate quantitative metrics (QM) describing well-aerated and ill portions of the lungs from the histogram distribution of lung CT numbers in both lungs of each image and in four geometrical subdivision. Furthermore, radiomic features (RF) of first and second order were extracted from bilateral lungs using PyRadiomic tools. QM and RF were used to develop 4 different Multi-Layer Perceptron (MLP) classifier to discriminate images of patients with COVID (n=646) and non-COVID (n=382) viral pneumonia. Results: The Gaussian model applied to lung CT histogram correctly described healthy parenchyma 94% of the patients. The resulting accuracy of the models for COVID diagnosis were in the range 0.76-0.87, as the integral of the receiver operating curve. The best diagnostic performances were associated to the model based on RF of first and second order, with 21 relevant features after LASSO regression and an accuracy of 0.81$\pm$0.02 after 4-fold cross validation Conclusions: Despite these results were obtained with CT images from a single center, a platform for extracting useful quantitative metrics from CT images was developed and optimized. Four artificial intelligence-based models for classifying patients with COVID and non-COVID viral pneumonia were developed and compared showing overall good diagnostic performances
翻译:目的:优化在COVID-19大流行危机期间实施的临床数据收集和CT图像处理管道,并开发不同病毒肺炎的人工智能模型。方法:1028个呈阳性脉冲的病人胸部CT图像被自动分割用于肺提取。用Python语言开发的高斯模型用于计算量度计(QM),描述每个图像肺部和4个几何直方位子肺部肺部的肺部CT值分布良好和患病部分。此外,使用PyRadimical 工具从双边肺部提取了第一和第二顺序的放射特征(RF)。 QM和RF被用于开发4种多Layer Percepron(MLP)的胸部图像,以区分患有COVID(n=646)和非COVID(n=382)的肺部。结果:高斯模型适用于肺部CTBS的准确度直径直径直达94%的病人。由此得出的COVID诊断模型的不准确性位数从双边肺部取价位取自双边肺部。 0.0中心诊断模型的不精确度分析,在 maleveralevalation1 4-Revlation中运行后运行后,以0.7-ralx 显示的模型显示的第二模型显示。