COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.

由 SARS-CoV-2 病原体引起的 COVID-19 感染已成为全世界灾难性的大流行病爆发，确诊病例呈指数增长，不幸的是，死亡人数呈指数增长。在这项工作中，我们提出了一个基于深度学习范例的人工智能管道，用于从 CT 扫描中自动检测 COVID-19 和病变分类。我们首先提出了一个新的分割模块，旨在自动识别肺实质和肺叶。接下来，我们将分割网络与分类网络结合起来进行 COVID-19 识别和病变分类。我们将模型的分类结果与三位放射科专家在 166 次 CT 扫描的数据集上获得的分类结果进行比较。结果显示 COVID-19 检测的灵敏度为 90.3%，特异性为 93.5%，至少与放射科专家的结果相当，平均病灶分类准确率约为 84%。此外，先前的肺和肺叶分割发挥了重要作用，这使我们能够将分类性能提高超过 6 个百分点。对经过训练的 AI 模型的解释表明，支持 COVID-19 识别决定的最重要区域与临床上与病毒相关的病变一致，即疯狂铺路、实变和毛玻璃。这意味着人工模型能够通过在 CT 扫描中评估这些病变的存在来区分阳性患者和阴性患者（对照组和间质性肺炎患者均对 COVID 检测呈阴性）。最后，AI 模型被集成到用户友好的 GUI 中，以支持放射科医生的 AI 可解释性，可在 http://perceivelab.com/covid-ai 上公开获取。整个人工智能系统是独一无二的，因为据我们所知，它是第一个公开可用的基于人工智能的软件，它试图向放射科医生解释人工智能方法使用哪些信息来做出决策，并主动让他们参与决策循环，以进一步提高对 COVID-19 的理解。