Chatzitofis Anargyros, Cancian Pierandrea, Gkitsas Vasileios, Carlucci Alessandro, Stalidis Panagiotis, Albanis Georgios, Karakottas Antonis, Semertzidis Theodoros, Daras Petros, Giannitto Caterina, Casiraghi Elena, Sposta Federica Mrakic, Vatteroni Giulia, Ammirabile Angela, Lofino Ludovica, Ragucci Pasquala, Laino Maria Elena, Voza Antonio, Desai Antonio, Cecconi Maurizio, Balzarini Luca, Chiti Arturo, Zarpalas Dimitrios, Savevski Victor
Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece.
Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy.
Int J Environ Res Public Health. 2021 Mar 11;18(6):2842. doi: 10.3390/ijerph18062842.
Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.
自2019年12月以来，全球遭受了2019冠状病毒病（COVID-19）大流行的重创。急诊科一直面临紧急情况，临床专家在抗击COVID-19方面缺乏长期经验和成熟手段，不得不迅速决定最恰当的患者治疗方案。在此背景下，我们引入了一种基于计算机断层扫描（CT）的人工智能工具，用于有效且高效的风险评估，以改善治疗和患者护理。在本文中，我们介绍了一种基于感兴趣体积感知深度神经网络的数据驱动方法，用于基于通过分割进行的肺部感染量化以及随后的CT分类，对COVID-19患者进行自动风险评估（出院、住院、重症监护病房）。我们通过仅检测和分析CT的一个子体积，即感兴趣体积（VoI），来处理CT输入的高维和变化维度。与最近的策略不同，那些策略考虑受感染的CT切片而不要求它们之间有任何空间连贯性，或者通过应用突然且有损的体积下采样来使用整个肺体积，我们仅评估由具有原始空间分辨率的切片组成的“感染最严重的体积”。为实现上述目标，我们创建、展示并发布了一个新的带有标签和注释的CT数据集，其中包含来自COVID-19患者的626个CT样本。与这些策略的比较证明了我们基于VoI方法的有效性。我们在平衡数据上评估患者风险评估时取得了显著性能，准确率、灵敏度、特异性和F1分数分别达到88.88%、89.77%、94.73%和88.88%。
