Gazzoni Marco, La Salvia Marco, Torti Emanuele, Secco Gianmarco, Perlini Stefano, Leporati Francesco
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
Emergency Medicine Unit and Emergency Medicine Postgraduate Training Program, Department of Internal Medicine, University of Pavia, IRCCS Policlinico San Matteo Foundation, 27100 Pavia, Italy.
Bioengineering (Basel). 2023 Feb 21;10(3):282. doi: 10.3390/bioengineering10030282.
The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress syndrome (ARDS), causing hundreds of thousands of casualties worldwide. Guidelines for first-line diagnosis of pneumonia suggest Chest X-rays (CXR) for patients exhibiting symptoms. Potential alternatives include Computed Tomography (CT) scans and Lung UltraSound (LUS). Deep learning (DL) has been helpful in diagnosis using CT scans, LUS, and CXR, whereby the former commonly yields more precise results. CXR and CT scans present several drawbacks, including high costs. Radiation-free LUS imaging requires high expertise, and physicians thus underutilise it. LUS demonstrated a strong correlation with CT scans and reliability in pneumonia detection, even in the early stages. Here, we present an LUS video-classification approach based on contemporary DL strategies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. This research addressed SARS-CoV-2 patterns detection, ranked according to three severity scales by operating a trustworthy dataset comprising ultrasounds from linear and convex probes in 5400 clips from 450 hospitalised subjects. The main contributions of this study are related to the adoption of a standardised severity ranking scale to evaluate pneumonia. This evaluation relies on video summarisation through key-frame selection algorithms. Then, we designed and developed a video-classification architecture which emerged as the most promising. In contrast, the literature primarily concentrates on frame-pattern recognition. By using advanced techniques such as transfer learning and data augmentation, we were able to achieve an F1-Score of over 89% across all classes.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）大流行给全球卫生系统带来了挑战，因此需要实用、快速且高度可靠的诊断工具来帮助医务人员。它具有潜伏期长和传染率高的特点，会导致双侧多灶性间质性肺炎，通常会发展为急性呼吸窘迫综合征（ARDS），在全球造成了数十万人死亡。肺炎一线诊断指南建议对出现症状的患者进行胸部X光（CXR）检查。潜在的替代方法包括计算机断层扫描（CT）和肺部超声（LUS）。深度学习（DL）在使用CT、LUS和CXR进行诊断方面发挥了作用，其中前者通常能产生更精确的结果。CXR和CT扫描存在一些缺点，包括成本高。无辐射的LUS成像需要很高的专业知识，因此医生对其使用不足。LUS在肺炎检测中，即使在早期阶段，也与CT扫描显示出很强的相关性和可靠性。在此，我们与帕维亚圣马泰奥综合医院急诊科（ED）密切合作，基于当代DL策略提出了一种LUS视频分类方法。本研究针对SARS-CoV-2模式检测，通过一个可靠的数据集进行排名，该数据集包含来自450名住院患者的线性和凸面探头的5400个超声片段，并根据三个严重程度等级进行操作。本研究的主要贡献与采用标准化的严重程度排名量表来评估肺炎有关。这种评估依赖于通过关键帧选择算法进行视频摘要。然后，我们设计并开发了一种最具前景的视频分类架构。相比之下，文献主要集中在帧模式识别上。通过使用迁移学习和数据增强等先进技术
