Rana Ashish, Singh Harpreet, Mavuduru Ravimohan, Pattanaik Smita, Rana Prashant Singh
Department of Computer Science and Engineering, TIET, Patiala, Punjab India.
Department of Urology and Pharmacology, PGIMER, Chandigarh, India.
Multimed Tools Appl. 2022;81(13):18129-18153. doi: 10.1007/s11042-022-12214-6. Epub 2022 Mar 8.
The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.
新冠疫情以其飞沫传播方式影响了世界上所有国家。由于感染的严重性，尤其是对患有合并症的人来说，大量的病例使所有医疗系统都不堪重负。一种具有非常高特异性的逆转录聚合酶链反应（RT-PCR）检测是用于诊断新冠患者的主要技术。此外，CT扫描有助于医学专业人员评估新冠病毒患者的严重程度并跟踪病情进展。在本研究中，我们展示了自己可扩展的新冠病毒感染跟踪预后技术。它使用在医学专业人员帮助下创建的胸部CT扫描切片图像的注释数据集。该注释数据集包含用于新冠检测的不同特征的边界框坐标，如磨玻璃影、铺路石样改变、实变、病变等。在我们的研究中，我们定性地确定患者的严重程度，以便用于后期的预后阶段，以协助医护人员对患者进行优先级排序。首先，我们使用预训练的暹罗神经网络（SNN）检测新冠阳性患者，该网络获得了87.6%的准确率、87.1%的F1分数和95.1%的AUC分数。这些指标是在从新冠CT数据集中去除40%定量高度相似的图像后实现的。这个精简后的数据集进一步用新冠特征进行医学注释，用于边界框检测。在此之后，我们为检测到的新冠特征分配严重程度分数，并计算新冠患者的累积严重程度分数。为了利用预后临床辅助信息对患者进行定性优先级排序，我们最终将这个分数转换为一个多分类问题，该问题获得了47%的加权平均F1分数。
