PDF(Pubmed)
Abstract：
COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1 s t and 2 n d order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.
摘要：
COVID-19（冠状病毒），急性呼吸系统疾病,是由SARS-CoV-2（冠状病毒严重急性呼吸道综合症）引起的。COVID-19感染的高患病率引起了人们对常见疾病症状的关注：嗅觉和味觉功能障碍。该手稿的主要目的是创建一个计算机辅助诊断（CAD）系统，以确定COVID-19患者是否正常，温和,或者严重的失眠症.为了实现这一目标,我们使用流体衰减反转恢复（FLAIR）磁共振成像（FLAIR-MRI）和扩散张量成像（DTI）来提取外观，形态学,和嗅觉神经的扩散标记。所提出的系统从嗅觉神经的识别开始，由熟练的专家或放射科医师执行。然后继续执行后续的主要步骤：（i）提取外观标记（即，1st和2nd阶标记)，形态/形状标记(即，球面谐波)，和扩散标记(即，分数各向异性(FA)和平均扩散率(MD))，(ii)应用基于集成标记的标记融合，和(iii)基于最有希望的分类器确定决策和相应的性能指标。当前的研究是不寻常的，因为它集成了学习和微调的ML分类器，并使用多数投票诊断嗅球（OB）嗅觉缺失。在5倍方法中，它达到了94.1%的准确率，平衡精度(BAC)为92.18%，精度91.6%，召回90.61%，特异性为93.75%，F1得分为89.82%,和交汇处（IoU）为82.62%。在10倍方法中，堆叠继续展示令人印象深刻的结果，准确率为94.43%，BAC为93.0%,精度为92.03%，召回91.39%，特异性94.61%，F1得分为91.23%，IoU为84.56%。在留一主题(LOSO)方法中，该模型继续表现出显著的成果，达到91.6%的准确率，BAC为90.27%，精度88.55%,召回87.96%，特异性92.59%，F1得分为87.94%,IOU为78.69%。这些结果表明，堆叠和多数投票是CAD系统的关键组成部分，对整体性能改进做出了重大贡献。拟议的技术可以帮助医生评估哪些患者需要更深入的临床护理。
