论文标题

基于AI的端到端护理诊断系统,用于分类呼吸系统疾病和早期检测COVID-19

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

论文作者

Belkacem, Abdelkader Nasreddine, Ouhbi, Sofia, Lakas, Abderrahmane, Benkhelifa, Elhadj, Chen, Chao

论文摘要

呼吸症状可能是由不同的潜在疾病引起的,通常是由病毒感染引起的,例如流感样疾病或其他新兴病毒(如冠状病毒)。这些呼吸道病常常具有常见症状,包括咳嗽,高温,鼻子拥挤和呼吸困难。但是,早期诊断病毒类型可能至关重要,尤其是在最近的Covid-19-19大流行中。导致大流行的因素之一是诊断后期诊断或使其与常规流感样症状混淆。科学证明,这些不同呼吸道疾病的基本原因的可能区分之一是咳嗽,它具有不同的类型和形式。因此,非常需要一个可靠的无实验室工具,可用于早期诊断,可以区分不同的呼吸道疾病。本文提出了一个端到端便携式系统,该系统可以记录症状患者的数据,包括咳嗽(自愿或非自愿),并将其转化为健康数据以进行诊断,并借助机器学习,将其分类为不同的呼吸道疾病,包括Covid-19。随着持续的努力阻止当今各地的Covid-19疾病的传播,以及将来反对类似疾病的疾病,我们提议的低成本和用户友好的解决方案可以在早期诊断中起重要作用。

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis.

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