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

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

doi:10.3389/fmed.2021.585578

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

Front Med (Lausanne). 2021 Mar 31:8:585578. doi: 10.3389/fmed.2021.585578. eCollection 2021.

Authors

Abdelkader Nasreddine Belkacem¹, Sofia Ouhbi², Abderrahmane Lakas¹, Elhadj Benkhelifa³, Chao Chen⁴

Affiliations

¹ Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.
² Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.
³ Cloud Computing and Applications Research Lab, Staffordshire University, Stoke-on-Trent, United Kingdom.
⁴ Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China.

Abstract

Respiratory symptoms can be 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: 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 COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, 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 theoretical solution could play an important part in the early diagnosis.

Keywords: COVID-19; e-health; health diagnosis; intelligent learning; respiratory illness.