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

Belkacem, Abdelkader Nasreddine, Ouhbi, Sofia, Lakas, Abderrahmane, BENKHELIFA, Elhadj and Chen, Chao (2021) End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework. Frontiers in Medicine, 8. ISSN 2296-858X

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Abstract or description

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.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, intelligent learning, respiratory illness, health diagnosis, e-health
Faculty:	School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User:	Elhadj BENKHELIFA
Date Deposited:	29 Nov 2022 15:08
Last Modified:	24 Feb 2023 14:04
URI:	https://eprints.staffs.ac.uk/id/eprint/7501

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