Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey

Area of Science:

• Medical Diagnostics
• Infectious Diseases
• Artificial Intelligence in Healthcare

Background:

• The COVID-19 pandemic, caused by the novel coronavirus, emerged in late 2019, leading to significant global health and economic impacts.
• Over 600 million cases and 6.6 million deaths were reported worldwide by November 2022.
• Diverse diagnostic techniques were developed to combat the rapid spread of the virus.

Purpose of the Study:

• To conduct a systematic review of various COVID-19 diagnostic techniques.
• To classify and comparatively analyze the efficacy of different diagnostic modalities.
• To highlight the role of Artificial Intelligence in advancing COVID-19 diagnostics.

Main Methods:

• Systematic review of COVID-19 diagnostic techniques.
• Classification based on working principles: chest X-ray imaging, cough sound analysis, RT-PCR, antigen testing, and antibody testing.
• Comparative analysis of the efficacy of each diagnostic method.

Main Results:

• Diagnostic techniques were categorized into imaging, respiratory pattern analysis, molecular tests (RT-PCR), and serological tests (antigen/antibody).
• Artificial Intelligence (AI) emerged as a crucial element in developing advanced diagnostic tools.
• The review provides a comprehensive overview for researchers and supports healthcare systems.

Conclusions:

• AI plays a vital role in enhancing the accuracy and efficiency of COVID-19 diagnostics.
• The reviewed techniques offer lasting solutions applicable to future pandemic scenarios.
• This consolidated information serves as a valuable resource for ongoing research and development in diagnostic technologies.