COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Infectious Disease Diagnostics

Background:

• Coronavirus Disease 19 (COVID-19) emerged in early 2020, rapidly becoming a global health concern monitored by the World Health Organization (WHO).
• Accurate and timely diagnosis is crucial for managing the COVID-19 pandemic, necessitating the exploration of advanced diagnostic technologies.

Purpose of the Study:

• To investigate the infection mechanisms, patient symptoms, and laboratory diagnosis of COVID-19.
• To conduct an extensive assessment of various technologies and computational models for accurate COVID-19 detection.
• To evaluate the efficacy of AI-driven diagnostic tools utilizing Electrocardiographic (ECG) signals, voice analysis, and X-ray imaging.

Main Methods:

• Systematic review of existing literature on COVID-19 diagnostic technologies.
• Analysis of computational models employing machine learning and deep learning techniques.
• Evaluation of diagnostic performance metrics including accuracy and F1-Scores for ECG, voice, and X-ray based models.

Main Results:

• Reviewed computational models demonstrated high accuracy rates for COVID-19 detection, ranging from 85.70% to 100%.
• F1-Scores for the evaluated diagnostic models were notably high, varying between 89.52% and 100%.
• AI-based approaches using ECG, voice, and X-ray data show promising results for rapid and accurate COVID-19 diagnosis.

Conclusions:

• The current state-of-the-art in AI-driven COVID-19 detection shows significant achievements with high performance metrics.
• Despite high accuracy, the field of COVID-19 diagnostics using computational models has substantial room for improvement.
• Further research is needed to address the diverse symptomatology and evolving understanding of disease progression in individuals.