This article presents a comprehensive systematic literature review (SLR) on the outcomes of using artificial intelligence (AI) for the recognition of diseases through voice analysis, with a focus on biomarkers. The SLR process involved the selection and analysis of relevant research studies, including their methodologies, datasets, and findings. This review highlights the effectiveness of AI-guided tools for screening COVID-19 infections using cough and sneezing sounds, discusses the state-of-the-art methods employed, and explores the potential of deep learning models in improving disease detection and prediction.<\/p>\n\n\n\n
Eligibility of the Studies:<\/strong><\/p>\n\n\n\n The SLR included a total of 28 studies that met the inclusion and exclusion criteria. The selected studies utilized various datasets, including cough sound data from laboratory-confirmed patients, crowd-sourced data, and data collected via mobile apps. While some studies focused on COVID-19 prediction, others provided essential information for the SLR without specific COVID-19 relevance. The analysis excluded studies with invalid measurements, unavailability of full text, and publication years before 2019. Eight studies were included based on references, resulting in a final set of 28 studies for synthesis.<\/p>\n\n\n\n Studied non-Laboratory Confirmed Dataset and State-of-the-Art Methods:<\/strong><\/p>\n\n\n\n The review examined the use of deep learning methods, such as convolutional neural networks (CNNs), for the recognition of COVID-19 through cough and sneezing sounds. AI-guided cough screening tools employing CNNs showed promising results, with high sensitivity, specificity, and overall accuracy. The studies compared laboratory-confirmed datasets with non-laboratory-confirmed datasets, demonstrating the superior performance of AI-guided approaches when trained on reliable data. Ensemble learning, CNNs, and deep neural networks were utilized to identify COVID-19 in sneezing noises. The research also emphasized the potential of incorporating biomarkers and clinical data, such as respiration, speech, throat clearing, wheezing, and breathing, to enhance decision-making in disease screening.<\/p>\n\n\n\n Discussion and Conclusions:<\/strong><\/p>\n\n\n\n Based on the selected studies, coughing was identified as one of the most prominent symptoms for the detection and prediction of COVID-19. AI-guided cough screening, along with clinical testing, can aid in the diagnosis of COVID-19, making it a valuable tool, particularly in resource-constrained areas. Deep features were found to extend the generalization of AI models without relying on past information, allowing for more precise predictions and insights. The use of AI-guided tools for COVID-19 screening showed superior performance when trained on laboratory-confirmed datasets. However, some studies lacked comprehensive method descriptions or employed biased models, highlighting the need for further improvements.<\/p>\n\n\n\n The review concluded that AI-guided solutions using biomarkers and clinical data, combined with advanced deep learning models like CNNs, hold promise for improving the analysis and prediction of COVID-19 and other diseases. Future research should explore the integration of ensemble deep neural networks and multimodal learning methods to enhance disease screening outcomes.<\/p>\n\n\n\n Overall, this systematic literature review provides valuable insights into the application of AI in biomarker recognition and disease detection from voice analysis. The findings demonstrate the potential of AI-guided tools for accurate and efficient screening, paving the way for advancements in healthcare and disease management.<\/p>\n\n\n\n <\/p>\n\n\n\n