Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19

Abstract

Biomedical data analysis is an exceedingly broad field. It includes array data analysis, biomedical image analysis, integrated or hybrid data analysis, and patient data analysis using machine learning (ML) and artificial intelligence (AI). Array data analysis can be further classified into RNA-sequence, single-cell RNA-sequence, microarray, and ChiP-seq data analysis. Biomedical imaging encompasses different parameters like gathering of biomedical signal, formation of an image, processing of an image, display of image, and the medical diagnosis that are built on the features obtained from various images. AI was mainly used to break down healthcare data and used to track and screen patients while Internet of Things was used mainly for monitoring a patient remotely. There are different radiological imaging processes that include the radiography, ultrasound, thermography, magnetic resonance imaging, nuclear medicine and computed tomography. We, in this book chapter, provide a comprehensive survey (road map) on various array-based sequence data analyses and biomedical imaging along with their integrated studies for different tissue-specific dreadful diseases (such as cancer). We included the integrated studies of biomedical imaging and array-based data analysis for the same set of patients (samples) that covered the problem of combinatorial gene signature detection as well as disease subtype image classifications while specific multimodal data from well-known data repository (e.g., TCGA, ICGC) had been provided. Finally, our book chapter covers the maximum area of biomedical imaging as well as array-based sequence data analysis along with the contribution of AI and ML in order to build a smart healthcare system, and provide a new dimension to the interested biomedical researchers.