Molecular Biomarkers & Diagnosis

Coronavirus disease is a highly contagious infection that has triggered a pandemic in 2019. Early disease diagnosis has been identified as one of the most important approaches to reducing pathological impact and infection spread. Point-of-care tests have proven to be valuable analytical tools, particularly lateral flow immunoassays. Biosensors have grown in popularity in recent years. These are simple but highly sensitive and precise analytical devices made up of a selective molecule like an antibody or antigen and a sensor platform. Biosensors would be a more advanced alternative to current point-of-care tests and standard laboratory methods for COVID-19 diagnosis. Recent breakthroughs in COVID-19 point-of-care diagnostic tests, as well as the development of biosensors for specific antibodies and viruses.

As medical science and technology advance toward the "big data" era, a multi-dimensional dataset pertaining to medical diagnosis and treatment becomes available for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and have a high degree of redundancy. As a result, extensive data processing is widely recommended before feeding the dataset into the mathematical model. Artificial intelligence techniques, such as machine learning and deep learning algorithms based on artificial neural networks and their variants, are being used in this context to generate a precise and cross-sectional illustration of clinical data. Datasets derived from prostate-specific antigen, MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring in prostate cancer patients.