THREE WAYS DEEP LEARNING YIELDS NEW INSIGHTS FOR MEDICAL RESEARCHERS

Think of where the state of medicine would be without the blood pressure cuff.

Without it, it would be more difficult to understand the risk of strokes, heart disease and kidney disease. Blood pressure is what is known as a biomarker - a signal that can help medical researchers understand underlying disease mechanisms and biological responses to specific disorders.

For obvious reasons, biomarker identification has direct benefits for early detection and external interventions. When it comes to neurological disorders, neuroimaging techniques like magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT) scans are frequently used to identify underlying disease phenotypes that indicate the presence of Alzheimer's disease or brain cancer.

Increasingly, researchers have applied deep learning (DL), which consists of advanced machine learning techniques and a subset of artificial intelligence, to the task of biomarker identification by studying and understanding neuroimaging results.

DL has one key advantage over traditional machine learning and statistical methods. It does not rely on hand-crafted datasets that are themselves the subject of human limitations (time, resources and research bias). Rather, DL can leverage information from raw data that is processed with minimal human intervention.

Because of this, the results we get for various downstream applications using DL are more accurate. They also open a variety of analysis opportunities, one of them being the discovery of new biomarkers. In other words, DL can help build tools that better predict and classify patients suffering from disease from healthy subjects. DL can also reveal potentially useful insights for medical research that was not known.