Author Interview: Dr. Rajat Dhar on “Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage”
A conversation with Dr. Rajat Dhar, MD, Associate Professor of Neurology and Neuro-critical care, Washington University School of Medicine, St. Louis, MO.
Interviewed by Dr. Saurav Das, MD, Fellow in Vascular Neurology, Washington University School of Medicine, St. Louis, MO.
They will be discussing the article “Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage,” published in Stroke.
Dr. Das: Dr. Dhar, on behalf of the Blogging Stroke team, we welcome you to this author interview. I read with great interest your paper pertaining to the automated estimation of selective sulcal volume (SSV) to quantify global cerebral edema (GCE) from early brain injury (EBI) in aneurysmal subarachnoid hemorrhage (aSAH). This is an important paper as our understanding of clinical outcomes following aSAH is shifting from vasospasm induced delayed cerebral ischemia (DCI) towards GCE from EBI. Also, we currently do not have the tools to measure GCE accurately.
This research uses a “deep learning-based approach” for the analysis of serial CT scans to measure SSV. Many of our readers may not be familiar with the use of artificial intelligence (AI) in image analysis. I will begin by requesting you to explain what deep learning is.
Dr. Dhar: Applications of artificial intelligence, specifically machine learning, to the realm of biomedical image analysis have been growing exponentially over the past few years. AI is well-suited to image analysis because, at its core, machine learning seeks to find patterns in data, and images are just patterns of intensity and location data. Machine learning algorithms can be trained to learn from labeled data. For example, to determine what regions of a scan represent blood vs. brain vs. CSF is called a segmentation task. We can use machine learning to perform a segmentation task on new imaging data. AI algorithms can perform image analysis in a fast and reproducible way, eliminating the need for time-intensive human input. They can measure volumes of similar brain structures over serial time points more objectively and accurately than one or more humans may be able to.