AI algorithm can detect, quantify brain infarcts

Researchers discussed how they used a deep-learning algorithm to detect, quantify, and assess the severity of infarcts in the brain on diffusion-weighted MRI (DWI-MRI) exams in acute ischemic stroke patients in a Sunday presentation at the virtual RSNA 2020 meeting. A team of researchers led by presenter Seung Hyun Hwang of Yonsei University in Seoul, South Korea, developed a deep-learning model that can segment and quantify brain infarcts using DWI-MRI and then assess their severity by analyzing apparent diffusion coefficient (ADC) maps of the lesions. In testing, the model achieved high sensitivity and specificity. "The qualitative and quantitative results of our study show feasibility for detecting and quantifying infarcts," Hwang said. Due to its sensitivity for the detection of small and early infarcts, DWI-MRI is commonly used for evaluation of acute ischemic stroke, according to Hwang.