Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,

Human analysis of cardiac MRI scans is subject to enough noise and bias that a quick machine-learning approach easily matched it for accuracy, researchers found. A single expert reader contouring a scan to get left ventricular (LV) ejection fraction and LV mass had intra-observer error manifest as coefficients of variation of 5.4 and 3.8, respectively, while junior trainees had similar 5.2 and 5.5 coefficients. When a scan was repeated on the same person but at a different time, there was no difference in overall variation when comparing results from an expert, two trainees, and an automated deep-learning neural network, reported Charlotte Manisty, PhD, of University College London and Barts Heart Centre, and colleagues in Circulation: Cardiovascular Imaging. "Given that the greatest sources of measurement error were human factors (i.e., non-modifiable intra- and inter-observer variability), we believe that, with improvement, it is only a matter of time before automated approaches are super-human," according to the investigators. "These data demonstrated that human (intra-observer) error was greater than half of scan-rescan error, an effect that was not minimized by an expert when compared with junior clinicians after appropriate training, despite fifteen years' additional experience," they added.