Use of neural networks to improve quality control of interpretations in myocardial perfusion imaging

Background: The aim of this study was to explore the feasibility of using a technique based on artificial neural networks for quality assurance of image reporting. The networks were used to identify potentially suboptimal or erroneous interpretations of myocardial perfusion scintigrams (MPS). Methods: Reversible perfusion defects (ischaemia) in each of five myocardial regions, as interpreted by one experienced nuclear medicine physician during his daily routine of clinical reporting, were assessed by artificial neural networks in 316 consecutive patients undergoing stress/rest 99mTc-sestamibi myocardial perfusion scintigraphy. After a training process, the networks were used to select the 20 cases in each region that were more likely to have a false clinical interpretation. These cases, together with 20 control cases in which the networks detected no likelihood of false clinical interpretation, were presented in random order to a group of three experienced physicians for a consensus re-interpretation; no information regarding clinical or neural network interpretations was provided to the re-evaluation panel. Results: The clinical interpretation and the re-evaluation differed in 53 of the 200 cases. Forty-six of the 53 cases (87%) came from the group selected by the neural networks, and only seven (13%) were control cases (P < 0.001). The disagreements between clinical routine interpretation by an experienced nuclear medicine expert and artificial networks were related to small and mild perfusion defects and localization of defects. Conclusion: The results demonstrate that artificial neural networks can identify those myocardial perfusion scintigrams that may have suboptimal image interpretations. This is a potentially highly cost-effective technique, which could be of great value, both in daily practice as a clinical decision support tool and as a tool in quality assurance.