title: An artificial neural network-based noninvasive detector for suction and left atrium pressure in the control of rotary blood pumps: an in vitro study.
abstract: Rotary blood pumps are used in clinical applications to assist circulation via pumping blood from the left atrium to the aorta. Negative inflow pressures at high flow rates can cause suction of the cannula in the left atrium with deleterious effects on the atrial wall, the blood, and the lung. Therefore, stable and reliable detection of suction and the prediction of the left atrium pressure (LAP) would be of major interest for the control of these pumps. This work reports about an in vitro study of such a detector based on artificial neural networks (ANN). In the first project phase, an ANN was used to estimate the LAP based on pump speed, pump flow, and aortic pressure, obtained from a mock circulation. The inputs for the ANN were 11 characteristic values computed from these three parameters. In the second phase, another ANN was trained to classify various system states, such as suction, danger of suction (a state close to actual suction), and no suction. The first ANN was able to estimate the LAP with an accuracy of +/- 1.8 mm Hg. The discrimination of suction versus the other two states could be performed with a sensitivity and specificity of about 95% while the more interesting task of distinguishing danger of suction from no suction reached a sensitivity and specificity of about 65% (leaving 25% of each class unclassified and 10% of each class incorrectly classified).(ABSTRACT TRUNCATED AT 250 WORDS)
authors.affiliation: Austrian Research Institute for Artificial Intelligence, Department of Medical Cybernetics and Artificial Intelligence, LBI of Cardiothoracic Research University of Vienna.
authors.forename: C
authors.initials: C
authors.lastname: Stöcklmayer
authors.forename: G
authors.initials: G
authors.lastname: Dorffner
authors.forename: C
authors.initials: C
authors.lastname: Schmidt
authors.forename: H
authors.initials: H
authors.lastname: Schima
mesh_terms: D016275:Atrial Function; D001783:Blood Flow Velocity; D001794:Blood Pressure / Q000502:physiology; D006353:Heart-Assist Devices / Q000592:standards*; D066298:In Vitro Techniques; D016571:Neural Networks, Computer; D012399:Rotation; D012680:Sensitivity and Specificity