Automated intraoperative detection of Doppler microembolic signals using the bigate approach.

BACKGROUND AND PURPOSE We undertook this study to evaluate the performance of an automated detection software in the detection of Doppler microembolic signals (MES) during cardiac surgery. METHODS Intraoperative monitoring was performed over two spatially separated vessel segments of each middle cerebral artery in 18 patients undergoing coronary artery bypass surgery (n= 16) or cardiac valve replacement (n=2). All monitoring sessions were saved on digital audiotape and subsequently played back to the same ultrasound machine, set up to automatically detect MES by evaluating the temporary delay in their appearance between the two segments, in the presence of an experienced examiner. Software sensitivity and specificity in MES detection were then evaluated, with the results of the human observer considered the gold standard. RESULTS A total of 44,933 high-intensity signals (artifacts and MES) were evaluated. Overall sensitivity and specificity of the software, with the human observer considered the gold standard, were 64% and 78.5%, respectively, ranging from 54% to 96% and from 74% to 90% in individual patients. When the overall results of the software were compared with those of the human observer, kappa was 0.72. CONCLUSIONS The tested software displayed a satisfactory specificity. Provided that the sensitivity is further improved, it could provide a valuable tool in intraoperative monitoring.

[1]  P R Bell,et al.  Differentiation between emboli and artefacts using dual-gated transcranial Doppler ultrasound. , 1996, Ultrasound in medicine & biology.

[2]  J. Gold,et al.  Impact of embolization during coronary artery bypass grafting on outcome and length of stay. , 1997, The Annals of thoracic surgery.

[3]  M Kaps,et al.  Bigated transcranial Doppler for the detection of clinically silent circulating emboli in normal persons and patients with prosthetic cardiac valves. , 1997, Stroke.

[4]  G Rose,et al.  Real-time identification of cerebral microemboli with US feature detection by a neural network. , 1994, Radiology.

[5]  B. Everitt,et al.  Statistical methods for rates and proportions , 1973 .

[6]  Merrill P. Spencer,et al.  Basic identification criteria of Doppler microembolic signals , 1995 .

[7]  W. Conover.  Statistical Methods for Rates and Proportions , 1974 .

[8]  R. E. Clark,et al.  Microemboli during coronary artery bypass grafting. Genesis and effect on outcome. , 1995, The Journal of thoracic and cardiovascular surgery.

[9]  J. Linley,et al.  The detection of microemboli in the middle cerebral artery during cardiopulmonary bypass: a transcranial Doppler ultrasound investigation using membrane and bubble oxygenators. , 1987, The Annals of thoracic surgery.

[10]  T. Treasure,et al.  Detection of middle cerebral emboli during coronary artery bypass surgery using transcranial Doppler sonography. , 1990, Stroke.

[11]  T. Treasure,et al.  The Impact of Microemboli During Cardiopulmonary Bypass on Neuropsychological Functioning , 1994, Stroke.

[12]  E B Ringelstein,et al.  A novel technique for identification of doppler microembolic signals based on the coincidence method: in vitro and in vivo evaluation. , 1996, Stroke.

[13]  H. Markus,et al.  Multigated Doppler ultrasound in the detection of emboli in a flow model and embolic signals in patients. , 1996, Stroke.

[14]  W. Mess,et al.  Automatic embolus detection compared with human experts. A Doppler ultrasound study. , 1996, Stroke.

[15]  K. Lees,et al.  Quantification of Ultrasound Emboli Signals in Patients With Cardiac and Carotid Disease , 1993, Stroke.