Real-time identification of cerebral microemboli with US feature detection by a neural network.
OBJECTIVE
Abnormal transcranial Doppler ultrasonographic (US) signals indicating cerebral microembolism have characteristic but complex features. The authors wanted to assess the agreement among human observers and test the feasibility of an automated detection system.


METHODS
Automated on-line detection of cerebral microemboli was accomplished by employing real-time overlapping Fourier transform and artificial neural network technology. By using long-term transcranial Doppler US recordings of the middle cerebral artery in consecutive cerebrovascular and cardiac patients, the method was evaluated in a clinical setting.


RESULTS
The proportion of specific agreement (ps) among four experienced investigators identifying cerebral microemboli was high (mean ps, 0.91). Agreement among the neural network and the human observers was only slightly less (mean ps, 0.77).


CONCLUSIONS
The technique allows highly reliable on-line evaluation of transcranial Doppler US recordings across multiple centers. It obviates time-consuming analyses by human observers.
Siebler
M
M
Department of Neurology, Heinrich-Heine University, Düsseldorf, Germany.
Rose
G
G
Sitzer
M
M
Bender
A
A
Steinmetz
H
H
D006801:Humans; D002542:Intracranial Embolism and Thrombosis / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D015588:Observer Variation; D017585:Ultrasonography, Doppler, Transcranial