title: Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm--preliminary report.
abstract: To determine the performance of an artificial neural network in transcranial color-coded duplex sonography (TCCS) diagnosis of middle cerebral artery (MCA) spasm. TCCS was prospectively acquired within 2 h prior to routine cerebral angiography in 100 consecutive patients (54M:46F, median age 50 years). Angiographic MCA vasospasm was classified as mild (<25% of vessel caliber reduction), moderate (25-50%), or severe (>50%). A Learning Vector Quantization neural network classified MCA spasm based on TCCS peak-systolic, mean, and end-diastolic velocity data. During a four-class discrimination task, accurate classification by the network ranged from 64.9% to 72.3%, depending on the number of neurons in the Kohonen layer. Accurate classification of vasospasm ranged from 79.6% to 87.6%, with an accuracy of 84.7% to 92.1% for the detection of moderate-to-severe vasospasm. An artificial neural network may increase the accuracy of TCCS in diagnosis of MCA spasm.
authors.affiliation: Bialystok Technical University, Faculty of Electrical Engineering, ul. Wiejska 45D, 15-351, Bialystok, Poland.
authors.forename: Miroslaw
authors.initials: M
authors.lastname: Swiercz
authors.forename: Jan
authors.initials: J
authors.lastname: Kochanowicz
authors.forename: John
authors.initials: J
authors.lastname: Weigele
authors.forename: Robert
authors.initials: R
authors.lastname: Hurst
authors.forename: David S
authors.initials: DS
authors.lastname: Liebeskind
authors.forename: Zenon
authors.initials: Z
authors.lastname: Mariak
authors.forename: Elias R
authors.initials: ER
authors.lastname: Melhem
authors.forename: Jaroslaw
authors.initials: J
authors.lastname: Krejza
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001703:Biophysics / Q000379:methods; D001931:Brain Mapping / Q000379:methods; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D020244:Infarction, Middle Cerebral Artery / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D020768:Middle Cerebral Artery / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012720:Severity of Illness Index; D020521:Stroke / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D017585:Ultrasonography, Doppler, Transcranial / Q000379:methods*; D020301:Vasospasm, Intracranial / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology