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Learning Vector Quantization Neural Networks Improve Accuracy of Transcranial Color-coded Duplex Sonography in Detection of Middle Cerebral Artery Spasm—Preliminary Report

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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.

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Acknowledgments

Supported by American Heart Association Established Investigator Award grant 044099N—(Krejza J) and Polish State Committee for Scientific Research—grant 3 T11E 027 28 (Swiercz M).

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Authors and Affiliations

  1. Bialystok Technical University, Faculty of Electrical Engineering, ul. Wiejska 45D, 15-351, Bialystok, Poland

    Miroslaw Swiercz

  2. Department of Neurosurgery, Medical University of Bialystok, Bialystok, Poland

    Jan Kochanowicz & Zenon Mariak

  3. Department of Radiology, Division of Neuroradiology, University of Pennsylvania, Philadelphia, USA

    John Weigele, Robert Hurst & Elias R. Melhem

  4. Department of Neurology, UCLA Stroke Center, University of California, Los Angeles, USA

    David S. Liebeskind

  5. Department of Radiology, Division of Neuroradiology, University of Pennsylvania, 3600 Market street, Philadelphia, PA 19104, USA

    Jaroslaw Krejza

  6. Department of Nuclear Medicine, Medical University of Gdańsk, Gdańsk, Poland

    Jaroslaw Krejza

Authors
  1. Miroslaw Swiercz
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  2. Jan Kochanowicz
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  3. John Weigele
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  4. Robert Hurst
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  5. David S. Liebeskind
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  6. Zenon Mariak
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  7. Elias R. Melhem
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  8. Jaroslaw Krejza
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Correspondence to Jaroslaw Krejza.

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Swiercz, M., Kochanowicz, J., Weigele, J. et al. Learning Vector Quantization Neural Networks Improve Accuracy of Transcranial Color-coded Duplex Sonography in Detection of Middle Cerebral Artery Spasm—Preliminary Report. Neuroinform 6, 279–290 (2008). https://doi.org/10.1007/s12021-008-9023-0

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  • DOI: https://doi.org/10.1007/s12021-008-9023-0

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