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Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

New software solutions emerged to support radiologists in image interpretation in acute ischemic stroke. This study aimed to validate the performance of computer-aided assessment of the Alberta Stroke Program Early CT score (ASPECTS) for detecting signs of early infarction.

Methods

ASPECT scores were assessed in 119 CT scans of patients with acute middle cerebral artery ischemia. Patient collective was differentiated according to (I) normal brain, (II) leukoencephalopathic changes, (III) infarcts, and (IV) atypical parenchymal defects (multiple sclerosis, etc.). ASPECTS assessments were automatically provided by the software package e-ASPECTS (Brainomix®, UK) (A). Subsequently, three neuroradiologists (B), (C), and (D) examined independently 2380 brain regions. Interrater comparison was performed with the definite infarct core as reference standard after best medical care (thrombolysis and/or thrombectomy).

Results

Interrater comparison revealed higher correlation coefficient of (B) 0.71, (C) 0.76, and of (D) 0.80 with definite infarct core compared to (A) 0.59 for ASPECTS assessment in the acute ischemic stroke setting. While (B), (C), and (D) showed a significant correlation for individual patient groups (I), (II), (III), and (IV), except for (D) (II), (A) was not significant in patient groups with pre-existing changes (II), (III), and (IV). The following sensitivities, specificities, PPV, NPV, and accuracies given in percent were achieved: (A) 83, 57, 55, 82, and 67; (B) 74, 76, 69, 83, and 77; (C) 80.8, 85.2, 76, 84, and 80; (D) 63, 90.7, 82, 79, and 80, respectively.

Conclusion

For ASPECTS assessment, the examined software may provide valid data in case of normal brain. It may enhance the work of neuroradiologists in clinical decision making. A final human check for plausibility is needed, particularly in patient groups with pre-existing cerebral changes.

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Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany

    Nika Guberina, U. Dietrich, A. Radbruch, J. Goebel, C. Deuschl, A. Ringelstein, M. Forsting & C. Mönninghoff

  2. Department of Radiology and Neuroradiology, Mariahilf Mönchengladbach, Mönchengladbach, Germany

    A. Ringelstein

  3. Clinic for Neurology, University Hospital Essen, Essen, Germany

    M. Köhrmann & C. Kleinschnitz

Authors
  1. Nika Guberina
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  2. U. Dietrich
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  3. A. Radbruch
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  4. J. Goebel
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  5. C. Deuschl
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  6. A. Ringelstein
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  7. M. Köhrmann
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  8. C. Kleinschnitz
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  9. M. Forsting
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  10. C. Mönninghoff
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Corresponding author

Correspondence to Nika Guberina.

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No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Guberina, N., Dietrich, U., Radbruch, A. et al. Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Neuroradiology 60, 889–901 (2018). https://doi.org/10.1007/s00234-018-2066-5

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