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Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest.
Kenda, Martin; Cheng, Zhuo; Guettler, Christopher; Storm, Christian; Ploner, Christoph J; Leithner, Christoph; Scheel, Michael.
Affiliation
  • Kenda M; Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Cheng Z; BIH Charité Junior Digital Clinician Scientist Program, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany.
  • Guettler C; Department of Neuroradiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Storm C; Department of Neuroradiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Ploner CJ; Department of Nephrology and Intensive Care Medicine-Circulatory Arrest Center Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Leithner C; Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Scheel M; Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Front Neurol ; 13: 990208, 2022.
Article in En | MEDLINE | ID: mdl-36313501
ABSTRACT

Background:

Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.

Methods:

Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.

Results:

Inter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.

Conclusion:

Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Guideline / Prognostic_studies / Qualitative_research Language: En Journal: Front Neurol Year: 2022 Type: Article Affiliation country: Germany

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Guideline / Prognostic_studies / Qualitative_research Language: En Journal: Front Neurol Year: 2022 Type: Article Affiliation country: Germany