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Submitted URL: https://info.viz.ai/e/870371/le-10-3171-2022-8-jns22888-xml/62nzvt/790148394?h=wykrwh8ttajzm68azjjhp-kafsouc1s3irxff...
Effective URL: https://thejns.org/view/journals/j-neurosurg/aop/article-10.3171-2022.8.JNS22888/article-10.3171-2022.8.JNS22888.xml
Submission: On November 11 via api from US — Scanned from DE

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AUTOMATED DETECTION AND ANALYSIS OF SUBDURAL HEMATOMAS USING A MACHINE LEARNING
ALGORITHM

Marco Colasurdo MD1, Nir Leibushor BSc2, Ariadna Robledo MD3, Viren
Vasandani MD3, Zean Aaron Luna BSc3, Abhijit S. Rao BSA3, Roberto Garcia MD3,
Visish M. Srinivasan MD5, Sunil A. Sheth MD6, Naama Avni BSc2, Moleen
Madziva PhD2, Mor Berejick BSc2, Goni Sirota BSc2, Aielet Efrati PhD2, Avraham
Meisel HSD2, Hashem Shaltoni MD4, and Peter Kan MD, MPH3
View More View Less
 * 1 Department of Radiology, Division of Neuroradiology, The University of
   Texas Medical Branch, Galveston, Texas;
 * | 2 Viz.ai Inc., San Francisco, California;
 * | 3 Departments of Neurosurgery and
 * | 4 Neurology, The University of Texas Medical Branch, Galveston, Texas;
 * | 5 Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s
   Hospital and Medical Center, Phoenix, Arizona; and
 * | 6 Department of Neurology, McGovern Medical School, University of Texas
   Health Science Center, Houston, Texas

Publication Date: 30 Sep 2022 Page Range: 1–8 Volume/Issue: Publish Before Print
DOI link: https://doi.org/10.3171/2022.8.JNS22888

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 * ABSTRACT


OBJECTIVE

Machine learning algorithms have shown groundbreaking results in neuroimaging.
Herein, the authors evaluate the performance of a newly developed convolutional
neural network (CNN) to detect and quantify the thickness, volume, and midline
shift (MLS) of subdural hematoma (SDH) from noncontrast head CT (NCHCT).


METHODS

NCHCT studies performed for the evaluation of head trauma in consecutive
patients between July 2018 and April 2021 at a single institution were
retrospectively identified. Ground truth determination of SDH, thickness, and
MLS was established by the neuroradiology report. The primary outcome was
performance of the CNN in detecting SDH in an external validation set, as
measured using area under the receiver operating characteristic curve analysis.
Secondary outcomes included accuracy for thickness, volume, and MLS.


RESULTS

Among 263 cases with valid NCHCT according to the study criteria, 135 patients
(51%) were male, the mean (± standard deviation) age was 61 ± 23 years, and 70
patients were diagnosed with SDH on neuroradiologist evaluation. The median SDH
thickness was 11 mm (IQR 6 mm), and 16 patients had a median MLS of 5 mm (IQR
2.25 mm). In the independent data set, the CNN performed well, with sensitivity
of 91.4% (95% CI 82.3%–96.8%), specificity of 96.4% (95% CI 92.7%–98.5%), and
accuracy of 95.1% (95% CI 91.7%–97.3%); sensitivity for the subgroup with an SDH
thickness above 10 mm was 100%. The maximum thickness mean absolute error was
2.75 mm (95% CI 2.14–3.37 mm), whereas the MLS mean absolute error was 0.93 mm
(95% CI 0.55–1.31 mm). The Pearson correlation coefficient computed to determine
agreement between automated and manual segmentation measurements was 0.97 (95%
CI 0.96–0.98).


CONCLUSIONS

The described Viz.ai SDH CNN performed exceptionally well at identifying and
quantifying key features of SDHs in an independent validation imaging data set.




ABBREVIATIONS

AI = artificial intelligence; CNN = convolutional neural network; LVO = large
vessel occlusion; MLS = midline shift; NCHCT = noncontrast head CT; NPV =
negative predictive value; PPV = positive predictive value; SDH = subdural
hematoma.


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ARTICLE INFORMATION


CONTRIBUTOR NOTES

Correspondence Peter Kan: University of Texas Medical Branch, Galveston, TX.
ptkan@utmb.edu.

INCLUDE WHEN CITING Published online September 30, 2022; DOI:
10.3171/2022.8.JNS22888.

Disclosures Dr. Kan is a consultant for Stryker Neurovascular, Imperative Care,
Cerenovus, and MicroVention and has received grant support from Siemens for
non–study-related clinical or research effort.

Keywords: subdural; hemorrhage; trauma; technology; CT; AI; artificial
intelligence Page Count: 8

HEADINGS

 * In Brief
 * OBJECTIVE
 * METHODS
 * RESULTS
 * CONCLUSIONS
 * ABBREVIATIONS
 * Methods
   * Cohort Characteristics
   * Imaging Settings
   * Pipeline for Analysis
   * CNN Description
   * Statistical Analysis
 * Results
   * Detection and Thickness Measurement
   * MLS and Volume Measurement
 * Discussion
 * Conclusions
 * Acknowledgments
 * Disclosures
 * Author Contributions
 * Supplemental Information
   * Abstract Presentations
 * References

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