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Article overview
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Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model | Mizuho Nishio
; Sho Koyasu
; Shunjiro Noguchi
; Takao Kiguchi
; Kanako Nakatsu
; Thai Akasaka
; Hiroki Yamada
; Kyo Itoh
; | Date: |
9 Apr 2020 | Abstract: | Background and Purpose: We aimed to develop and evaluate an automatic acute
ischemic stroke-related (AIS) detection system involving a two-stage deep
learning model.
Methods: We included 238 cases from two different institutions. AIS-related
findings were annotated on each of the 238 sets of head CT images by referring
to head magnetic resonance imaging (MRI) images in which an MRI examination was
performed within 24 h following the CT scan. These 238 annotated cases were
divided into a training set including 189 cases and test set including 49
cases. Subsequently, a two-stage deep learning detection model was constructed
from the training set using the You Only Look Once v3 model and Visual Geometry
Group 16 classification model. Then, the two-stage model performed the AIS
detection process in the test set. To assess the detection model’s results, a
board-certified radiologist also evaluated the test set head CT images with and
without the aid of the detection model. The sensitivity of AIS detection and
number of false positives were calculated for the evaluation of the test set
detection results. The sensitivity of the radiologist with and without the
software detection results was compared using the McNemar test. A p-value of
less than 0.05 was considered statistically significant.
Results: For the two-stage model and radiologist without and with the use of
the software results, the sensitivity was 37.3%, 33.3%, and 41.3%,
respectively, and the number of false positives per one case was 1.265, 0.327,
and 0.388, respectively. On using the two-stage detection model’s results, the
board-certified radiologist’s detection sensitivity significantly improved
(p-value = 0.0313).
Conclusions: Our detection system involving the two-stage deep learning model
significantly improved the radiologist’s sensitivity in AIS detection. | Source: | arXiv, 2004.4432 | Services: | Forum | Review | PDF | Favorites |
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