Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model. (November 2020)

• Record Type: Journal Article
• Authors: Nishio, Mizuho; Koyasu, Sho; Noguchi, Shunjiro; Kiguchi, Takao; Nakatsu, Kanako; Akasaka, Thai; Yamada, Hiroki; Itoh, Kyo
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Acute ischemic stroke -- Deep learning -- Non-contrast computed tomography -- Magnetic resonance imaging -- Computer-aided detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105711
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Detection system of acute ischemic stroke on non-contract CT images was developed. Two-stage deep learning model was used for the system. The system could be used to assist specialists in their clinical practice. The system might improve the clinical outcomes of acute ischemic stroke. Abstract: Background and objective: Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic 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.