دورية أكاديمية
Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.
| العنوان: | Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography. |
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| المؤلفون: | Natsuda Kaothanthong, Kamin Atsavasirilert, Soawapot Sarampakhul, Pantid Chantangphol, Dittapong Songsaeng, Stanislav Makhanov |
| المصدر: | PLoS ONE, Vol 17, Iss 12, p e0277573 (2022) |
| بيانات النشر: | Public Library of Science (PLoS), 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not available in every hospital. Moreover, even if it is available in the clinic for the routine tests, emergency often does not have it. Therefore, this paper proposes an end-to-end framework for detection and segmentation of the brain infarct on the ncCT. The computer tomography perfusion (CTp) is used as the ground truth. The proposed ensemble model employs three deep convolution neural networks (CNNs) to process three end-to-end feature maps and a hand-craft features characterized by specific contra-lateral features. To improve the accuracy of the detected infarct area, the spatial dependencies between neighboring slices are employed at the postprocessing step. The numerical experiments have been performed on 18 ncCT-CTp paired stroke cases (804 image-pairs). The leave-one-out approach is applied for evaluating the proposed method. The model achieves 91.16% accuracy, 65.15% precision, 77.44% recall, 69.97% F1 score, and 0.4536 IoU. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| العلاقة: | https://doaj.org/toc/1932-6203Test |
| DOI: | 10.1371/journal.pone.0277573 |
| الوصول الحر: | https://doaj.org/article/d4c9cf6f43ba46899ca2e94e2c80b8f3Test |
| رقم الانضمام: | edsdoj.4c9cf6f43ba46899ca2e94e2c80b8f3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0277573 |