Literature DB >> 31990267

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Wu Qiu1, Hulin Kuang1, Ericka Teleg1, Johanna M Ospel1, Sung Il Sohn1, Mohammed Almekhlafi1, Mayank Goyal1, Michael D Hill1, Andrew M Demchuk1, Bijoy K Menon1.   

Abstract

Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose To develop an automated approach to detect and quantitate infarction by using non-contrast-enhanced CT scans in patients with AIS. Materials and Methods Non-contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation. Results In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59-76 years; 59 men), baseline non-contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27-93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24-48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans (r = 0.76, P < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9-38 mL) and the DW MRI volume (median, 19 mL; IQR, 5-43 mL) was 11 mL (P = .89). Conclusion A machine learning approach for segmentation of infarction on non-contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nael in this issue.

Entities:  

Year:  2020        PMID: 31990267     DOI: 10.1148/radiol.2020191193

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  19 in total

1.  Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.

Authors:  Chen Cao; Zhiyang Liu; Guohua Liu; Song Jin; Shuang Xia
Journal:  Quant Imaging Med Surg       Date:  2022-01

2.  Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography.

Authors:  Shih-Yen Lin; Pi-Ling Chiang; Peng-Wen Chen; Li-Hsin Cheng; Meng-Hsiang Chen; Pei-Chun Chang; Wei-Che Lin; Yong-Sheng Chen
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-03-07       Impact factor: 2.924

Review 3.  Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.

Authors:  Nathan A Shlobin; Ammad A Baig; Muhammad Waqas; Tatsat R Patel; Rimal H Dossani; Megan Wilson; Justin M Cappuzzo; Adnan H Siddiqui; Vincent M Tutino; Elad I Levy
Journal:  World Neurosurg       Date:  2021-12-08       Impact factor: 2.210

4.  Automated detection of brain metastases on non-enhanced CT using single-shot detectors.

Authors:  Shimpei Kato; Shiori Amemiya; Hidemasa Takao; Hiroshi Yamashita; Naoya Sakamoto; Osamu Abe
Journal:  Neuroradiology       Date:  2021-06-10       Impact factor: 2.804

5.  Predictive values of early head computed tomography for survival outcome after cardiac arrest in childhood: a pilot study.

Authors:  Kenichi Tetsuhara; Noriyuki Kaku; Yuka Watanabe; Masaya Kumamoto; Yuko Ichimiya; Soichi Mizuguchi; Kanako Higashi; Wakato Matsuoka; Yoshitomo Motomura; Masafumi Sanefuji; Akio Hiwatashi; Yasunari Sakai; Shouichi Ohga
Journal:  Sci Rep       Date:  2021-06-08       Impact factor: 4.379

6.  Stroke medicine terminology: imprecise, wordy, and misleading.

Authors:  Rüdiger von Kummer; Lisa S Babinec
Journal:  Neuroradiology       Date:  2021-04-14       Impact factor: 2.804

7.  Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?

Authors:  Mawya A Khafaji; Mohammed A Safhi; Roia H Albadawi; Salma O Al-Amoudi; Salah S Shehata; Fadi Toonsi
Journal:  Saudi Med J       Date:  2022-01       Impact factor: 1.422

8.  Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non-Contrast Material-enhanced CT Scans of the Brain Using Simulated Lesions.

Authors:  Søren Christensen; Michael Mlynash; Julian MacLaren; Christian Federau; Gregory W Albers; Maarten G Lansberg
Journal:  Radiol Artif Intell       Date:  2021-05-12

Review 9.  Artificial Intelligence and Acute Stroke Imaging.

Authors:  J E Soun; D S Chow; M Nagamine; R S Takhtawala; C G Filippi; W Yu; P D Chang
Journal:  AJNR Am J Neuroradiol       Date:  2020-11-26       Impact factor: 3.825

10.  Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.

Authors:  Chengyan Wang; Zhang Shi; Ming Yang; Lixiang Huang; Wenxing Fang; Li Jiang; Jing Ding; He Wang
Journal:  J Cereb Blood Flow Metab       Date:  2021-06-08       Impact factor: 6.960

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