GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Ovid Technologies (Wolters Kluwer Health)  (2)
Material
Publisher
  • Ovid Technologies (Wolters Kluwer Health)  (2)
Person/Organisation
Language
Years
Subjects(RVK)
  • 1
    Online Resource
    Online Resource
    Automated Detection and Location Specification of Large Vessel Occlusion on Computed Tomography Angiography in Acute Ischemic Stroke
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Stroke: Vascular and Interventional Neurology Vol. 2, No. 4 ( 2022-07)
    Details
    In: Stroke: Vascular and Interventional Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 2, No. 4 ( 2022-07)
    Abstract: Fast and accurate detection of large vessel occlusions (LVOs) is crucial in selection of patients with acute ischemic stroke for endovascular treatment. We assessed accuracy of an automated LVO detection algorithm with LVO localization feature. Methods Consecutive patients who underwent computed tomography angiography in 2 centers between January 2018 and September 2019 and between June and November 2020 for suspected anterior circulation LVO were retrospectively included. Reference standard for presence and site of an anterior circulation LVO (intracranial internal carotid artery, M1, or M2 segments of the middle cerebral artery) was established by consensus of 2 independent neuroradiologist readings. All computed tomography angiographies were processed by StrokeViewer‐LVO, Nicolab. Accuracy of this algorithm with LVO localization feature was assessed. Results In total, computed tomography angiographies of 364 patients with suspected anterior circulation LVO were analyzed (mean age 67±15 years; 185 male patients). A total of 180 patients (49%) had an LVO (intracranial internal carotid artery [n=49 (27%)], M1 [n=91 (51%)] , and M2 [n=40 (22%)]). Sensitivity and specificity for LVO detection were, respectively, 91% (95% CI, 86%–95%) and 87% (95% CI, 81%–91%). NPV and PPV were, respectively, 91% (95% CI, 86%–94%) and 87% (95% CI, 82%–91%). Accuracy of the LVO localization feature was 95%. Median upload‐to‐notification time was 04:31 (interquartile range, 04:21–05:50) minutes. Conclusions The automated LVO detection algorithm evaluated in this study, rapidly and accurately detected anterior circulation LVOs with high accuracy of the LVO localization feature. Therefore, it is a suitable screening tool to support and speed up diagnosis of stroke.
    Type of Medium: Online Resource
    ISSN: 2694-5746
    URL: Article
    DOI: 10.1161/SVIN.121.000158
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 3144224-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Abstract WP70: Artificial Intelligence Based Detection of Large Vessel Occlusion on Non-Contrast Computed Tomography in Stroke
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Stroke Vol. 50, No. Suppl_1 ( 2019-02)
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 50, No. Suppl_1 ( 2019-02)
    Abstract: Introduction: Rapid detection of a large vessel occlusion (LVO) in acute ischemic stroke is beneficial as it may accelerate therapeutic management. The hyperdense artery sign (HAS) on non-contrast CT (NCCT) is result of an acute thrombolytic occlusion and is known as the earliest radiological marker to identify LVO. However, HAS is not apparent for all LVOs and detection by a less-trained eye can be sub-optimal. Artificial intelligence (AI) may support the non-trained radiologist in swift and accurate LVO detection. We evaluated the LVO detection accuracy of AI, and compared this to human expert performance. Materials and Methods: We used a convolutional neural network (CNN) developed by Nico.lab (www.nico-lab.com) that automatically detects and segments thrombi on NCCT using a patch-based approach. The CNN was trained using thrombus and non-thrombus patches, combined with the contralateral side. In a retrospective analysis of thin-slice NCCTs, obtained from Amsterdam UMC patients, with (ICA-T, M1, or M2) and without occlusions (stroke mimics), the CNN and two observers ( 〉 15y and 〉 4y of experience) assessed LVO-presence and location. Ground truth was established by consensus between two different experts with 〉 5y of experience, using both CTA and NCCT. Thrombus segmentations were considered accurate if segmentations overlapped with the ground truth. Sensitivity and specificity of the LVO detection were assessed. Results: We included 107 patients, of which 59 had proven LVOs. Nico.lab CNN showed a thrombus segmentation accuracy of 81%, vs. 81% and 77% of human experts. Sensitivity of LVO detection by CNN was 0.86, vs. 0.95 and 0.79 for human observers. Specificity was 0.65 for the CNN vs. 0.58 and 0.82 for human experts respectively. Conclusion: AI-based LVO detection on NCCT showed comparable results to expert observers. This supportive tool could lead to earlier detection of LVO in acute stroke situations. Fig 1A. HAS; B. CNN output (red); C. clot on CTA (blue arrow)
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/str.50.suppl_1.WP70
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2019
    detail.hit.zdb_id: 1467823-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...