Endovascular Treatment of Distal-M2 Segment Occlusions: A Clinical Registry and Meta-Analysis

Bensoussan, Sacha; Finitsis, Stephanos; Lapergue, Bertrand; Marnat, Gaultier; Sibon, Igor; Moulin, Solène; Olivot, Jean‐Marc; Richard, Sébastien; Rosso, Charlotte; Gory, Benjamin; Clarençon, Frédéric

doi:10.5853/jos.2022.03692

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…The ability of our deep learning algorithm to accurately predict isolated MCA-M2 occlusion with good performance is of significant importance. Although isolated MCA-M2 occlusion is an important and emerging target of the EVT, 24 it was reported that the rate of misdiagnosis for isolated MCA-M2 occlusion is substantially higher than that for intracranial LVO even among the neuroimaging specialists (35.0% vs. 9.7%, respectively), 25 and the use of previously developed deep learning models has yielded worse results (50.8%). 20 This characteristic of isolated MCA-M2 occlusion have influenced our model as well, leading to improved specificity and NPV compared to previous studies, 20,21 despite with somewhat less satisfactory sensitivity and PPV.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Thrombectomy Amenable Vessel Occlusion on Computed Tomography Angiography using Deep Learning

Han,

Lee,

Park

et al. 2024

Preprint

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Objectives: Recent advancements have extended the treatment window for large vessel occlusion in acute ischemic stroke, prompting a shift in the standard of care for patients presenting within 6 to 24 hours. We developed and externally validated an automated deep learning algorithm for detecting thrombectomy amenable vessel occlusion (TAVO) in computed tomography angiography (CTA). Methods: The algorithm was trained on 2,045 acute ischemic stroke patients who underwent CTA, and validation was conducted using two external datasets comprising 64 (external 1) and 313 (external 2) patients with ischemic stroke. TAVO was defined as occlusion in the intracranial internal carotid artery (ICA), or M1/M2 segment of the middle cerebral artery (MCA). Utilizing U-Net for vessel segmentation and EfficientNetV2 for TAVO prediction, the algorithm's diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The mean (SD) age in the training and validation dataset was 68.7 (12.6); 56.3% were men, and 18.0% had TAVO. The algorithm achieved AUC of 0.950 (95% CI, 0.915 to 0.971) in the internal test. For the external datasets 1 and 2, the AUCs were 0.970 (0.897 to 0.997) and 0.971 (0.924 to 0.990), respectively. Notably, the algorithm demonstrated robust sensitivity and specificity (approximately 0.95) for intracranial ICA or M1-MCA occlusion, but a slight reduction in performance for isolated M2-MCA occlusion. Conclusion: This validated algorithm has potential applications in identifying TAVO and could aid less-experienced clinicians, potentially expediting the treatment process for eligible patients.

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