Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

LaGrange, Daniela Dumitriu; Hofmeister, Jérémy; Rosi, Andréa; Vargas, María Isabel; Wanke, Isabel; Machi, Paolo; Lövblad, Karl-Olof

doi:10.1016/j.neuri.2022.100114

Cited by 5 publications

(2 citation statements)

References 111 publications

(143 reference statements)

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“…of thrombus using artificial intelligence is also an emerging method that has shown to be potentially impactful for prognostic and therapeutic decision-making in ischemic stroke. 51 Such an approach can be envisioned in CAT for determinations that have therapeutic significance such as to differentiate chronicity of a thrombus as well as distinguish between bland thrombus and intravascular involvement by tumor. 52,53…”

Section: N O N -C O M M E R C I a L U S E O N L Ymentioning

confidence: 99%

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Patell,

Zwicker,

Singh

et al. 2024

Bleed Thromb Vascul Biol

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The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.

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Section: N O N -C O M M E R C I a L U S E O N L Ymentioning

confidence: 99%

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Patell,

Zwicker,

Singh

et al. 2024

Bleed Thromb Vascul Biol

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“…Unlike manual methods, automatic methods can be used to create full thrombus annotations for a large dataset with minimal effort [ 14 , 15 ]. Additionally, provided the same model is used for segmentation, there will be no inter-observer variability in the automatic annotations.…”

Section: Introductionmentioning

confidence: 99%

Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients

Mojtahedi,

Bruggeman,

van Voorst

et al. 2024

JCM

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(1) Background: For acute ischemic strokes caused by large vessel occlusion, manually assessed thrombus volume and perviousness have been associated with treatment outcomes. However, the manual assessment of these characteristics is time-consuming and subject to inter-observer bias. Alternatively, a recently introduced fully automated deep learning-based algorithm can be used to consistently estimate full thrombus characteristics. Here, we exploratively assess the value of these novel biomarkers in terms of their association with stroke outcomes. (2) Methods: We studied two applications of automated full thrombus characterization as follows: one in a randomized trial, MR CLEAN-NO IV (n = 314), and another in a Dutch nationwide registry, MR CLEAN Registry (n = 1839). We used an automatic pipeline to determine the thrombus volume, perviousness, density, and heterogeneity. We assessed their relationship with the functional outcome defined as the modified Rankin Scale (mRS) at 90 days and two technical success measures as follows: successful final reperfusion, which is defined as an eTICI score of 2b-3, and successful first-pass reperfusion (FPS). (3) Results: Higher perviousness was significantly related to a better mRS in both MR CLEAN-NO IV and the MR CLEAN Registry. A lower thrombus volume and lower heterogeneity were only significantly related to better mRS scores in the MR CLEAN Registry. Only lower thrombus heterogeneity was significantly related to technical success; it was significantly related to a higher chance of FPS in the MR CLEAN-NO IV trial (OR = 0.55, 95% CI: 0.31–0.98) and successful reperfusion in the MR CLEAN Registry (OR = 0.88, 95% CI: 0.78–0.99). (4) Conclusion: Thrombus characteristics derived from automatic entire thrombus segmentations are significantly related to stroke outcomes.

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MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke

Santo,

Jenkins,

Ciecierska

et al. 2024

Clin Neuroradiol

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Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

Cited by 5 publications

References 111 publications

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke

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