Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity.

ABSTRACT:

Background

Intracranial occlusion recanalization fails in 20% of endovascular thrombectomy procedures, and thrombus composition is likely to be an important factor. In this study, we demonstrate that the combination of electrical impedance spectroscopy (EIS) and machine learning constitutes a novel and highly accurate method for the identification of different human thrombus types.

Methods

134 samples, subdivided into four categories, were analyzed by EIS: 29 'White', 26 'Mixed', 12 'Red' thrombi, and 67 liquid 'Blood' samples. Thrombi were generated in vitro using citrated human blood from five healthy volunteers. Histological analysis was performed to validate the thrombus categorization based on red blood cell content. A machine learning prediction model was trained on im

SUBMITTER: Messina P 

PROVIDER: S-EPMC10314040 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature