TY - JOUR T1 - Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity JF - Journal of NeuroInterventional Surgery JO - J NeuroIntervent Surg SP - 526 LP - 531 DO - 10.1136/neurintsurg-2021-018631 VL - 15 IS - 6 AU - Pierluca Messina AU - Cédric Garcia AU - Joachim Rambeau AU - Jean Darcourt AU - Ronan Balland AU - Bruno Carreel AU - Myline Cottance AU - Elena Gusarova AU - Julie Lafaurie-Janvore AU - Gor Lebedev AU - Franz Bozsak AU - Abdul I Barakat AU - Bernard Payrastre AU - Christophe Cognard Y1 - 2023/06/01 UR - http://jnis.bmj.com/content/15/6/526.abstract N2 - 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 impedance data to differentiate blood samples from any type of thrombus and in between the four sample categories.Results Histological analysis confirmed the similarity between the composition of in vitro generated thrombi and retrieved human thrombi. The prediction model yielded a sensitivity/specificity of 90%/99% for distinguishing blood samples from thrombi and a global accuracy of 88% for differentiating among the four sample categories.Conclusions Combining EIS measurements with machine learning provides a highly effective approach for discriminating among different thrombus types and liquid blood. These findings raise the possibility of developing a probe-like device (eg, a neurovascular guidewire) integrating an impedance-based sensor. This sensor, placed in the distal part of the smart device, would allow the characterization of the probed thrombus on contact. The information could help physicians identify optimal thrombectomy strategies to improve outcomes for stroke patients.Data are available upon reasonable request. ER -