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Messina P, Garcia C, Rambeau J, Darcourt J, Balland R, Carreel B, Cottance M, Gusarova E, Lafaurie-Janvore J, Lebedev G, Bozsak F, Barakat AI, Payrastre B, Cognard C. Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity. J Neurointerv Surg 2022; 15:526-530. [PMID: 35478173 DOI: 10.1136/neurintsurg-2021-018631] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/13/2022] [Indexed: 11/03/2022]
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 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.
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Affiliation(s)
| | - Cédric Garcia
- INSERM, U1048, Toulouse, France.,Université Toulouse III Paul Sabatier, Toulouse, France.,Department of Hematology, CHU Toulouse, Hôpital Rangueil, Toulouse, France
| | | | - Jean Darcourt
- INSERM, U1048, Toulouse, France.,Université Toulouse III Paul Sabatier, Toulouse, France.,Department of Diagnostic and Therapeutic Neuroradiology, CHU Toulouse, Hôpital Purpan, Toulouse, France
| | | | | | | | | | | | | | | | - Abdul I Barakat
- LadHyX, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Bernard Payrastre
- INSERM, U1048, Toulouse, France.,Université Toulouse III Paul Sabatier, Toulouse, France.,Department of Hematology, CHU Toulouse, Hôpital Rangueil, Toulouse, France
| | - Christophe Cognard
- INSERM, U1048, Toulouse, France.,Université Toulouse III Paul Sabatier, Toulouse, France.,Department of Diagnostic and Therapeutic Neuroradiology, CHU Toulouse, Hôpital Purpan, Toulouse, France
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