Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy

Abstract New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is still limited. This study aims to use classification algorithms with minimally invasive electrical impedance spectroscopy to improve neoplasti...

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Bibliographic Details
Main Authors: Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Classification
Machine learning
Minimally-invasive bioimpedance
Bronchoscopy
Neoplasm
Online Access:https://doi.org/10.1038/s41598-025-94826-0
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https://doi.org/10.1038/s41598-025-94826-0

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