A Cold Atmospheric Plasma Sensor for Identification and Differentiation of Biological Tissues

Abstract

Cold atmospheric plasmas (CAPs) have emerged as the central component to plasma medicine, a relatively new research field in which CAPs have shown promise for a variety of biomedical uses and medical therapies. CAPs comprise of a partially ionized gas that exists at near room temperature and atmospheric pressure. CAPs affect biological materials via chemical, thermal, and electrical interactions that are observable using common plasma characterization measurements. For cases in which the to-be-characterized interface is already exposed (e.g., early skin cancer detection), we propose CAPs can be used for real-time tissue identification in a noninvasive manner. We leverage the sensitivity of CAP interactions with biological interfaces to identify and differentiate biological tissues by using real-time chemical (via optical emission spectra) and electrical (via voltage probes along the circuit) measurements. These information-rich measurements have embedded physics knowledge about the plasma chemistry and its interactions with biological tissues. Thus, we incorporate common physics knowledge to extract and analyze such measurements using machine learning. Our proof-of-concept studies demonstrate that biological tissues can be differentiated with up to 99% test accuracy when differentiating four tissue types (i.e., skin, muscle, bone, and fat) of an ex vivo chicken model. The proposed CAP tissue identification and differentiation approach can effectively augment the medical diagnostic toolkit, including in cancer detection, vascular studies, and real-time surgical analysis.