Artificially intelligent nasal perception for rapid sepsis diagnostics

Abstract

Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due to its high morbidity and mortality rates. A rapid and precise diagnosis of sepsis is essential for better patient outcomes. However, conventional diagnostic methods, such as bacterial cultures, are time-consuming and can delay sepsis diagnosis. Considering these, researchers investigated alternative techniques that detect volatile organic compounds (VOCs) produced by bacteria. In this study, we designed colorimetric gas sensor arrays, which change color upon interaction with biomarkers, offer a direct visual signal, and demonstrate high sensitivity and specificity in detecting sepsis-related VOCs. Furthermore, an artificial intelligence (AI) based algorithm, Rapid Sepsis Boosting (RSBoost), was employed as an analytical technique to enhance diagnostic accuracy (96.2%) in blood sample. This approach significantly improves the speed and accuracy of sepsis diagnostics within 24 h, holding great potential for transforming clinical diagnostics, saving lives, and reducing healthcare costs.

Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due to its high morbidity and mortality rates. A rapid and precise diagnosis of sepsis is essential for better patient outcomes. However, conventional diagnostic methods, such as bacterial cultures, are time-consuming and can delay sepsis diagnosis. Considering these, researchers investigated alternative techniques that detect volatile organic compounds (VOCs) produced by bacteria. In this study, we designed colorimetric gas sensor arrays, which change color upon interaction with biomarkers, offer a direct visual signal, and demonstrate high sensitivity and specificity in detecting sepsis-related VOCs. Furthermore, an artificial intelligence (AI) based algorithm, Rapid Sepsis Boosting (RSBoost), was employed as an analytical technique to enhance diagnostic accuracy (96.2%) in blood sample. This approach significantly improves the speed and accuracy of sepsis diagnostics within 24 h, holding great potential for transforming clinical diagnostics, saving lives, and reducing healthcare costs.