Non‐Invasive Detection of Early‐Stage Fatty Liver Disease via an On‐Skin Impedance Sensor and Attention‐Based Deep Learning

Abstract

Abstract Early‐stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non‐invasive and cost‐effective detection method for early‐stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on‐skin sensor with low electrode‐skin contact impedance for early‐stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on‐skin sensor to reduce electrode‐skin contact impedance by increasing double‐layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention‐based deep learning algorithm is introduced to differentiate impedance signals associated with early‐stage NAFLD in high‐fat‐diet‐fed low‐density lipoprotein receptor knockout (Ldlr−/−) mice compared to healthy controls. The integration of an adhesive, soft on‐skin sensor with low electrode‐skin contact impedance and the attention‐based deep learning algorithm significantly enhances the detection accuracy for early‐stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non‐invasive approach for early‐stage NAFLD detection and display a strategy for improved early detection through on‐skin electronics and deep learning.

Keywords

• attention mechanism
• deep learning
• electrical impedance
• nonalcoholic fatty liver disease
• on‐skin sensor