1. Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.
- Author
-
Wang, Kaidong, Margolis, Samuel, Cho, Jae, Wang, Shaolei, Arianpour, Brian, Jabalera, Alejandro, Yin, Junyi, Hong, Wen, Zhang, Yaran, Zhao, Peng, Zhu, Enbo, Reddy, Srinivasa, and Hsiai, Tzung
- Subjects
attention mechanism ,deep learning ,electrical impedance ,nonalcoholic fatty liver disease ,on‐skin sensor ,Deep Learning ,Animals ,Non-alcoholic Fatty Liver Disease ,Mice ,Electric Impedance ,Disease Models ,Animal ,Early Diagnosis ,Mice ,Knockout ,Humans - 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.
- Published
- 2024