Deep‐Learning Integrated Bioelectronic‐Tissue Interface for Cardiovascular Diagnosis and Prognosis

Abstract Bioelectronic‐tissue interfaces hold promise for monitoring electrophysiological signals and diagnosing cardiovascular diseases. However, current interfaces lack the required tissue adaptability and robust adhesion in the dynamic physiological environment of cardiac tissue, leading to low‐quality signal feedback that hinders precise diagnostics and prognosis for cardiovascular conditions. To address these challenges, a bioadhesive polyampholyte‐based electronic interface (BPAEI) is developed featuring a polyampholyte (PA) network interpreting with amino‐functionalized hyaluronic acid (BPA‐NH₂) and seamless integration with eutectic gallium–indium (EGaIn) liquid metal (LM). This on‐tissue bioelectronic offers high tissue adaptability, de‐swelling, and long‐term stability, sustaining over 1000 cycles with minimal residual strain and less than 2% resistance change. Through a rapid‐drying cross‐linking mechanism, the BPAEI forms an instant and robust adhesive that is tailored for dynamic cardiac cycles, demonstrating an adhesive interfacial toughness of 283.18 Jm⁻², and durable adhesive strength with minimal change even after 1000 peel‐off tests. Furthermore, BPAEI enables triggerable benign detachment and exhibits excellent biocompatibility. Benefiting from these advanced properties, BPAEI can be further integrated with a deep‐learning model for diagnosis and prognosis, facilitating early warnings in myocardial infarction. These advancements pave the way for next‐generation implantable bioelectronics, offering intelligent diagnostics and improved outcomes for cardiovascular disease management.