Polymer-based bioresorbable vascular scaffolds (BVS) have garnered significant attention in biomedical applications. Among various BVS, polycaprolactone (PCL)-based scaffolds exhibit excellent biocompatibility, flexibility, chemical stability, and controlled degradation. However, their low radial strength limits practical applicability. Moreover, most reported BVS require periodic postimplantation monitoring to enable early detection of in-stent restenosis and thrombosis. To overcome these limitations, we fabricate a carbon nanotube (CNT)-reinforced PCL BVS using a 3D printing process, enabling patient-specific customization while significantly improving mechanical strength and durability. The proposed PCL/CNT-based stent not only serves as a structural scaffold but also facilitate real-time vascular pressure monitoring by integrating a wireless LC capacitive pressure sensor. The LC pressure sensor is microfabricated using microelectromechanical systems (MEMS) technology and exhibits highly stable resonance characteristics. A key innovation is the integration of a supporting micropillar within the capacitor cavity, which minimizes structural deformation and ensures a stable capacitance response. Mechanical testing demonstrates that PCL/CNT stents achieve significantly higher radial force (0.1 N/mm) compared to pristine PCL (0.013 N/mm). The wireless sensor exhibits high sensitivity (49 kHz/mmHg) with minimal capacitance variation (±5%). <i>In-vitro</i> studies in a phantom experiment confirm stable resonance frequency fluctuations that accurately correlate with hemodynamic changes. This smart stent integrates biodegradable nanocomposites, 3D printing, and wireless sensing, providing a noninvasive platform for restenosis and thrombosis monitoring. It marks a significant advancement in cardiovascular implants, paving the way for personalized and proactive patient care.

Polymer-based bioresorbable vascular scaffolds (BVS) have garnered significant attention in biomedical applications. Among various BVS, polycaprolactone (PCL)-based scaffolds exhibit excellent biocompatibility, flexibility, chemical stability, and controlled degradation. However, their low radial strength limits practical applicability. Moreover, most reported BVS require periodic postimplantation monitoring to enable early detection of in-stent restenosis and thrombosis. To overcome these limitations, we fabricate a carbon nanotube (CNT)-reinforced PCL BVS using a 3D printing process, enabling patient-specific customization while significantly improving mechanical strength and durability. The proposed PCL/CNT-based stent not only serves as a structural scaffold but also facilitate real-time vascular pressure monitoring by integrating a wireless LC capacitive pressure sensor. The LC pressure sensor is microfabricated using microelectromechanical systems (MEMS) technology and exhibits highly stable resonance characteristics. A key innovation is the integration of a supporting micropillar within the capacitor cavity, which minimizes structural deformation and ensures a stable capacitance response. Mechanical testing demonstrates that PCL/CNT stents achieve significantly higher radial force (0.1 N/mm) compared to pristine PCL (0.013 N/mm). The wireless sensor exhibits high sensitivity (49 kHz/mmHg) with minimal capacitance variation (±5%). <i>In-vitro</i> studies in a phantom experiment confirm stable resonance frequency fluctuations that accurately correlate with hemodynamic changes. This smart stent integrates biodegradable nanocomposites, 3D printing, and wireless sensing, providing a noninvasive platform for restenosis and thrombosis monitoring. It marks a significant advancement in cardiovascular implants, paving the way for personalized and proactive patient care.

Abstract To address the complication of in‐stent restenosis that occurs with traditional stent treatments, this study proposes an innovative hybrid smart stent‐based medical system. This approach allows to overcome the limitations of existing bare metal or polymer‐based smart stents, which interfere with radio frequency signals, less deformability, or do not provide adequate radial support, respectively. The proposed hybrid stent, which uses a Co/Cr–polycaprolactone (PCL)–Co/Cr configuration connected by a unique dual inverted Y‐type connector for metal–polymer integration is integrated with a LC wireless pressure sensor fabricated through a semiconductor process. The fabricated hybrid stent made by laser machining and custom‐made 3D printing, offers excellent properties such as radial strength (0.125 N/mm) and flexibility (2 N mm 2 ) and provides intravascular information to the outside through the integrated sensor without signal degradation. After basic experiments using a phantom, animal experiments are conducted by combining the fabricated sensor with artificial blood vessel, and the results measured by the external antenna system are consistent with the results of a commercial reference sensor. The proposed wireless sensor‐based smart stents and artificial blood vessels aim to gather diverse patient health data for integration with artificial intelligence, laying the groundwork for next‐generation medical innovation.

In this paper, we introduce an innovative high-sensitivity wireless LC pressure sensor that is carefully designed for integration into biodegradable polymer stents, providing a remarkable improvement in restenosis detection. The key to this breakthrough is a significant increase in the cavity volume of the capacitive pressure sensor in the design. This design optimization improves the sensor sensitivity by a factor of 7. This improved sensitivity has been consistently demonstrated through our comprehensive performance evaluation (phantom and animal experiment) in air and liquid conditions while confirming the robustness and reliability of the sensor in various environments. The smart stent proposed in this study is the first to demonstrate practical medical potential.

This outcome substantiates that our approach can reduce the repetitions for finding the final SRT, and, as the result, the hearing test time can be reduced.

An automatic sleep respiratory cycle segmentation method in time-domain is introduced. Unlike the methods using spectral analysis that require high computational process such as Fourier transform, our approach uses a nonoverlapping window for simple computation in time domain, which is suitable for low power computing systems. Typical sleep breathing sound files over 6-hour length were used to evaluate our method, and the experimental results showed that our method demonstrated the accuracy over 95% and possible improvement with further investigation on parameter optimization for the steps by close examination of the signal characteristics.