Continuous and precise bladder volume monitoring is essential for patients with lower urinary tract dysfunction (LUTD) to support timely voiding and effective rehabilitation management. However, existing devices lack skin conformity and cannot support reliable "stick-once, long-term daily use" in real-world scenarios. We present BladderSense, a skin-conforming wireless wearable system featuring an X-shaped flexible phased-array ultrasound probe. Its development faces three key challenges: preserving beam focusing under skin deformation, maintaining accurate volume estimation despite bladder position shifts, and enabling low-power wireless transmission despite large raw data volume. To address these obstacles, we employ a suitable-frequency deep-focus design to stabilize beam quality, introduce a dual-orthogonal array with a shared geometric anchor and develop a coordinate-encoded deep learning (DL) model, together enabling bladder tracking and end-to-end volume estimation. An envelope-extraction-based compression scheme further enables Bluetooth Low Energy (BLE) transmission, supporting continuous monitoring with intermittent (1-minute) sensing. Experiments with 10 participants show that BladderSense provides accurate, robust bladder volume estimation across bladder changes, posture transitions, and dynamic daily activities, realizing dependable "stick-once, long-term monitoring" for LUTD patients.

The dental plaque disclosing agent helps guide plaque removal by revealing plaque on the teeth. However, existing toothbrushes cannot provide visualization of the stained plaque beneath the toothpaste foam during brushing. To fill this gap, this paper proposes PIT and uses smartphone to provide real-time plaque visualization during brushing. PIT introduces novel hardware, including a micro camera, four green LEDs, and a mechanical structure, offering a stable camera view of bristle position and bristle rotation. To address the challenge of toothpaste foam obstruction, we derived an optical channel model that guided the design of the light source to enhance plaque visibility. Furthermore, we designed a deep neural network specifically for plaque segmentation under thick foam. Finally, the trained distilled student model was run on a smartphone and evaluated on both denture models and human subjects. The results show that PIT achieved an IoU (Intersection over Union) of 75.22% and a latency of 29 ms, with robust performance across various conditions. Evaluation by 10 participants revealed that PIT helped reduce plaque coverage to 5.6% within two minutes of brushing, significantly outperforming existing advanced toothbrushes.