[论文解读] A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate

Yunhao Bian, Dawei Wang+6 more|arXiv (Cornell University)|Jan 25, 2026

Gastrointestinal Bleeding Diagnosis and Treatment被引用 0

一句话总结

一个胶囊大小、具备多波长光学传感的传感器，配备 on-device edge-AI，对 GI 出血流量进行分类，并通过在设备端进行推理来减少能量消耗，而不是流式传输原始数据。

ABSTRACT

Post-endoscopic gastrointestinal (GI) rebleeding frequently occurs within the first 72 hours after therapeutic hemostasis and remains a major cause of early morbidity and mortality. Existing non-invasive monitoring approaches primarily provide binary blood detection and lack quantitative assessment of bleeding severity or flow dynamic, limiting their ability to support timely clinical decision-making during this high-risk period. In this work, we developed a capsule-sized, multi-wavelength optical sensing wireless platform for order-of-magnitude-level classification of GI bleeding flow rate, leveraging transmission spectroscopy and low-power edge artificial intelligence. The system performs time-resolved, multi-spectral measurements and employs a lightweight two-dimensional convolutional neural network for on-device flow-rate classification, with physics-based validation confirming consistency with wavelength-dependent hemoglobin absorption behavior. In controlled in vitro experiments under simulated gastric conditions, the proposed approach achieved an overall classification accuracy of 98.75% across multiple bleeding flow-rate levels while robustly distinguishing diverse non-blood gastrointestinal interference. By performing embedded inference directly on the capsule electronics, the system reduced overall energy consumption by approximately 88% compared with continuous wireless transmission of raw data, making prolonged, battery-powered operation feasible. Extending capsule-based diagnostics beyond binary blood detection toward continuous, site-specific assessment of bleeding severity, this platform has the potential to support earlier identification of clinically significant rebleeding and inform timely re-intervention during post-endoscopic surveillance.

研究动机与目标

• 开发一个胶囊大小的无线光学平台，用于在 GI 道内进行时间分辨的多光谱血液感测。
• 实现 GI 出血流量的数量级级别分类，超越简单的二分检测。
• 实现轻量级的设备端 CNN 推理，以实时评估出血严重程度。
• 验证基于物理的与波长相关的血红蛋白吸收一致性。
• 通过嵌入式处理减少数据传输来展示能效提升。

提出的方法

• 胶囊大小的无线传感器进行时间分辨的多光谱光学测量。
• 采用一个轻量级的二维卷积神经网络用于设备端的流量分类。
• 基于物理的验证确认在不同波长下与血红蛋白吸收一致。
• 推理在胶囊电子设备上执行，以最小化数据传输。
• 体外实验模拟胃部条件，以评估不同水平的出血流量分类，涵盖非血液干扰的情况。

实验结果

研究问题

• RQ1 胶囊大小的多波长光学系统是否能使用 edge-AI 设备端推理来对 GI 出血流量进行分类？
• RQ2 在模拟胃部条件下，设备端处理是否在保持高分类准确率的同时降低能量消耗？
• RQ3 出血流量分类是否与波长相关的血红蛋白吸收物理一致性？

主要发现

• 在受控的体外实验中，多种出血流量水平的分类准确率达到 98.75%。
• 系统能稳健地区分多样的非血液性胃肠干扰。
• 嵌入式推理使能量消耗比连续无线传输原始数据降低约 88%。
• 该方法使胶囊诊断在电池供电下实现更长时间的运行。
• 该平台超越二元血液检测，朝向连续、部位特异的出血严重度评估。