A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910000098574336 |
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| author | Bian, Yunhao Wang, Dawei Shen, Mingyang Li, Xinze Shi, Jiayi Zhou, Ziyao Cao, Tiancheng Huang, Hen-Wei |
| author_facet | Bian, Yunhao Wang, Dawei Shen, Mingyang Li, Xinze Shi, Jiayi Zhou, Ziyao Cao, Tiancheng Huang, Hen-Wei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_17752 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate Bian, Yunhao Wang, Dawei Shen, Mingyang Li, Xinze Shi, Jiayi Zhou, Ziyao Cao, Tiancheng Huang, Hen-Wei Image and Video Processing Systems and Control 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. |
| title | A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate |
| topic | Image and Video Processing Systems and Control |
| url | https://arxiv.org/abs/2601.17752 |