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Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa

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Advances in Soft Computing (MICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14392))

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Abstract

Assessing the condition and visibility of veins is a crucial step before obtaining intravenous access in the antecubital fossa, which is a common procedure to draw blood or administer intravenous therapies (IV therapies). Even though medical practitioners are highly skilled at intravenous cannulation, they usually struggle to perform the procedure in patients with low visible veins due to fluid retention, age, overweight, dark skin tone, or diabetes. Recently, several investigations proposed combining Near Infrared (NIR) imaging and deep learning (DL) techniques for forearm vein segmentation. Although they have demonstrated compelling results, their use has been rather limited owing to the portability and precision needs to perform venipuncture. In this paper, we aim to contribute in bridging this gap using three strategies. First, we introduce a new NIR-based forearm vein segmentation dnataset of 2016 labelled images collected from 1008 subjects with low visible veins. Second, we propose a modified U-Net architecture that locates veins specifically in the antecubital fossa region of the examined patient. Finally, a compressed version of the proposed architecture was deployed inside an bespoke, portable vein finder device after testing four common embedded microcomputers and four common quantization modalities. Experimental results showed that the model compressed with Dynamic Range Quantization and deployed on a Raspberry Pi 4B card produced the best execution time and precision balance, with 5.14 FPS and 0.957 of latency and Intersection over Union (IoU), respectively. These results show promising performance inside a resource-restricted low-cost device. The full implementation and data are available at: https://github.com/EdwinTSalcedo/CUBITAL

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Universidad Católica Boliviana “San Pablo”, La Paz, Bolivia

    Edwin Salcedo & Patricia Peñaloza

Authors
  1. Edwin Salcedo
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  2. Patricia Peñaloza
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Correspondence to Edwin Salcedo .

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Editors and Affiliations

  1. Center for Computing Research, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, Mexico

    Hiram Calvo

  2. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Hiram Ponce

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Salcedo, E., Peñaloza, P. (2024). Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Soft Computing. MICAI 2023. Lecture Notes in Computer Science(), vol 14392. Springer, Cham. https://doi.org/10.1007/978-3-031-47640-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-47640-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47639-6

  • Online ISBN: 978-3-031-47640-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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