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Ng, Kian Wei; Furqan, Mohammad Shaheryar; Gao, Yujia; Ngiam, Kee Yuan; Khoo, Eng Tat
Electronics, 01/2023, Letnik: 12, Številka: 2Journal Article
Attaining venous access is a common requirement for clinical care worldwide, with a non-negligible portion of cases often being categorized as ‘difficult intravenous access’. Such complications result in far-reaching consequences affecting clinicians and patients alike. We propose a mixed-reality-based vein detection and visual guidance system that provides several key advantages, including a wider field of view, flexible operating distance, and hands-free, intuitive usage compared to existing solutions. A semi-supervised learning approach was used in model training to circumvent dataset availability limitations. Quantitative evaluation showed that the semi-supervised approach improved vein detection performance and temporal consistency. The system was also implemented and trialed in a clinical setting to assess real-world usability. Initial, preliminary assessment of HoloVein by medical professionals in a clinical setting showed improvements in detection quality using the semi-supervised approach over the baseline model. This result was deemed to be promising from a clinical perspective and could set the stage for more widespread mixed-reality venipuncture guidance tools in the future.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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