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| Autores principales: | , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.18929669 |
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| _version_ | 1866901534127685632 |
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| author | Ivchenko, Oleh Grybeniuk, Dmytro |
| author_facet | Ivchenko, Oleh Grybeniuk, Dmytro |
| contents | Examines the intersection of computer vision, emergency medicine, and forensic identification through Ukraine's 2026 national tattoo identification service. Presents a technical framework for automated feature extraction and similarity matching integrated into the ScanLab medical AI platform. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18929669 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Tattoo-Based Emergency Patient Identification: Extending Medical AI Diagnostics Beyond Imaging — Evidence from Ukraine's National Registry Ivchenko, Oleh Grybeniuk, Dmytro tattoo identification emergency medicine missing persons computer vision feature extraction GDPR biometric data Ukraine ScanLab Examines the intersection of computer vision, emergency medicine, and forensic identification through Ukraine's 2026 national tattoo identification service. Presents a technical framework for automated feature extraction and similarity matching integrated into the ScanLab medical AI platform. |
| title | Tattoo-Based Emergency Patient Identification: Extending Medical AI Diagnostics Beyond Imaging — Evidence from Ukraine's National Registry |
| topic | tattoo identification emergency medicine missing persons computer vision feature extraction GDPR biometric data Ukraine ScanLab |
| url | https://doi.org/10.5281/zenodo.18929669 |