Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia
| Autor: | Lawrence Mwananyanda, Alinani Simukanga, Caroline Carbo, Jackson Phiri, Margrit Betke, Wenda Qin, Lauren Etter, Rachel Pieciak, Christopher J. Gill, Arnold Hamapa |
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| Rok vydání: | 2020 |
| Předmět: |
biometrics
Biometrics Computer science Population global health Medicine (miscellaneous) 02 engineering and technology Machine learning computer.software_genre Biochemistry Genetics and Molecular Biology (miscellaneous) Facial recognition system Child health Patient identification Unique identifier 03 medical and health sciences 0302 clinical medicine Software Immunology and Microbiology (miscellaneous) electronic medical records 0202 electrical engineering electronic engineering information engineering 030212 general & internal medicine Android (operating system) education patient identification education.field_of_study business.industry Health Policy Public Health Environmental and Occupational Health Articles ComputingMethodologies_PATTERNRECOGNITION 020201 artificial intelligence & image processing Artificial intelligence business computer Research Article |
| Zdroj: | Gates Open Research |
| ISSN: | 2572-4754 |
| DOI: | 10.12688/gatesopenres.13197.1 |
| Popis: | Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification. Unlike many popular biometric approaches (e.g., fingerprints, irises, facial recognition), ears are noninvasive and easily accessible on individuals across a lifespan. Our ear biometric tool uses a combination of hardware and software to identify a person using an image of their ear. The hardware supports an image capturing process that reduces undesired variability. The software uses a pattern recognition algorithm to transform an image of the ear into a unique identifier. We created three cross-sectional datasets of ear images, each increasing in complexity, with the final dataset representing our target use-case population of Zambian infants (N=224, aged 6days-6months). Using these datasets, we conducted a series of validation experiments, which informed iterative improvements to the system. Results of the improved system, which yielded high recognition rates across the three datasets, demonstrate the feasibility of an Android ear biometric tool as a solution to the persisting patient identification challenge. |
| Databáze: | OpenAIRE |
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