Evidence map›Paper›PMID 33655198›Full record

ArticleGates open research2020

Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia.

Lauren Etter, Alinani Simukanga, Wenda Qin, Rachel Pieciak, Lawrence Mwananyanda, Margrit Betke, Jackson Phiri, Caroline Carbo, Arnold Hamapa, Chris Gill

Abstract read
In one paragraph

Article in Gates open research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lauren EtterDepartment of Global Health, School of Public Health, Boston University Medical Campus, Boston, MA, 02118, USA.ORCID https://orcid.org/0000-0002-8921-4632
Alinani SimukangaDepartment of Computer Science, School of Natural Sciences, University of Zambia, Lusaka, Zambia.ORCID https://orcid.org/0000-0003-0768-4065
Wenda QinDepartment of Computer Science, College of Arts and Sciences, Boston University, Boston, MA, 02215, USA.ORCID https://orcid.org/0000-0001-9265-6899
Rachel PieciakDepartment of Global Health, School of Public Health, Boston University Medical Campus, Boston, MA, 02118, USA.
Lawrence MwananyandaDepartment of Global Health, School of Public Health, Boston University Medical Campus, Boston, MA, 02118, USA.
Margrit BetkeDepartment of Computer Science, College of Arts and Sciences, Boston University, Boston, MA, 02215, USA.ORCID https://orcid.org/0000-0002-4491-6868
Jackson PhiriDepartment of Computer Science, School of Natural Sciences, University of Zambia, Lusaka, Zambia.ORCID https://orcid.org/0000-0002-4430-1580
Caroline CarboCollege of Engineering, Boston University, Boston, MA, 02215, USA.
Arnold HamapaRight to Care Zambia, Lusaka, Zambia.
Chris GillDepartment of Global Health, School of Public Health, Boston University Medical Campus, Boston, MA, 02118, USA.ORCID https://orcid.org/0000-0003-3353-0617

Funding

Project SEARCH (Scanning EARs for Child Health)R21TW010939 · FIC · BOSTON UNIVERSITY MEDICAL CAMPUS · PI GILL, CHRISTOPHER J, MWANANYANDA, LAWRENCE · 2018 to 2019
$407k
FIC NIH HHS R21 TW010939
6 · The paper itself

Abstract

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.

Indexed as

biometricselectronic medical recordsglobal healthpatient identification

Identifiers

PMID33655198
PMCPMC7887869

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.