Evidence map›Paper›PMID 39720666›Full record

ArticleInfectious Disease Modelling2025

Deep learning model meets community-based surveillance of acute flaccid paralysis.

Gelan Ayana, Kokeb Dese, Hundessa Daba Nemomssa, Hamdia Murad, Efrem Wakjira, Gashaw Demlew, Dessalew Yohannes, Ketema Lemma Abdi, Elbetel Taye, Filimona Bisrat and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2025. 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

16 authors.

Gelan AyanaSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Kokeb DeseSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Hundessa Daba NemomssaSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Hamdia MuradSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Efrem WakjiraFaculty of Civil and Environmental Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Gashaw DemlewFaculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Dessalew YohannesFaculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia.
Ketema Lemma AbdiDepartment of Reproductive Health, Faculty of Public Health, Jimma University, Jimma, 378, Ethiopia.
Elbetel TayeComputer Vision Division, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia.
Filimona BisratCORE Group Partner Project, Addis Ababa, 5674, Ethiopia.
Tenager TadesseCORE Group Partner Project, Addis Ababa, 5674, Ethiopia.
Legesse KidanneCORE Group Partner Project, Addis Ababa, 5674, Ethiopia.
Se-Woon ChoeDepartment of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39253, South Korea.
Netsanet Workneh GidiDepartment of Pediatrics & Child Health, Jimma Institute of Health, Jimma University, Jimma, 378, Ethiopia.
Bontu HabtamuAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada.
Jude KongArtificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, ON, M5T 3M7, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (

Indexed as

Acute flaccid paralysisCommunityComputer visionDeep learning modelSurveillanceTransfer learning

Identifiers

PMID39720666
PMCPMC11666939

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.