Evidence map›Paper›PMID 40354021›Full record

ArticleEuropean archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry2025

Development and evaluation of an early childhood caries prediction model: a deep learning-based hybrid statistical modelling approach.

S Z Eusufzai, N B Jamayet, S Ahmed, M B Islam, W M A W Ahmad, M K Alam

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Article in European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry, 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. Statistical considerations in machine learning-based prediction of early childhood caries.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2026
    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

6 authors.

S Z EusufzaiDepartment of Biostatistics, Universiti Sains Malaysia, Kota Bharu, Malaysia.ORCID https://orcid.org/0000-0002-4128-2804
N B JamayetSchool of Dentistry, IMU University, Kuala Lumpur, Malaysia.ORCID http://orcid.org/0000-0003-4656-0946
S AhmedDepartment of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
M B IslamDepartment of Computing and Software Engineering, Florida Gulf Coast University, Florida, USA.ORCID http://orcid.org/0000-0002-9928-5776
W M A W AhmadDepartment of Biostatistics, Universiti Sains Malaysia, Kota Bharu, Malaysia. wmamir@usm.my.ORCID https://orcid.org/0000-0003-2366-3918
M K AlamCollege of Dentistry, Jouf University, Sakaka, Saudi Arabia.ORCID http://orcid.org/0000-0001-7131-1752

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAn effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction.

methodsThe study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent-child pairs, providing a robust dataset for examining the research questions.

resultsIn the current study, the predictors named, "mother's education" (β

conclusionIt has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention.

Indexed as

Deep LearningDental CariesModels, StatisticalChildChild, PreschoolCross-Sectional StudiesFemaleHumansMaleNeural Networks, ComputerArtificial intelligenceCaries prediction modelEarly childhood cariesMachine learningNeural networks

Identifiers

PMID40354021

What OpenQuestion holds

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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.