Evidence map›Paper›PMID 35399834›Full record

SynthesisJournal of healthcare engineering2022

Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries.

Sarena Talpur, Fahad Azim, Munaf Rashid, Sidra Abid Syed, Baby Alisha Talpur, Saad Jawaid Khan

Open access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
9.4field-weighted citation impact, top 1% of its field
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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 52 citations in OpenAlex.

  1. Pooled it
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  4. AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024
    Pooled it
  5. Charting the artificial intelligence revolution in orthodontics : A bibliometric analysis of key players, collaborations, and research directions.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026
    Review
  6. Photonic Methods for the Assessment of Lesion Activity.Diagnostics (Basel, Switzerland) · 2026
    Review
  7. Article
  8. Article
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  10. Article
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  14. Early childhood caries (ECC) prediction models using Machine Learning.Journal of clinical and experimental dentistry · 2024
    Article
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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 at 2 institutions in 1 country.

Sarena TalpurDepartment of Biomedical Engineering, Ziauddin University, Karachi, Pakistan.ORCID 0000-0001-6013-7621
Fahad AzimDepartment of Electrical Engineering, Ziauddin University, Karachi, Pakistan.ORCID 0000-0001-6737-4953
Munaf RashidDepartment of Software Engineering, Ziauddin University, Karachi, Pakistan.ORCID 0000-0003-2063-4513
Sidra Abid SyedDepartment of Biomedical Engineering, Ziauddin University, Karachi, Pakistan.ORCID 0000-0002-0647-8270
Baby Alisha TalpurLiaquat University of Medical and Health Sciences, Jamshoro, Pakistan.ORCID 0000-0002-4178-3748
Saad Jawaid KhanDepartment of Biomedical Engineering, Ziauddin University, Karachi, Pakistan.ORCID 0000-0001-7351-2494
Ziauddin University · PKLiaquat University of Medical & Health Sciences · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives: The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods: A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result: This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion: In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.

Indexed as

Artificial IntelligenceDental CariesAlgorithmsChildFemaleHumansMachine LearningNeural Networks, Computer

Identifiers

PMID35399834
PMCPMC8989613
OpenAlexW4220943068

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.