Evidence map›Paper›PMID 39600121›Full record

ArticleClinical and experimental dental research2024

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.

Wei Chen, Monisha Dhawan, Jonathan Liu, Damie Ing, Kruti Mehta, Daniel Tran, Daniel Lawrence, Max Ganhewa, Nicola Cirillo

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and experimental dental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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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

9 authors.

Wei ChenMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Monisha DhawanMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Jonathan LiuMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Damie IngMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Kruti MehtaMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Daniel TranMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Daniel LawrenceCoTreatAI, CoTreat Pty Ltd., Melbourne, Victoria, Australia.
Max GanhewaCoTreatAI, CoTreat Pty Ltd., Melbourne, Victoria, Australia.
Nicola CirilloMelbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0003-1429-1323

Funding

The authors received no specific funding for this work.
6 · The paper itself

Abstract

objectivesArtificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature. MATERIAL AND

methodsThis review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.

resultsOf the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management.

conclusionsA variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

Indexed as

Artificial IntelligenceClinical Decision-MakingDentistryHumansImage Processing, Computer-AssistedNeural Networks, Computerartificial intelligenceconvolutional neural networksdentistryimage analysis

Identifiers

PMID39600121
PMCPMC11599430

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.