Evidence map›Paper›PMID 41523291›Full record

ReviewJournal of orthodontic science2025

Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review.

Sharvari Mairal, Vipul Kumar Sharma, K J Jakshmi, Ulhaas Kashyap, Mahesh Khairnar, T P Chaturvedi, Ankita Jamdade

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of orthodontic science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Sharvari MairalDepartment of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Vipul Kumar SharmaDepartment of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
K J JakshmiDepartment of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Ulhaas KashyapDepartment of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Mahesh KhairnarDepartment of Public Health Dentistry, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
T P ChaturvediDepartment of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Ankita JamdadeDepartment of Mathematics, University of Waterloo, Waterloo, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the diagnostic accuracy of artificial intelligence-based models in the determination of tooth extraction in orthodontic treatment planning. MATERIALS AND

methodsA comprehensive literature search was conducted in multiple databases (PubMed, LILACS, Web of Science, Scopus, EBSCO, and Google Scholar) up to June, 2024. Studies that met the inclusion criteria based on the PIRD (Participants, Index test, Reference test, Diagnostic) framework were selected. The risk of bias of included studies was assessed using the QUADAS-2 tool, and their methodological quality was evaluated as well using a standardized checklist.

resultsOut of 361 retrieved records, eleven studies were included in this review. Nine of these studies achieved a score of over 50% on the AI quality checklist, indicating acceptable methodological quality. However, a comprehensive assessment using the QUADAS-2 tool revealed that all studies had some level of risk of bias, particularly in patient selection, the conduct of AI-based predictions, and the reference standard used.

conclusionNeural networks and classifier models demonstrated the high level of accuracy ranging from 82% to 94% in determining the optimal tooth extraction protocol. However, to ensure reliable predictions, artificial intelligence-based models should be rigorously trained, incorporating a comprehensive range of factors.

Indexed as

Accuracyartificial intelligencediagnosismachine learningorthodonticstooth extraction

Identifiers

PMID41523291
PMCPMC12788713

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.