Evidence map›Paper›PMID 39744558›Full record

ReviewImaging science in dentistry2024

Artificial intelligence application in endodontics: A narrative review.

Dennis Dennis, Siriwan Suebnukarn, Min-Suk Heo, Trimurni Abidin, Cut Nurliza, Nevi Yanti, Wandania Farahanny, Widi Prasetia, Fitri Yunita Batubara

Abstract readReview
In one paragraph

Review in Imaging science in dentistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. 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.

Dennis DennisDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0000-0003-1718-4363
Siriwan SuebnukarnFaculty of Dentistry, Thammasat University, Pathumthani 12121, Bangkok, Thailand.ORCID https://orcid.org/0000-0003-1237-1274
Min-Suk HeoDepartment of Oral and Maxillofacial Radiology School of Dentistry, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0003-3406-0645
Trimurni AbidinDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0000-0003-4865-4874
Cut NurlizaDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0009-0008-4418-1196
Nevi YantiDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0000-0003-1310-0142
Wandania FarahannyDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0000-0001-5157-9866
Widi PrasetiaDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0009-0004-6026-4267
Fitri Yunita BatubaraDepartment of Conservative Dentistry, Faculty of Dentistry, Universitas Sumatera Utara, Medan, Indonesia.ORCID https://orcid.org/0000-0002-4159-9748

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This review aimed to explore the scientific literature concerning the methodologies and applications of artificial intelligence (AI) in the field of endodontics. The findings may equip dentists with the necessary technical knowledge to understand the opportunities presented by AI. Materials and Methods: Articles published between 1992 and 2023 were retrieved through an electronic search of Medline via the PubMed, Scopus, and Google Scholar databases. The search, which was limited to articles published in English, aimed to identify relevant studies by employing the following keywords: "artificial intelligence," "machine learning," "deep learning," "endodontic," "root canal treatment," and "radiography." Ultimately, 71 studies that addressed the application of AI in endodontics were selected. Results: Numerous studies have demonstrated the effectiveness of AI applications in endodontics. These uses encompass the identification of root fractures and periapical lesions, assessment of working length, investigation of root canal system anatomy, prediction of retreatment success, and evaluation of dental pulp stem cell viability. Conclusion: AI technology is poised to advance aspects of endodontics including scheduling, patient care, management of drug-drug interactions, prognostic diagnosis, and the emerging area of robotic endodontic surgery. AI methods have demonstrated accuracy and precision in the identification, assessment, and prediction of diseases. Thus, AI can significantly improve endodontic diagnosis and treatment, increasing the overall efficacy of endodontic therapy.

Indexed as

Artificial IntelligenceDeep LearningEndodonticsRadiography

Identifiers

PMID39744558
PMCPMC11685306

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.