Evidence map›Paper›PMID 38884831›Full record

ReviewAnnals of biomedical engineering2024

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.

Cristiana Adina Șalgău, Anca Morar, Andrei Daniel Zgarta, Diana-Larisa Ancuța, Alexandros Rădulescu, Ioan Liviu Mitrea, Andrei Ovidiu Tănase

Erratum issuedAbstract readReview
In one paragraph

Review in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Cristiana Adina ȘalgăuUniversity of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
Anca MorarNational University of Science and Technology Politehnica Bucharest, Bucharest, Romania. anca.morar@cs.pub.ro.ORCID http://orcid.org/0000-0002-4773-6862
Andrei Daniel ZgartaMinerva University, San Francisco, USA.
Diana-Larisa AncuțaUniversity of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
Alexandros RădulescuUniversity of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
Ioan Liviu MitreaUniversity of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.
Andrei Ovidiu TănaseUniversity of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Indexed as

Machine LearningPeriodonticsDental ImplantationDental ImplantsHumansDental ImplantsArtificial intelligenceDeep learningImplant brands and typesImplant planningMachine learningPeri-implantitisPeriodontology

Identifiers

PMID38884831
PMCPMC11329670

What OpenQuestion holds

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LicenceCC BY
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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.