Evidence map›Paper›PMID 39329678›Full record

ReviewBioengineering (Basel, Switzerland)2024

Toward Digital Periodontal Health: Recent Advances and Future Perspectives.

Fatemeh Soheili, Niloufar Delfan, Negin Masoudifar, Shahin Ebrahimni, Behzad Moshiri, Michael Glogauer, Ebrahim Ghafar-Zadeh

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 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

14 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Pooled it
  4. Article
  5. Review
  6. Assessment of gingival characteristics by professional versus smartphone imaging.Journal of advanced periodontology & implant dentistry · 2026
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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

7 authors.

Fatemeh SoheiliBiologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.ORCID 0000-0001-6329-4630
Niloufar DelfanBiologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.ORCID 0009-0008-4377-693X
Negin MasoudifarDepartment of Internal Medicine, University Health Network, Toronto, ON M5G 2C4, Canada.
Shahin EbrahimniBiologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.
Behzad MoshiriSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran P9FQ+M8X, Kargar, Iran.ORCID 0000-0002-8390-4093
Michael GlogauerFaculty of Dentistry, University of Toronto, Toronto, ON M5G 1G6, Canada.ORCID 0000-0002-6248-5977
Ebrahim Ghafar-ZadehBiologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.ORCID 0000-0002-7090-2304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Indexed as

artificial intelligencedeep neural networkmedical imagingmicroscopic imagingperiodontal diseaseperiodontitissystemic disorders

Identifiers

PMID39329678
PMCPMC11428937

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.