Evidence map›Paper›PMID 38066804›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Assessing the Efficacy of AI Segmentation in Diagnostics of Nine Supernumerary Teeth in a Pediatric Patient.

Rasa Mladenovic, Zoran Arsic, Stefan Velickovic, Milan Paunovic

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
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

4 authors at 1 institution in 1 country.

Rasa MladenovicDepartment for Dentistry, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0003-0767-8423
Zoran ArsicDepartment for Dentistry, Faculty of Medicine, University of Pristina, 38220 Kosovska Mitrovica, Serbia.
Stefan VelickovicDepartment for Dentistry, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
Milan PaunovicDepartment of Surgery, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
University of Kragujevac · RS

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a very rare case of a child with nine supernumerary teeth to analyze the potential, benefits, and limitations of artificial intelligence, as well as two commercial tools for tooth segmentation. Artificial intelligence (AI) is increasingly finding applications in dentistry today, particularly in radiography. Special attention is given to models based on convolutional neural networks (CNN) and their application in automatic segmentation of the oral cavity and tooth structures. The integration of AI is gaining increasing attention, and the automation of the detection and localization of supernumerary teeth can accelerate the treatment planning process. Despite advancements in 3D segmentation techniques, relying on trained professionals remains crucial. Therefore, human expertise should remain key, and AI should be seen as a support rather than a replacement. Generally, a comprehensive tool that can satisfy all clinical needs in terms of supernumerary teeth and their segmentation is not yet available, so it is necessary to incorporate multiple tools into practice.

Indexed as

3D segmentationartificial intelligencesupernumerary teeth

Identifiers

PMID38066804
PMCPMC10706728
OpenAlexW4389143927

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.