Evidence map›Paper›PMID 29304130›Full record

ArticlePloS one2018

Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus.

Guilherme Giacomini, Ana Luiza Menegatti Pavan, João Mauricio Carrasco Altemani, Sergio Barbosa Duarte, Carlos Magno Castelo Branco Fortaleza, José Ricardo de Arruda Miranda, Diana Rodrigues de Pina

Abstract readValidation Study
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023
    Article
  4. The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023
    Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.International journal of computer assisted radiology and surgery · 2020
    Article
  12. Observational
  13. Review
  14. 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

7 authors.

Guilherme GiacominiInstituto de Biociências de Botucatu, Universidade Estadual Paulista (IBB-UNESP), Botucatu, São Paulo, Brazil.
Ana Luiza Menegatti PavanInstituto de Biociências de Botucatu, Universidade Estadual Paulista (IBB-UNESP), Botucatu, São Paulo, Brazil.
João Mauricio Carrasco AltemaniHospital de Clínicas, Universidade Estadual de Campinas (HC-UNICAMP), Campinas, São Paulo, Brazil.
Sergio Barbosa DuarteCentro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Rio de Janeiro, Brazil.
Carlos Magno Castelo Branco FortalezaFaculdade de Medicina de Botucatu, Universidade Estadual Paulista (FMB-UNESP), Botucatu, São Paulo, Brazil.
José Ricardo de Arruda MirandaInstituto de Biociências de Botucatu, Universidade Estadual Paulista (IBB-UNESP), Botucatu, São Paulo, Brazil.
Diana Rodrigues de PinaFaculdade de Medicina de Botucatu, Universidade Estadual Paulista (FMB-UNESP), Botucatu, São Paulo, Brazil.ORCID 0000-0003-1967-1990

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Volume measurements of maxillary sinus may be useful to identify diseases affecting paranasal sinuses. However, literature shows a lack of consensus in studies measuring the volume. This may be attributable to different computed tomography data acquisition techniques, segmentation methods, focuses of investigation, among other reasons. Furthermore, methods for volumetrically quantifying the maxillary sinus are commonly manual or semiautomated, which require substantial user expertise and are time-consuming. The purpose of the present study was to develop an automated tool for quantifying the total and air-free volume of the maxillary sinus based on computed tomography images. The quantification tool seeks to standardize maxillary sinus volume measurements, thus allowing better comparisons and determinations of factors that influence maxillary sinus size. The automated tool utilized image processing techniques (watershed, threshold, and morphological operators). The maxillary sinus volume was quantified in 30 patients. To evaluate the accuracy of the automated tool, the results were compared with manual segmentation that was performed by an experienced radiologist using a standard procedure. The mean percent differences between the automated and manual methods were 7.19% ± 5.83% and 6.93% ± 4.29% for total and air-free maxillary sinus volume, respectively. Linear regression and Bland-Altman statistics showed good agreement and low dispersion between both methods. The present automated tool for maxillary sinus volume assessment was rapid, reliable, robust, accurate, and reproducible and may be applied in clinical practice. The tool may be used to standardize measurements of maxillary volume. Such standardization is extremely important for allowing comparisons between studies, providing a better understanding of the role of the maxillary sinus, and determining the factors that influence maxillary sinus size under normal and pathological conditions.

Indexed as

AdultAlgorithmsCone-Beam Computed TomographyFemaleHumansImaging, Three-DimensionalLinear ModelsMaleMaxillary SinusOrgan SizePattern Recognition, AutomatedReproducibility of ResultsRetrospective Studies

Identifiers

PMID29304130
PMCPMC5755892

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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.