Evidence map›Paper›PMID 37796336›Full record

ArticleClinical oral investigations2023

Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea.

Hui Chen, Tao Lv, Qing Luo, Lei Li, Qing Wang, Yanzhong Li, Debo Zhou, Elham Emami, Matthieu Schmittbuhl, Paul van der Stelt and 1 more

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Article in Clinical oral investigations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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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

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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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Hui ChenDepartment of Orthodontics, School and Hospital of Stomatology, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Cheeloo College of Medicine, Shandong University, Jinan, 250100, Shandong, China. h.chen@sdu.edu.cn.
Tao LvDepartment of Orthodontics, School and Hospital of Stomatology, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Cheeloo College of Medicine, Shandong University, Jinan, 250100, Shandong, China. lvtao@sdu.edu.cn.
Qing LuoHospital of Stomatology, Ningbo, Zhejiang, China.
Lei LiCentre for Advanced Jet Engineering Technologies (CaJET), School of Mechanical Engineering, Key Laboratory of High-Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, China.
Qing WangDepartment of Orthodontics, Stomatological Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Yanzhong LiDepartment of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, China.
Debo ZhouKey Laboratory of Special Functional Aggregated Materials, Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Elham EmamiFaculty of Dentistry, McGill University, Montreal, Quebec, Canada.
Matthieu SchmittbuhlFaculty of Dental Medicine, Université de Montréal, Montreal, Quebec, Canada.
Paul van der SteltDepartment of Oral Radilology, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands.
Nelly HuynhFaculty of Dental Medicine, Université de Montréal, Montreal, Quebec, Canada.

Funding

CIHR grant 325899
6 · The paper itself

Abstract

objectivesThe objectives of this study included using the cone beam computed tomography (CBCT) technology to assess: (1) intra- and inter-observer reliability of the volume measurement of the nasal cavity; (2) the accuracy of the segmentation protocol for evaluation of the nasal cavity. MATERIALS AND

methodsThis study used test-retest reliability and accuracy methods within two different population sample groups, from Eastern Asia and North America. Thirty obstructive sleep apnea (OSA) patients were randomly selected from administrative and research oral health data archived at two dental faculties in China and Canada. To assess the reliability of the protocol, two observers performed nasal cavity volume measurement twice with a 10-day interval, using Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA). The accuracy study used a computerized tomography (CT) scan of an OSA patient, who was not included in the study sample, to fabricate an anthropomorphic phantom of the nasal cavity volume with known dimensions (18.9 ml, gold standard). This phantom was scanned using one NewTom 5G (QR systems, Verona, Italy) CBCT scanner. The nasal cavity was segmented based on CBCT images and converted into standard tessellation language (STL) models. The volume of the nasal cavity was measured on the acquired STL models (18.99 ± 0.066 ml).

resultsThe intra-observer and inter-observer intraclass correlation coefficients for the volume measurement of the nasal cavity were 0.980-0.997 and 0.948-0.992 consecutively. The nasal cavity volume measurement was overestimated by 1.1%-3.1%, compared to the gold standard.

conclusionsThe semi-automatic segmentation protocol of the nasal cavity in patients with sleep apnea and by using cone beam computed tomography is reliable and accurate. CLINICAL RELEVANCE: This study provides a reliable and accurate protocol for segmentation of nasal cavity, which will facilitate the clinician to analyze the images within nasoethmoidal region.

Indexed as

Sleep Apnea, ObstructiveSleep Apnea SyndromesCone-Beam Computed TomographyHumansImaging, Three-DimensionalNasal CavityReproducibility of ResultsAccuracyCone beam computed tomographyNasal cavityObstructive sleep apneaReliability

Identifiers

PMID37796336

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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.