<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>51(3)</volume><submitter>Diaconu A</submitter><pubmed_abstract>&lt;h4>Objectives&lt;/h4>To propose and validate a reliable semi-automatic approach for three-dimensional (3D) analysis of the upper airway (UA) based on voxel-based registration (VBR).&lt;h4>Methods&lt;/h4>Post-operative cone beam computed tomography (CBCT) scans of 10 orthognathic surgery patients were superimposed to the pre-operative CBCT scans by VBR using the anterior cranial base as reference. Anatomic landmarks were used to automatically cut the UA and calculate volumes and cross-sectional areas (CSA). The 3D analysis was performed by two observers twice, at an interval of two weeks. Intraclass correlations and Bland-Altman plots were used to quantify the measurement error and reliability of the method. The relative Dahlberg error was calculated and compared with a similar method based on landmark re-identification and manual measurements.&lt;h4>Results&lt;/h4>Intraclass correlation coefficient (ICC) showed excellent intra- and inter-observer reliability (ICC ≥ 0.995). Bland-Altman plots showed good observer agreement, low bias and no systematic errors. The relative Dahlberg error ranged between 0.51 and 4.30% for volume and 0.24 and 2.90% for CSA. This was lower when compared with a similar, manual method. Voxel-based registration introduced 0.05-1.44% method error.&lt;h4>Conclusions&lt;/h4>The proposed method was shown to have excellent reliability and high observer agreement. The method is feasible for longitudinal clinical trials on large cohorts due to being semi-automatic.</pubmed_abstract><journal>Dento maxillo facial radiology</journal><pagination>20210253</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8925868</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A semi-automatic approach for longitudinal 3D upper airway analysis using voxel-based registration.</pubmed_title><pmcid>PMC8925868</pmcid><pubmed_authors>Diaconu A</pubmed_authors><pubmed_authors>Cattaneo PM</pubmed_authors><pubmed_authors>Holte MB</pubmed_authors><pubmed_authors>Pinholt EM</pubmed_authors></additional><is_claimable>false</is_claimable><name>A semi-automatic approach for longitudinal 3D upper airway analysis using voxel-based registration.</name><description>&lt;h4>Objectives&lt;/h4>To propose and validate a reliable semi-automatic approach for three-dimensional (3D) analysis of the upper airway (UA) based on voxel-based registration (VBR).&lt;h4>Methods&lt;/h4>Post-operative cone beam computed tomography (CBCT) scans of 10 orthognathic surgery patients were superimposed to the pre-operative CBCT scans by VBR using the anterior cranial base as reference. Anatomic landmarks were used to automatically cut the UA and calculate volumes and cross-sectional areas (CSA). The 3D analysis was performed by two observers twice, at an interval of two weeks. Intraclass correlations and Bland-Altman plots were used to quantify the measurement error and reliability of the method. The relative Dahlberg error was calculated and compared with a similar method based on landmark re-identification and manual measurements.&lt;h4>Results&lt;/h4>Intraclass correlation coefficient (ICC) showed excellent intra- and inter-observer reliability (ICC ≥ 0.995). Bland-Altman plots showed good observer agreement, low bias and no systematic errors. The relative Dahlberg error ranged between 0.51 and 4.30% for volume and 0.24 and 2.90% for CSA. This was lower when compared with a similar, manual method. Voxel-based registration introduced 0.05-1.44% method error.&lt;h4>Conclusions&lt;/h4>The proposed method was shown to have excellent reliability and high observer agreement. The method is feasible for longitudinal clinical trials on large cohorts due to being semi-automatic.</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Mar</publication><modification>2025-04-04T08:52:54.704Z</modification><creation>2025-04-04T08:52:54.704Z</creation></dates><accession>S-EPMC8925868</accession><cross_references><pubmed>34644181</pubmed><doi>10.1259/dmfr.20210253</doi></cross_references></HashMap>