Accuracy of deep learning-based upper airway segmentation

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dc.authoridDuran, Gökhan Serhat / 0000-0001-6152-6178
dc.contributor.authorSüküt, Yağızalp
dc.contributor.authorYurdakurban, Ebru
dc.contributor.authorDuran, Gökhan Serhat
dc.date.accessioned2025-01-27T20:14:59Z
dc.date.available2025-01-27T20:14:59Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIntroduction: In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method. Materials and methods: An automatic segmentation model was trained using the MONAI Label framework to segment the upper airway from CBCT images. An open-source program, ITK-SNAP, was used for semi-automatic segmentation. The accuracy of both methods was evaluated against manual segmentations. Evaluation metrics included Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences. Results: The automatic segmentation group averaged a DSC score of 0.915+0.041, while the semi-automatic group scored 0.940+0.021, indicating clinically acceptable accuracy for both methods. Analysis of the 95% HD revealed that semi-automatic segmentation (0.997+0.585) was more accurate and closer to manual segmentation than automatic segmentation (1.447+0.674). Volumetric comparisons revealed no statistically significant differences between automatic and manual segmentation for total, oropharyngeal, and velopharyngeal airway volumes. Similarly, no significant differences were noted between the semi-automatic and manual methods across these regions. Conclusion: It has been observed that both automatic and semi-automatic methods, which utilise opensource software, align effectively with manual segmentation. Implementing these methods can aid in decision-making by allowing faster and easier upper airway segmentation with comparable accuracy in orthodontic practice. (c) 2024 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
dc.identifier.doi10.1016/j.jormas.2024.102048
dc.identifier.issn2468-8509
dc.identifier.issn2468-7855
dc.identifier.issue2
dc.identifier.pmid39244033
dc.identifier.scopus2-s2.0-85203804565
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.jormas.2024.102048
dc.identifier.urihttps://hdl.handle.net/20.500.12428/21253
dc.identifier.volume126
dc.identifier.wosWOS:001374948200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Stomatology Oral and Maxillofacial Surgery
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectArtificial intelligence (AI)
dc.subjectConvolutional neural networks (CNNs)
dc.subjectUpper airway segmentation
dc.subjectCone-beam computed tomography (CBCT)
dc.titleAccuracy of deep learning-based upper airway segmentation
dc.typeArticle

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