Next Article in Journal
Whether Renal Pathology Is an Independent Predictor for End-Stage Renal Disease in Diabetic Kidney Disease Patients with Nephrotic Range Proteinuria: A Biopsy-Based Study
Previous Article in Journal
Drug-Coated Balloon versus Plain Balloon Angioplasty in the Treatment of Infrainguinal Vein Bypass Stenosis: A Systematic Review and Meta-Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 12
Issue 1
10.3390/jcm12010084
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population

by
Caroline de Bataille
1,2,
David Bernard
3,4,
Jean Dumoncel
1,
Frédéric Vaysse
1,2,
Sylvain Cussat-Blanc
3,5,
Norbert Telmon
1,6,
Delphine Maret
1,2,† and
Paul Monsarrat
2,4,5,*,†
1
Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
2
School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
3
Institute of Research in Informatics (IRIT) of Toulouse, CNRS—UMR5505, 31062 Toulouse, France
4
RESTORE Research Center, Department of Oral Medicine, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, Toulouse University Hospital (CHU), Batiment INCERE, 4bis Avenue Hubert Curien, 31100 Toulouse, France
5
Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
6
Service de Médecine Légale, Centre Hospitalier Universitaire Rangueil, Avenue du Professeur Jean Poulhès, CEDEX 9, 31059 Toulouse, France
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2023, 12(1), 84; https://doi.org/10.3390/jcm12010084
Submission received: 11 November 2022 / Revised: 12 December 2022 / Accepted: 12 December 2022 / Published: 22 December 2022
(This article belongs to the Topic Bridging Oral Medicine and Systemic Disease)
Browse Figures
Versions Notes

Abstract

The objective of this study is to assess, using cone-beam CT (CBCT) examinations, the correlation between hard and soft anatomical parameters and their impact on the characteristics of the upper airway using symbolic regression as a machine learning strategy. Methods: On each CBCT, the upper airway was segmented, and 24 anatomical landmarks were positioned to obtain six angles and 19 distances. Some anatomical landmarks were related to soft tissues and others were related to hard tissues. To explore which variables were the most influential to explain the morphology of the upper airway, principal component and symbolic regression analyses were conducted. Results: In total, 60 CBCT were analyzed from subjects with a mean age of 39.5 ± 13.5 years. The intra-observer reproducibility for each variable was between good and excellent. The horizontal soft palate measure mostly contributed to the reduction of the airway volume and minimal section area with a variable importance of around 50%. The tongue and the position of the hyoid bone were also linked to the upper airway morphology. For hard anatomical structures, the anteroposterior position of the mandible and the maxilla had some influence. Conclusions: Although the volume of the airway is not accessible on all CBCT scans performed by dental practitioners, this study demonstrates that a small number of anatomical elements may be markers of the reduction of the upper airway with, potentially, an increased risk of obstructive sleep apnea. This could help the dentist refer the patient to a suitable physician.
Keywords: cone-beam CT; upper airway; anatomical parameters; soft tissues; machine learning; symbolic regression cone-beam CT; upper airway; anatomical parameters; soft tissues; machine learning; symbolic regression

Share and Cite

MDPI and ACS Style

de Bataille, C.; Bernard, D.; Dumoncel, J.; Vaysse, F.; Cussat-Blanc, S.; Telmon, N.; Maret, D.; Monsarrat, P. Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population. J. Clin. Med. 2023, 12, 84. https://doi.org/10.3390/jcm12010084

AMA Style

de Bataille C, Bernard D, Dumoncel J, Vaysse F, Cussat-Blanc S, Telmon N, Maret D, Monsarrat P. Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population. Journal of Clinical Medicine. 2023; 12(1):84. https://doi.org/10.3390/jcm12010084

Chicago/Turabian Style

de Bataille, Caroline, David Bernard, Jean Dumoncel, Frédéric Vaysse, Sylvain Cussat-Blanc, Norbert Telmon, Delphine Maret, and Paul Monsarrat. 2023. "Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population" Journal of Clinical Medicine 12, no. 1: 84. https://doi.org/10.3390/jcm12010084

APA Style

de Bataille, C., Bernard, D., Dumoncel, J., Vaysse, F., Cussat-Blanc, S., Telmon, N., Maret, D., & Monsarrat, P. (2023). Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population. Journal of Clinical Medicine, 12(1), 84. https://doi.org/10.3390/jcm12010084

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop