Comparison of anterior mandible anatomical characteristics between obstructive sleep apnea patients and healthy individuals: a combined cone beam computed tomography and polysomnographic study

Shamshiev

Ezhov

et al. 2022

Sci Rep

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This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.

Section: Methodsmentioning

confidence: 99%

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients

Shamshiev

Ezhov

et al. 2022

Sci Rep

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“…Values for the intraclass correlation coefficient are classified from 0 to 1. Intraclass correlation coefficient values were greater than 0.75 express great reliability, and the low coefficient of variation indicated the precision error as an indicator of reproducibility [ 15 ]. Differences in variables were evaluated using chi-square test and paired t -test.…”

Section: Methodsmentioning

confidence: 99%

“…Dentomaxillofacial radiologists and dentists are concerned with carotid artery calcifications since they are often observed as incidental findings on 2D panoramic and 3D CBCT scans. Thus, dentists need to diagnose these calcifications in the CBCT scans [ 15 ]. Table 1 shows the advantages and disadvantages of imaging techniques used in the diagnosis of OSA.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Intracranial and Extracranial Carotid Artery Calcifications between Obstructive Sleep Apnea Patients and Healthy Individuals: A Combined Cone-Beam Computed Tomography and Polysomnographic Study

Firincioglulari

Aksoy

Radiology Research and Practice

et al. 2022

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Purpose. This study aimed to compare the presence and grades of intra- and extracranial carotid artery calcifications between obstructive sleep apnea (OSA) and non-OSA patients. Methods. CBCT records of 190 patients (95 OSA patients and 95 non-OSA patients) were retrospectively collected and analyzed. Patient demographic data, including age and gender for both study groups and body mass index (BMI), and apnea-hypopnea index (AHI) for OSA patients were recorded. The presence of intra- and extracranial carotid artery calcifications and the number of calcifications were noted according to the grading scale. Results. There was a significant difference in carotid artery calcifications between OSA patients and healthy individuals. A total of 56.8% of the OSA patients showed at least one carotid artery calcification, whereas 13.8% of healthy individuals showed at least one carotid artery calcification ( p < 0.05 ). For intracranial calcifications, OSA patients showed a significantly higher prevalence than healthy individuals ( p < 0.05 ). The results showed that as the apnea-hypopnea index increases in OSA patients, the incidence of carotid artery calcification increases simultaneously. AHI > 30 patients showed the highest percentage of calcifications. Conclusion. In conclusion, OSA patients showed a higher prevalence of calcified carotid artery calcifications than healthy individuals. The results can be interpreted as the higher AHI, the more carotid artery calcification occurs. As these lesions can be a precursor of future strokes, 3D MDCT/CBCT images should evaluate meticulously not only extracranial but also intracranially, especially in OSA patients.

“…The dataset of a previous study of ours is used in this study [17]. "CBCT records of 200 patients (100 images of OSA patients and 100 images of the control group) were retrospectively collected and analyzed along with the polysomnography records and body mass index (BMI) of OSA patients at the Department of Allergy, Sleep and Respiratory Diseases.…”

Section: Patient Test Datasetmentioning

confidence: 99%

AI-Based Automatic Segmentation of Craniomaxillofacial Anatomy From CBCT Scans for Automatic Detection of Pharyngeal Airway Evaluations in OSA patients

Shamshiev²,

Ezhov³

et al. 2021

Preprint

Self Cite

This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI system which will procure an easy, errorless and fast method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network based machine learning algorithm did the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used a semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate and severe groups and the mean airway volumes were compared. Narrowest points (mm), airway areas (mm2) and airway volumes (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual and Diagnocat measurements in all groups (p>0.05). According to the results of the Diagnocat and manual segmentation, a successful algorithm which can automatically segment the pharyngeal airway from was created which can be used for a swift and precise measurement of pharyngeal airway volume.