Automatic segmentation of the pharyngeal airway space with convolutional neural network

Shujaat, Sohaib; Jazil, Omid; Willems, Holger; Gerven, Adriaan Van; Shaheen, Eman; Politis, Constantinus; Jacobs, Reinhilde

doi:10.1016/j.jdent.2021.103705

Cited by 43 publications

(24 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, lack of data heterogeneity remains a limitation, and there is a need to incorporate data from other CBCT devices with varying scanning parameters to justify the generalizability of the tool. In the near future, we plan to integrate other validated individual anatomical regions, such as the mandible, inferior alveolar canal, and pharyngeal airway [ 9 – 11 ]. It is also expected to expand the tool’s ability by integrating data from intra-oral scanners and facial scanners for the creation of a complete virtual patient, which could enhance the delivery of personalized dental care [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…The sample size was calculated based on previous comparable studies using a priori power analysis in G* power 3.1, with a power of 80% and a significance level of 5%[ 9 , 11 ]. In this way, a total dataset of 40 scans of two devices (20 Accuitomo 3D ; 20 Newtom VGi evo ) was selected, consisting of 560 teeth, 80 sinuses, and 40 maxillofacial complexes acquired with different scanning parameters (Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…So far, the authors of several studies have focussed on the segmentation of individual craniomaxillofacial anatomical structures using CNN models [ 8 – 11 ]. However, no evidence exists about the integration of these multiple anatomical structures as a single unit.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Reis

Morgan

Nomidis³

et al. 2022

Clin Oral Invest

Self Cite

View full text Add to dashboard Cite

Objective To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images. Materials and methods A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated. Results From the dataset, 85% scored 7–10, and 15% were within 3–6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm. Conclusion The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP. Clinical relevance The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Reis

Morgan

Nomidis³

et al. 2022

Clin Oral Invest

Self Cite

View full text Add to dashboard Cite

show abstract

“…ICC values were reported as 0.899 by Zhang et al 45 , 0.977 by Leonardi et al 46 , 0.985 by Sin et al 12 , 0.986 by Park et al 47 . Shujaat et al 48 provided precision, recall, accuracy, dice, intersection over union values in their study as 0.97 ± 0.01, 0.96 ± 0.03 1.00 ± 0.00 0.97 ± 0.02 and 0.93 ± 0.03, respectively. In our study, the ICC value between the ground truth and DC was 0.972 which indicates that an excellent reliability was present.…”

Section: Discussionmentioning

confidence: 99%

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

Orhan

Shamshiev

Ezhov

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

“…However, it only focused on the oropharynx but not on the nasopharynx. Some research reported automatic segmentation of the airway space with convolutional neural network on CBCT images [ 34 , 35 ]. We must admit that CBCT offers information on cross-sectional areas, volume, and 3D form that cannot be determined by cephalometric images.…”

Section: Discussionmentioning

confidence: 99%

Automated Evaluation of Upper Airway Obstruction Based on Deep Learning

Jeong

Nang

2023

BioMed Research International

View full text Add to dashboard Cite

Objectives. This study is aimed at developing a screening tool that could evaluate the upper airway obstruction on lateral cephalograms based on deep learning. Methods. We developed a novel and practical convolutional neural network model to automatically evaluate upper airway obstruction based on ResNet backbone using the lateral cephalogram. A total of 1219 X-ray images were collected for model training and testing. Results. In comparison with VGG16, our model showed a better performance with sensitivity of 0.86, specificity of 0.89, PPV of 0.90, NPV of 0.85, and F1-score of 0.88, respectively. The heat maps of cephalograms showed a deeper understanding of features learned by deep learning model. Conclusion. This study demonstrated that deep learning could learn effective features from cephalograms and automated evaluate upper airway obstruction according to X-ray images. Clinical Relevance. A novel and practical deep convolutional neural network model has been established to relieve dentists’ workload of screening and improve accuracy in upper airway obstruction.

show abstract

Automatic segmentation of the pharyngeal airway space with convolutional neural network

Cited by 43 publications

References 35 publications

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

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

Automated Evaluation of Upper Airway Obstruction Based on Deep Learning

Contact Info

Product

Resources

About