Deep Learning Based Airway Segmentation Using Key Point Prediction

Park, Jinyoung; Ryu, Jihye; Nam, Inhye; Kim, Sol-A; Shin, Sang‐Hun; Lee, Jae Yeol

doi:10.3390/app11083501

Cited by 14 publications

(11 citation statements)

References 40 publications

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“…All studies evaluated the accuracy of AI systems in segmenting and calculating airway volume based on CNN and RNN models. Three articles used their own model for software usage while one of the remaining two used the Mimics 19.0, InVivo 5 software and the other one the Diognocat, InVivo 5 software [26][27][28][29][30]. The procedure of article selection is presented on a flow diagram (Figure 1), and data are briefly presented in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review

Tsolakis

Kolokitha

Papadopoulou

et al. 2022

Life

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Background: The use of artificial intelligence (AI) in health sciences is becoming increasingly popular among doctors nowadays. This study evaluated the literature regarding the use of AI for CBCT airway analysis. To our knowledge, this is the first systematic review that examines the performance of artificial intelligence in CBCT airway analysis. Methods: Electronic databases and the reference lists of the relevant research papers were searched for published and unpublished literature. Study selection, data extraction, and risk of bias evaluation were all carried out independently and twice. Finally, five articles were chosen. Results: The results suggested a high correlation between the automatic and manual airway measurements indicating that the airway measurements may be automatically and accurately calculated from CBCT images. Conclusions: According to the present literature, automatic airway segmentation can be used for clinical purposes. The main key findings of this systematic review are that the automatic airway segmentation is accurate in the measurement of the airway and, at the same time, appears to be fast and easy to use. However, the present literature is really limited, and more studies in the future providing high-quality evidence are needed.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review

Tsolakis

Kolokitha

Papadopoulou

et al. 2022

Life

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“…Following an extensive literature research, we have given the ICC values of 5 studies that compared the segmentation of AI and ground truth in Table 4 . 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.…”

Section: Discussionmentioning

confidence: 97%

“…This limitation might affect the airway volume measurements as they have affected our segmentation process. The inconsistent head position of the patients, tongue, and breathing positions also cause errors in volumetric measurement, thus, scannings with controlling these possible limitations are required 47 .…”

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

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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.

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“…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

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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.

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Deep Learning Based Airway Segmentation Using Key Point Prediction

Cited by 14 publications

References 40 publications

Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review

Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review

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

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