Intraoperative cone beam computed tomography of tracheal stenting for stenosis and fistula diseases: a retrospective cohort study

Li, Jing; Xu, Kaihao; Li, Zhaonan; Li, Yahua; Zhou, Xueliang; Liu, Juanfang; Yao, Yuan; Han, Xinwei; Jiao, Dechao; Han, Xinwei

doi:10.21037/qims-21-858

Quant Imaging Med Surg

2022

DOI: 10.21037/qims-21-858

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Intraoperative cone beam computed tomography of tracheal stenting for stenosis and fistula diseases: a retrospective cohort study

Jing Li¹,

Kaihao Xu²,

Zhaonan Li³

et al.

Abstract: Background: Angiographic computed tomography (CT) is useful in various medical contexts, but little research has been presented regarding the application of cone beam CT (CBCT) in airway stenting. This study set out to evaluate the clinical feasibility of using CBCT in airway stent placement in a single-center retrospective cohort.Methods: A total of 228 patients with stenosis or fistula diseases were treated with metallic airway stents in

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2024

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Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

Vamsi Krishna,

Addepalli

et al. 2024

Int. Res. J. multidiscip. Technovation

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Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.

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Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

Vamsi Krishna,

Addepalli

et al. 2024

Int. Res. J. multidiscip. Technovation

View full text Add to dashboard Cite

show abstract

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Intraoperative cone beam computed tomography of tracheal stenting for stenosis and fistula diseases: a retrospective cohort study

Cited by 1 publication

References 27 publications

Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

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