Computer-based videofluorographic analysis of posterior pharyngeal wall movement during swallowing in patients with head-and-neck cancer

Gotoh, Masakazu; Watanabe, Satoshi; Oh-Shige, Hideo; Izumi, Mitsuru; Naitoh, Munetaka; Ariji, Yoshiko; Oh-Iwa, Ichiro; Shimozato, Kazuo; Ariji, Eiichiro

doi:10.1007/s11282-009-0022-0

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Furthermore, image quality depends on each clinician’s ability to control the radiation dose administered to patients 1 . It is also difficult to standardize patient position among studies 4 . Although our institution’s protocol involves sitting in a chair and swallowing to help maintain a consistent position, there were some static movements in patients who were unable to swallow well.…”

Section: Discussionmentioning

confidence: 99%

“…The quantitative evaluation of swallowing function using VFG has been studied 2 . Studies thus far have involved the quantification of pharyngeal residue 3 , laryngeal elevation and hyoid bone displacement during swallowing 3 , movement of the posterior pharyngeal wall during swallowing 4 , pharyngeal transit time 3 , and pharyngeal swallowing reaction time 5 . Clinicians must analyze swallowing videos in a frame-by-frame manner 2 , which requires considerable time and effort.…”

Section: Introductionmentioning

confidence: 99%

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A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing

Ariji

Gotoh

Fukuda

et al. 2022

Sci Rep

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Although videofluorography (VFG) is an effective tool for evaluating swallowing functions, its accurate evaluation requires considerable time and effort. This study aimed to create a deep learning model for automated bolus segmentation on VFG images of patients with healthy swallowing and dysphagia using the artificial intelligence deep learning segmentation method, and to assess the performance of the method. VFG images of 72 swallowing of 12 patients were continuously converted into 15 static images per second. In total, 3910 images were arbitrarily assigned to the training, validation, test 1, and test 2 datasets. In the training and validation datasets, images of colored bolus areas were prepared, along with original images. Using a U-Net neural network, a trained model was created after 500 epochs of training. The test datasets were applied to the trained model, and the performances of automatic segmentation (Jaccard index, Sørensen–Dice coefficient, and sensitivity) were calculated. All performance values for the segmentation of the test 1 and 2 datasets were high, exceeding 0.9. Using an artificial intelligence deep learning segmentation method, we automatically segmented the bolus areas on VFG images; our method exhibited high performance. This model also allowed assessment of aspiration and laryngeal invasion.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing

Ariji

Gotoh

Fukuda

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, image quality depends on each clinician's ability to control the radiation dose administered to patients 1 . It is also di cult to standardize patient position among studies 4 . Although our institution's protocol involves sitting in a chair and swallowing to help maintain a consistent position, there were some static movements in patients who were unable to swallow well.…”

Section: Discussionmentioning

confidence: 99%

“…The quantitative evaluation of swallowing function using VFG has been studied 2 . Studies thus far have involved the quanti cation of pharyngeal residue 3 , laryngeal elevation and hyoid bone displacement during swallowing 3 , movement of the posterior pharyngeal wall during swallowing 4 , pharyngeal transit time 3 , and pharyngeal swallowing reaction time 5 . Clinicians must analyze swallowing videos in a frameby-frame manner 2 , which requires considerable time and effort.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of contrast-enhanced bolus in videofluorography images of swallowing using deep learning: a preliminary study

Ariji

Gotoh

et al. 2022

Preprint

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Objective: This study aimed to create a deep learning model for automated bolus segmentation on videofluorography (VFG) images of patients with healthy swallowing and dysphagia using the artificial intelligence deep learning segmentation method, and to assess the performance of the method.Study Design: VFG images of 72 swallowing of 12 patients were continuously converted into 15 static images per second. In total, 3910 images were arbitrarily assigned to the training, validation, test 1, and test 2 datasets. In the training and validation datasets, images of colored bolus areas were prepared, along with original images. Using a U-Net neural network, a trained model was created after 500 epochs of training. The test datasets were applied to the trained model, and the performances of automatic segmentation (Jaccard index, Sørensen–Dice coefficient, and sensitivity) were calculated.Results: All performance values for the segmentation of the test 1 and 2 datasets were high, exceeding 0.9.Conclusion: Using an artificial intelligence deep learning segmentation method, we automatically segmented the bolus areas on VFG images; our method exhibited high performance. This model also allowed assessment of aspiration and laryngeal invasion.

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Alterations of posterior pharyngeal wall movement during swallowing in postoperative tongue cancer patients: assessment with a videofluoroscopic swallowing study

et al. 2022

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Computer-based videofluorographic analysis of posterior pharyngeal wall movement during swallowing in patients with head-and-neck cancer

Cited by 4 publications

References 26 publications

A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing

A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing

Automatic segmentation of contrast-enhanced bolus in videofluorography images of swallowing using deep learning: a preliminary study

Alterations of posterior pharyngeal wall movement during swallowing in postoperative tongue cancer patients: assessment with a videofluoroscopic swallowing study

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