Synchronized, concurrent optical coherence tomography and videostroboscopy for monitoring vocal fold morphology and kinematics

Maguluri, Gopi; Mehta, Daryush D.; Kobler, James B.; Park, Jesung; Iftimia, Nicusor

doi:10.1364/boe.10.004450

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…An interesting area is tissue evaluation before and after treatment as a supplement to describing movement phenomena 25 . Sharme et al used Optical Coherence Tomography (OCT) during phonation with a framerate of 250 Hz for subsequent analyses 26 , 27 . Further studies could define the region of interest in the glottis for OCT, which has been done for some time in ophthalmology 28 .…”

Section: Discussionmentioning

confidence: 99%

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Pedersen¹,

Larsen

Madsen³

et al. 2023

Sci Rep

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The entire glottis has mostly been the focus in the tracking of the vocal folds, both manually and automatically. From a treatment point of view, the various regions of the glottis are of specific interest. The aim of the study was to test if it was possible to supplement an existing convolutional neural network (CNN) with post-network calculations for the localization and quantification of posterior glottal gaps during phonation, usable for vocal fold function analysis of e.g. laryngopharyngeal reflux findings. 30 subjects/videos with insufficient closure in the rear glottal area and 20 normal subjects/videos were selected from our database, recorded with a commercial high-speed video setup (HSV with 4000 frames per second), and segmented with an open-source CNN for validating voice function. We made post-network calculations to localize and quantify the 10% and 50% distance lines from the rear part of the glottis. The results showed a significant difference using the algorithm at the 10% line distance between the two groups of p < 0.0001 and no difference at 50%. These novel results show that it is possible to use post-network calculations on CNNs for the localization and quantification of posterior glottal gaps.

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Section: Discussionmentioning

confidence: 99%

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Pedersen¹,

Larsen

Madsen³

et al. 2023

Sci Rep

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“…Various deep learning software are discussed [21][22][23]. Since stroboscopy is a major diagnostic clinical tool for functional larynx evaluation, its use in deep learning and optical coherence tomography probably must be elucidated more in the future [24,25].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Laryngoscopy for Quantitative Vocal Fold Analysis in Combination with AI, A Cohort Study of Manual Artefacts

Sousa¹,

Almeida

Alves

2021

SJO

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Introduction:A cohort of high-speed videoendoscopies was evaluated for usability for deep learning. The aim of our study was to find the percentage of our high-speed videos (15.732) that could be used for deep learning (AI). A screening of the material showed that some videos had artefacts, making them non usable for deep learning. Material:A randomization was made with Wolfram Alpha random number generator selecting between 15.732 videos from 7.909 patients. The various non usable videos are described including the rear parts of the vocal folds not seen, the epiglottis or uvula blocking vision, parts of the vocal folds not seen, no vibration of the vocal folds, persistent constricted larynx, picture taken from an oblique angle, the front part of the vocal folds not seen, and parts of the arytenoid region not seen. Method:Assuming the assessments are independent with regards to whether there is a finding, the total number of assessments with a given finding is binomial distributed. With 100 assessments, an observed incidence of 1, 10 and 25 findings will result in estimated 95% confidence intervals of [0%-3%], [4%-16%] and [17%-33%], respectively. 95% confidence intervals are calculated as Wald test using the asymptotic Normal distribution assumption of the estimated proportion in the binomial distribution. Assuming the incidence of findings for each of the different findings was below 25%, the expected length of the 95% confidence interval is 16%-point (33-17), with 200 and 500 assessments, the corresponding length is 14%-point and 8%-point, respectively. Based on these calculations 100 randomised films were sufficient to be used for calculations. Results and Conclusion:The prospective cohort study of high-speed videos covered 12 years from the February 2007 to January 2019 in an otorhinolaryngology medical centre. 7.909 patients with a total of 15.732 high-speed video films of the larynx including the vocal folds had been consecutively sampled (4.000 frames per second, Richard Wolf Ltd. endocam 5562). Observations on high-speed video for the usable versus non usable videos with 95% confidence intervals, showed that only 51% were usable. The interesting result is that oblique angle pictures (10%) as well as insufficient pictures of the front of the vocal folds and arytenoids (14%) were the largest groups of the non-usable. They can be augmented by the examiner in the future. Various video and deep learning programs are discussed.

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“…Direct subjective interpretation of OCT images by human observers requires extensive training. [ 38 ] This becomes even more challenging with modern OCT systems that can acquire OCT images at video rate with more than 40 images/s. As mentioned earlier, the interpretation of OCT images is highly based on the experience of the clinician causing an interobserver variability.…”

Section: Analyzing Oct Data By Deep Learning Methodsmentioning

confidence: 99%

Optical coherence tomography for tissue classification of the larynx in an outpatient setting‐a translational challenge on the verge of a resolution?

Wittig

Betz

Eggert

2020

Transl Biophotonics

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The detection and tissue classification of mucosal lesions of the upper aerodigestive tract (UADT) is crucial for the development of a treatment plan. Using modern endoscopic examination techniques mucosal changes can be detected early on in an office‐based setting. However, the further classification of these lesions requires a surgical procedure with a biopsy taken under general anesthesia. Optical coherence tomography (OCT) is a noninvasive, light‐based optical tool which can provide high‐resolution cross‐sectional images of tissue at near microscopic resolution. Applying it through a single mode optical fiber allows endoscopic approaches. Routine endoscopic assessment and a thin epithelial layer makes the larynx the perfect organ for implementing OCT into the clinical endoscopic routine. This review is focused on summarizing previous and ongoing translational applications of OCT imaging for tissue classification in the UADT focusing on the larynx, discussing limitations and benefits and offering an outlook on possible future applications.

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Synchronized, concurrent optical coherence tomography and videostroboscopy for monitoring vocal fold morphology and kinematics

Cited by 6 publications

References 36 publications

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Localization and quantification of glottal gaps on deep learning segmentation of vocal folds

Accuracy of Laryngoscopy for Quantitative Vocal Fold Analysis in Combination with AI, A Cohort Study of Manual Artefacts

Optical coherence tomography for tissue classification of the larynx in an outpatient setting‐a translational challenge on the verge of a resolution?

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