Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

Woo, Jonghye; Xing, Fangxu; Prince, Jerry L.; Stone, Maureen; Green, Jordan R.; Goldsmith, Tessa; Reese, Timothy G.; Wedeen, Van J.; Fakhri, Georges El

doi:10.1121/1.5103191

Cited by 7 publications

(4 citation statements)

References 25 publications

(29 reference statements)

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“…The motion estimated from the cine images could be treated as random uncertainties to generate personalized margins using the existing In addition to RT planning applications, quantification of swallowing motion on cine MR imaging (serially through treatment and survivorship) might provide a surrogate toxicity endpoint of dysphagia or a method to risk stratify for dysphagia early (e.g., during RT) when subclinical change is likely happening prior to clinically detectable pharyngeal dysfunction. Deep learning techniques, for instance, have shown ability to discriminate healthy volunteers from postsurgical (partial glossectomy) HNC survivors based on MR imaging tongue motion parameters during speaking tasks [6]. Our tool facilitates testing of similar discriminant capacity of motion from functional swallowing units or swallowing muscle ROI over the cancer treatment trajectory of individual survivors.…”

Section: Discussionmentioning

confidence: 99%

“…Magnetic resonance (MR) imaging is an effective tool to study aerodigestive tract motion and swallowing [6][7][8][9]. In the HNC radiotherapy population, 2D MR cine sequences of swallowing can evaluate motion in multiple regions-of-interest (ROI) with implications on both radiation therapy (RT) target margins in treatment planning and possibly the functionality of swallowing muscles longitudinally during and after radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

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Automatic registration of 2D MR cine images for swallowing motion estimation

et al. 2020

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To automate the estimation of swallowing motion from 2D MR cine images using deformable registration for future applications of personalized margin reduction in head and neck radiotherapy and outcome assessment of radiation-associated dysphagia. MethodsTwenty-one patients with serial 2D FSPGR-MR cine scans of the head and neck conducted through the course of definitive radiotherapy for oropharyngeal cancer. Included patients had at least one cine scan before, during, or after radiotherapy, with a total of 52 cine scans. Contours of 7 swallowing related regions-of-interest (ROIs), including pharyngeal constrictor, epiglottis, base of tongue, geniohyoid, hyoid, soft palate, and larynx, were manually delineated from consecutive frames of the cine scan covering at least one swallowing cycle. We applied a modified thin-plate-spline robust-point-matching algorithm to register the point sets of each ROI automatically over frames. The deformation vector fields from the registration were then used to estimate the motion during swallowing for each ROI. Registration errors were estimated by comparing the deformed contours with the manual contours. ResultsOn average 22 frames of each cine scan were contoured. The registration for one cine scan (7 ROIs over 22 frames) on average took roughly 22 minutes. A number of 8018 registrations were successfully batch processed without human interaction after the contours were drawn. The average registration error for all ROIs and all patients was 0.36 mm (range: 0.06 mm-2.06 mm). Larynx had the average largest motion in superior direction of all structures

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic registration of 2D MR cine images for swallowing motion estimation

et al. 2020

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“…Wang et al [58] used a support vector machine from electromagnetic articulographs to assess a set of flesh points on the tongue and lips optimal to classify speech movements. Woo et al [59] developed a DL approach using tagged-MRI to discriminate tongue motion patterns during speech in posttreatment tongue cancer patients. These tools may aid in the development of diagnostic and rehabilitation strategies for speech restoration.…”

Section: Machine Learning Applications To Assess Voice and Swallowing...mentioning

confidence: 99%

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

Srinivasan,

Liu,

Rameau

2023

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer. Recent findings Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer. Summary Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

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“…The digitized parameters of tongue images have been accepted by various researchers and are gradually being applied in clinical studies; however, the relationship between the tongue image parameters and diseases such as NAFLD, diabetes, and post-cancer diseases has not yet been elucidated. The results of relevant studies are shown in Table 1 ( Zhang et al., 2005 ; Li et al., 2010 ; Jiang et al., 2011 ; Qi et al., 2016 ; Xue et al., 2018 ; Woo et al., 2019 ; Li et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Oral-Gut Microbiome Analysis in Patients With Metabolic-Associated Fatty Liver Disease Having Different Tongue Image Feature

Zhu

Zhao

et al. 2022

Front. Cell. Infect. Microbiol.

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ObjectiveThe objective of this study was to identify the biological correlation between the tongue coating color and oral and gut micro-characteristics in metabolic-associated fatty liver disease (MAFLD) patients.MethodThe characteristics of the tongue coating were examined using an automatic tongue diagnosis system. Tongue coating and stool samples were collected from 38 MAFLD patients, and 16S rDNA full-length assembly sequencing technology (16S-FAST) was used for bioinformatic analysis.ResultsTwenty-two and 16 subjects were included in two distinct clusters according to the white/yellow color of the tongue coating, which was assessed by the L*a*b* values of the image. Upon analyzing the microorganisms in the tongue coating, 66 and 62 pathognomonic bacterial genera were found in the White and Yellow Coating Groups, respectively. The abundance of Stomatobaculumis positively correlated with the a* values of the tongue coating in the White Coating Group, while Fusobacterium, Leptotrichia, and Tannerella abundance was significantly correlated with the b* values in the Yellow Coating Group. Function prediction mainly showed the involvement of protein families related to BRITE hierarchies and metabolism. The MHR (MONO%/high-density lipoprotein cholesterol) of the Yellow Coating Group was higher than that of the White Coating Group.ConclusionIn MAFLD patients, lower a* values and higher b* values are indicators of a yellow tongue coating. There were also significant differences in the flora of different tongue coatings, with corresponding changes in the intestinal flora, indicating a correlation between carbohydrate metabolism disorders and inflammation in the oral microbiome.

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Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

Cited by 7 publications

References 25 publications

Automatic registration of 2D MR cine images for swallowing motion estimation

Automatic registration of 2D MR cine images for swallowing motion estimation

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

Oral-Gut Microbiome Analysis in Patients With Metabolic-Associated Fatty Liver Disease Having Different Tongue Image Feature

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