Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?

The videofluoroscopic swallowing study (VFSS) is a gold-standard imaging technique for assessing swallowing, but analysis and rating of VFSS recordings is time consuming and requires specialized training and expertise. Researchers have recently demonstrated that it is possible to automatically detect the pharyngeal phase of swallowing and to localize the bolus in VFSS recordings via computer vision, fostering the development of novel techniques for automatic VFSS analysis. However, training of algorithms to perform these tasks requires large amounts of annotated data that are seldom available. We demonstrate that the challenges of pharyngeal phase detection and bolus localization can be solved together using a single approach. We propose a deep-learning framework that jointly tackles pharyngeal phase detection and bolus localization in a weakly-supervised manner, requiring only the initial and final frames of the pharyngeal phase as ground truth annotations for the training. Our approach stems from the observation that bolus presence in the pharynx is the most prominent visual feature upon which to infer whether individual VFSS frames belong to the pharyngeal phase. We conducted extensive experiments with multiple convolutional neural networks (CNNs) on a dataset of 1245 bolus-level clips from 59 healthy subjects. We demonstrated that the pharyngeal phase can be detected with an F1-score higher than 0.9. Moreover, by processing the class activation maps of the CNNs, we were able to localize the bolus with promising results, obtaining correlations with ground truth trajectories higher than 0.9, without any manual annotations of bolus location used for training purposes. Once validated on a larger sample of participants with swallowing disorders, our framework will pave the way for the development of intelligent tools for VFSS analysis to support clinicians in swallowing assessment

RAJKIRAN NATARAJAN

Many research questions in dysphagia research require frame-by-frame annotation of anatomical landmarks visible in videofluorographs as part of the research workflow, which can be a tedious and error prone process. Such annotation is done manually using image analysis tools, is error prone, and characterized by poor rater reliability. In this thesis, a computer-assisted workflow that uses a point tracking technique based on the Kanade-Lucas-Tomasi tracker to semi-automate the annotation process, is developed and evaluated. Techniques to semi-automate the annotation process have been explored but none have had their research value demonstrated. To demonstrate the research value of a workflow based on point tracking in enhancing the annotation process, the developed workflow was used to perform an enhanced version of the recently published Coordinate Mapping swallowing study annotation technique to determine several swallowing parameters. Evaluation was done on eight swallow studies obtained from a variety of clinical sources and showed that the workflow produced annotation results with clinically insignificant spatial errors. The workflow has the potential to significantly enhance research processes that require frame-by-frame annotation of anatomical landmarks in videofluorographs as part of their data preparation steps, by reducing the total time required to annotate clinical case

Characterizing Spoken Discourse in Individuals with Parkinson Disease Without Dementia

Background: The effects of disease (PD) on cognition, word retrieval, syntax, and speech/voice processes may interact to manifest uniquely in spoken language tasks. A handful of studies have explored spoken discourse production in PD and, while not ubiquitously, have reported a number of impairments including: reduced words per minute, reduced grammatical complexity, reduced informativeness, and increased verbal disruption. Methodological differences have impeded cross-study comparisons. As such, the profile of spoken language impairments in PD remains ambiguous. Method: A cross-genre, multi-level discourse analysis, prospective, cross-sectional between groups study design was conducted with 19 PD participants (Mage = 70.74, MUPDRS-III = 30.26) and 19 healthy controls (Mage = 68.16) without dementia. The extensive protocol included a battery of cognitive, language, and speech measures in addition to four discourse tasks. Two tasks each from two discourse genres (picture sequence description; story retelling) were collected. Discourse samples were analysed using both microlinguistic and macrostructural measures. Discourse variables were collapsed statistically to a primal set of variables used to distinguish the spoken discourse of PD vs. controls. Results: Participants with PD differed significantly from controls along a continuum of productivity, grammar, informativeness, and verbal disruption domains including total words F(1,36) = 3.87, p = .06; words/minute F(1,36) = 7.74, p = .01 , % grammatical utterances F(1,36) = 11.92, p = .001, total CIUs F(1,36) = 13.30, p = .001, % CIUs (Correct Information Units) F(1,36) = 9.35, p = .004, CIUs/minute F(1,36) = 14.06, p = .001, and verbal disruptions/100 words F(1,36) = 3.87, p = .06 (α = .10). Discriminant function analyses showed that optimally weighted discourse variables discriminated the spoken discourse of PD vs. controls with 81.6% sensitivity and 86.8% specificity. For both discourse genres, discourse performance showed robust, positive, correlations with global cognition. In PD (picture sequence description), more impaired discourse performance correlated significantly with more severe motor impairment, more advanced disease staging, and higher doses of PD medications. Conclusions: The spoken discourse in PD without dementia differs significantly and predictably from controls. Results have both research and clinical implications