Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities

Surface topography systems enable the capture of spinal dynamic movement. A visualization of possible unique movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated a visualization approach using Siamese neural networks (SNN) and checked, if the identification of individuals is possible based on dynamic spinal data. The presented visualization approach seems promising in visualizing subjects in the presence of intraindividual variability between different gait cycles as well as day-to-day variability. Overall, the results indicate a possible existence of a personal spinal ‘fingerprint’. The work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users

Direct effects of Facio-Oral Tract Therapy on swallowing frequency of non-tracheotomised patients with acute neurogenic dysphagia

Objectives: The aim of this study was to investigate the direct effect of Facio-Oral Tract Therapy ® on swallowing frequency of non-tracheotomised patients with acute neurogenic dysphagia. Methods: Within a pre-, post-/during and follow-up study design, 19 non-tracheotomised dysphagic patients were included consecutively and treated according to three specific preselected Facio-Oral Tract Therapy stimulation techniques. Results: The primary outcome was the direct effect of the three different Facio-Oral Tract Therapy stimulation techniques on the number of swallows. We found a significant effect of Facio-Oral Tract Therapy on swallowing frequency as compared to baseline with an increase by 65.63% and medium effect size of D = 0.62. No significant difference could be demonstrated when comparing baseline to follow-up. Conclusion: For the first time, this positive therapy effect could be demonstrated on a population of non-tracheotomised patients. Facio-Oral Tract Therapy seems to be an appropriate means for improving effectiveness and safety of swallowing. Since improvement was not long lasting, it appears to be reasonable to apply therapy frequently during the day with the plausible result of minimising the amount of aspirated saliva and thereby reducing the risk of aspiration pneumonia. Further studies may consider choosing a randomised controlled trial design to demonstrate that change in swallow frequency is related to the target intervention only

How does biographic-narrative intervention influence identity negotiation and quality of life in aphasia? - The participants' perspective

Problem Many persons with aphasia experience a loss of Quality of Life (QoL). Although life story work supports processes of sense-making and by this QoL improvement, only a few studies made use of the “talk-based” approach in aphasic patients because of the language deficit (e.g. Shadden, 2005). We developed an adapted interdisciplinary biographic-narrative intervention, which was already shown to be effective in terms of gains in quantitative measures of QoL (Corsten, Konradi, Schimpf, Hardering, & Keilmann, 2013). For a deeper understanding we will now analyze the participants’ perspective obtained in interviews. Procedure and Analysis Five face-to-face in-depth interviews and seven group sessions were conducted over ten weeks in a mixed-method-design with pre- and post-tests and a follow-up assessment three months after the intervention. The multidimensional construct of QoL was measured with a battery of instruments: – the pictorial version of the Aachen Life Quality Inventory (ALQI, Engell, Hütter, Willmes, & Huber, 2003) – the Satisfaction with Life Scale (SWLS, Diener, Emmons, Larsen, & Griffin, 1985) – a German version of the Visual Analogue Mood Scales (VAMS, Stern, 1997) Semi-structured interviews, conducted post-treatment, included questions concerning the participants‘ experiences with the intervention, identity change and future perspectives e.g. Analysis was based on interpretative principles from grounded theory (Corbin & Strauss, 2008). Results For our entire sample of 27 participants with chronic but different types of aphasia we found a significant and stable growth in health-related QoL (ALQI, Wilcoxon signed-ranks test, two-tailed, p < .05). Self-reported states of mood also improved significantly (VAMS, t-test, two-tailed, p < .05). As expected, overall life satisfaction (SWLS) did not change. The interviews revealed three main themes “effectiveness of the intervention”, “QoL” and “self-concept”. The following associations with improvements in QoL were identified: enhanced coping regarding chronic illness, improved self-efficacy and control, and a more differentiated picture of self. The impacts of the different kinds of intervention are discussed. Discussion The quantitative and the qualitative results were complementary in demonstrating the effectiveness of the biographic-narrative intervention. As predicted, there was a specific treatment effect with a significant and stable improvement in QoL. Analysis of the semi-structured interviews indicated that through the approach the participants’ sense of self changed. The findings provide foundations for future work into intervention

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users

Dynamic surface topography data for assessing intra- and interindividual variation of vertebral motion

Spinal function is substantially related to the motion of the particular vertebrae and the spine as a whole. For systematic assessment of individual motion, data sets are required which cover the kinematics comprehensively. Additionally, the data should enable a comparison of inter- and intraindividual variation of vertebral orientation in dedicated motion tasks like gait. For this purpose, this article provides surface topography (ST) data which were acquired while the individual test persons were walking on a treadmill at three different speed levels (2 km/h, 3 km/h, 4 km/h). In each recording, ten full walking cycles were included per test case to enable a detailed analysis of motion patterns. The provided data reflects asymptomatic and pain-free volunteers. Each data set contains the vertebral orientation in all three motion directions for the vertebra prominens down to L4 as well as the pelvis. Additionally, spinal parameters like balance, slope, and lordosis / kyphosis parameters as well as an assignment of the motion data to single gait cycles are included. The complete raw data set without any preprocessing is provided. This allows to apply a broad range of further signal processing and evaluation steps in order to identify characteristic motion patterns as well as intra- and inter-individual variation of vertebral motion

Consistency of vertebral motion and individual characteristics in gait sequences

Vertebral motion reveals complex patterns, which are not yet understood in detail. This applies to vertebral kinematics in general but also to specific motion tasks like gait. For gait analysis, most of existing publications focus on averaging characteristics of recorded motion signals. Instead, this paper aims at analyzing intra- and inter-individual variation specifically and elaborating motion parameters, which are consistent during gait cycles of particular persons. For this purpose, a study design was utilized, which collected motion data from 11 asymptomatic test persons walking at different speed levels (2, 3, and 4 km/h). Acquisition of data was performed using surface topography. The motion signals were preprocessed in order to separate average vertebral orientations (neutral profiles) from basic gait cycles. Subsequently, a k-means clustering technique was applied to figure out, whether a discrimination of test persons was possible based on the preprocessed motion signals. The paper shows that each test sequence could be assigned to the particular test person without additional prior information. In particular, the neutral profiles appeared to be highly consistent intra-individually (across the gait cycles as well as speed levels), but substantially different between test persons. A full discrimination of test persons was achieved using the neutral profiles with respect to flexion/extension data. Based on this, these signals can be considered as individual characteristics for the particular test persons. Keywords: Gait analysis; Human spine; Intra- and interindividual variation; Motion analysis; Rasterstereography; Surface topography; k-means algorithm