Treatment of primary epiglottis collapse in OSA in adults with glossoepiglottopexy: a 5-year experience

Objective. To review our 5-year experience with a modified version of glossoepiglottopexy for treatment of obstructive sleep apnoea syndrome (OSA) in two hospitals.Methods. A retrospective analysis was carried out on a cohort of adult patients affected by OSA suffering from primary collapse of the epiglottis who underwent a modified glossoepiglottopexy. All patients underwent drug-induced sleep endoscopy, polysomnographic and swallowing evaluation, and assessment with the Epworth Sleepiness Scale (ESS).Results. Forty-nine patients were retrospectively evaluated. Both the apnoea-hypopnoea index (AHI) (median AHI(post)-AHI(pre) = -22.4 events/h; p < 0.001) and oxygen desaturation index (ODI) showed a significant postoperative decrease (median ODIpost-ODIpre = -18 events/h; p < 0.001), as did hypoxaemia index (median T-90% post - T-90% pre = 5%; p < 0.001). The ESS questionnaire revealed a significant decrease in postoperative scores (median ESSpost-ESSpre =- 9; p < 0.001). None of the patients developed postoperative dysphagia.Conclusions. Our 5-year experience demonstrates that modified glossoepiglottopexy is a safe and reliable surgical technique for treatment of primary epiglottic collapse in OSA patients

Organotopic organization of the porcine mid-cervical vagus nerve

Introduction: Despite detailed characterization of fascicular organization of somatic nerves, the functional anatomy of fascicles evident in human and large mammal cervical vagus nerve is unknown. The vagus nerve is a prime target for intervention in the field of electroceuticals due to its extensive distribution to the heart, larynx, lungs, and abdominal viscera. However, current practice of the approved vagus nerve stimulation (VNS) technique is to stimulate the entire nerve. This produces indiscriminate stimulation of non-targeted effectors and undesired side effects. Selective neuromodulation is now a possibility with a spatially-selective vagal nerve cuff. However, this requires the knowledge of the fascicular organization at the level of cuff placement to inform selectivity of only the desired target organ or function. / Methods and results: We imaged function over milliseconds with fast neural electrical impedance tomography and selective stimulation, and found consistent spatially separated regions within the nerve correlating with the three fascicular groups of interest, suggesting organotopy. This was independently verified with structural imaging by tracing anatomical connections from the end organ with microCT and the development of an anatomical map of the vagus nerve. This confirmed organotopic organization. / Discussion: Here we show, for the first time, localized fascicles in the porcine cervical vagus nerve which map to cardiac, pulmonary and recurrent laryngeal function (N = 4). These findings pave the way for improved outcomes in VNS as unwanted side effects could be reduced by targeted selective stimulation of identified organ-specific fiber-containing fascicles and the extension of this technique clinically beyond the currently approved disorders to treat heart failure, chronic inflammatory disorders, and more

Multi-modal and multi-dimensional biomedical image data analysis using deep learning

There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early diagnosis, and scientific discovery. Recent advances in artificial intelligence and machine learning, particularly in deep learning, have revolutionized computer vision and image analysis for many application areas. While processing of non-biomedical signal, image, and video data using deep learning methods has been very successful, high-stakes biomedical applications present unique challenges such as different image modalities, limited training data, need for explainability and interpretability etc. that need to be addressed. In this dissertation, we developed novel, explainable, and attention-based deep learning frameworks for objective, automated, and quantitative analysis of biomedical signal, image, and video data. The proposed solutions involve multi-scale signal analysis for oraldiadochokinesis studies; ensemble of deep learning cascades using global soft attention mechanisms for segmentation of meningeal vascular networks in confocal microscopy; spatial attention and spatio-temporal data fusion for detection of rare and short-term video events in laryngeal endoscopy videos; and a novel discrete Fourier transform driven class activation map for explainable-AI and weakly-supervised object localization and segmentation for detailed vocal fold motion analysis using laryngeal endoscopy videos. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of biomedical data, that is of great value for potential early diagnosis and effective disease progress or treatment monitoring.Includes bibliographical references

A survey of voice pathology surveillance systems based on internet of things and machine learning algorithms

The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors

Laryngeal preneoplastic lesions and cancer: challenging diagnosis. Qualitative literature review and meta-analysis

Background: The treatment of laryngeal cancer and its precursor lesions has a great impact on important laryngeal basic functions, thus, early detection and preoperative assessment are important for a curative and function-preserving therapy. Furthermore, delayed diagnosis, leads to loco-regional failure and a high incidence of second primary tumor, reasons for poor outcome. In this setting, there are two basic clinical problems in the management of premalignant and malignant laryngeal lesions. First, small and thin lesions are difficult to evaluate by the histopathologic examination and initial biopsies are often not sufficient for a conclusive diagnosis. Second, margins of the specimens from surgical excisions are difficult to evaluate due to tissue damage from the device, leaving us in doubt whether the excision is radical or not. From these observations, it is obvious that an instrument offering the possibility to detect pre-cancerous-early cancerous lesions, and satellite foci or second primaries would be the key to improving the survival rate in head and neck cancer. But, despite the high number of more advanced diagnostic techniques and methods, unfortunately, it is not uncommon for different clinicians to use different nomenclature or to identify different stage for the same laryngeal lesion. Object. Different modalities of diagnostic techniques of laryngeal lesions exist. Rather than difference between benign and obvious malignant diseases, more difficult is to detect the presence of precancerous epithelial alterations. Not all tests achieve the same diagnostic accuracy and that all tests must be considered against a gold standard, hence this meta-analysis of literature aimed to synthesise the validity of each single diagnostic technique in identifying and staging laryngeal diseases. Methods: A systematic review of literature was led searching for articles mentioning the following terms including their various combinations to maximize the yield: larynx, laryngeal cancer, white light (WL) endoscopy, contact endoscopy (CE), stroboscopy, autofluorescence (AF), ultrasound (US), narrow band imaging (NBI), computers assail tomography (CAT), magnetic resonance imaging (MRI), positron emission tomography (PET). A quantitative analysis was carried on for paper published after 2005 onward, reporting a minumun series of 10 patients each study, declaring sensitivity and specificity of each diagnostic system. Results: The search identified 7215 publications, of which 3616 published after 2005, with a final results of a total of 214 articles stratified and included by our selection criteria. 58 out of 214 articles were selected for quantitative synthesis. 35 out of 58 studies had a quality score of ≥ 6 (good), 15 presented a score between 4 and 5 (fair), the remaining 8 had a score between 2 and 3 (poor). While objections can be raised about the pooling of different diagnostic procedures under the same group and the high level of heterogeneity in the meta-analyses, the inclusion of over 2500 patients makes the results fairly robust. Conclusions: A comprehensive overview of the most recent advances in laryngeal imaging technology combined with all of the information needed to interpret findings and successfully manage patients with voice disorders can be found herein. With these data, clinicians can risk-stratify patients and select proper examination modalities in order to provide appropriate care. Moreover, study limitations, together with possible clinical and research implications have been counted, as well

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Computational Investigations of the Fluid-Structure Interaction During Phonation: The Role of Vocal Fold Elasticity and Glottal Flow Unsteadiness

Human voice production arises from the biomechanical interaction between vocal fold vibrations and airflow dynamics. Changes in vocal fold stiffness can lead to changes in vocal fold vibration patterns and further changes in voice outcomes. A good knowledge of the cause-and-effect relationship between vocal fold stiffness and voice production can not only deepen the understanding of voice production mechanisms but also benefit the treatment of voice disorders associated with vocal fold stiffness changes. This constitutes the first objective of this dissertation. The second objective of this dissertation is to further examine the range of validity of the quasi-steady assumption of glottal flow during phonation. The assumption is of vital importance for phonation modeling since it enables to eliminate the unsteady aspects of glottal flow, which greatly simplifies the flow modeling. A three-dimensional flow-structure interaction model of voice production is employed to investigate the effects of vocal fold stiffness parameters on voice production. The vocal fold is modeled as the cover-ligament-body structure with a transversely isotropic constitutive relation. Stiffness parameters in both the transverse plane and the longitudinal direction of each layer of the vocal fold are systematically varied. The results show that varying the stiffness parameters has obvious monotonic effects on the fundamental frequency, glottal flow rate and glottal opening, but has non-monotonic effects on the glottal divergent angle, open quotient and closing velocity. Compared to the transverse stiffness parameters, the longitudinal stiffness parameters generally have more significant impacts on glottal flows and vocal fold vibrations. Additionally, the sensitivity analysis reveals that the stiffness parameters of the ligament layer have the largest effect on most output measures. Next, flow-structure interaction simulations are carried out to study the effect of fiber orientation in the conus elasticus on voice production. Two continuum vocal fold models with different fiber orientations in the conus elasticus are constructed. The more realistic fiber orientation (caudal-cranial) in the conus elasticus is found to yield smaller structural stiffness and larger deflection at the junction of the conus elasticus and ligament than the anterior-posterior fiber orientation, which facilitates vocal fold vibrations and eventually causes a larger peak flow rate and higher speed quotient. The generated voice is also found to have a lower fundamental frequency and smaller spectral slope. Finally, the validity of the quasi-steady assumption for glottal flow is systematically examined by considering the voice frequency range, complexity of glottal shapes and air inertia in the vocal tract. The results show that at the normal speech frequency (~ 100 Hz), the dynamics of the quasi-steady flow greatly resembles that of a dynamic flow, and the glottal flow and glottal pressure predicted by the quasi-steady approximation have very small errors. However, the assumption produces huge errors at high frequencies (~ 500 Hz). In addition, air inertia in the vocal tract can undermine the validity of the assumption via the nonlinear interaction with the unsteady glottal flow. The role of glottal shapes in the validation is found to be insignificant