BackgroundPompe disease is a progressive metabolic myopathy. Disease progression is characterized, among other features, by progressive dysfunction of the voice apparatus. The aim of this study was to employ electroglottographic, acoustic and nasalance measurement methods on patients with late-onset Pompe disease in order to provide detailed information on the effect of the disease on voice quality. Voice quality is the key factor for estimating the effectiveness of ERT in late-onset Pompe disease. The study compared clinical phoniatric examination with electroglottographic, acoustic and nasalance measurement methods. The consistency of the aforementioned analyses was assessed.MethodsThe study examined 19 patients with late-onset Pompe disease (including 9 with the juvenile form of the disease). Of these, a total of 17 patients underwent otolaryngological examination with detailed phoniatric evaluation of their articulatory organs. Electroglottographic recordings and nasalance measurements (using the nasalance Separator Handle) were obtained from all patients. MATLAB (COVAREP toolkit) was used to analyse voice recording data.ResultsDysphonia observed in patients with late-onset Pompe disease is mainly caused by dysfunction of vocal fold closure and weakness of vocal muscle. However, substantial speech nasality is caused by insufficient closure of the soft palate. Electroglottographic signal analysis, acoustic and nasalance testing methods indicated that more significant changes in the function of the voice apparatus presented in the juvenile form than in the adult form of late-onset Pompe disease.ConclusionsIt was found that speech nasality and electroglottographic tests are more repeatable, comparable and versatile than phoniatric examination, allowing for earlier detection of voice pathology in late-onset Pompe disease. These sensitive and non-invasive acoustic and electroglottographic methods allow for the tracking of changes in voice as patients undergo treatment or as the disease progresses.

Morquio A syndrome, or mucopolysaccharidosis (MPS IV A), is an inherited lysosomal storage disorder which belongs to the group of mucopolysaccharidoses (MPSs). It is caused by N-acetylgalactosamine-6-sulfatase (GALNS) activity deficiency, which results in impaired degradation of glycosaminoglycans (GAGs), including keratan sulfate (KS) and chondroitin-6-sulfate (CS). These compounds infiltrate and disrupt the architecture of the extracellular matrix, compromising the integrity of the connective tissue. Patients with Morquio A have also been noted for exhibiting abnormalities of the larynx and vocal tract. The aim of the study was to assess voice alterations using noninvasive acoustic and electroglottographic voice analysis. Electroglottographic signal and acoustic analyses revealed considerable changes in the voices of patients with Morquio A syndrome when compared to the voices of healthy controls. Affected patients tended toward tense voice, incomplete glottal closure, increased incidence of vocal fold nodules, dysphonia, and hoarse voice. Morquio A syndrome is characterized by connective tissue disease, which adversely affects voice quality. The use of objective voice analysis makes it possible to quantitatively monitor changes in the vocal apparatus over the course of disease progression, and also allows for assessment of the effects of the enzyme replacement therapy.

BackgroundLate-onset Pompe disease (LOPD) is a metabolic myopathy disorder characterized by progressive muscle damage and among others dysfunction of the voice apparatus, which affects speech and – above all – voice quality. Symptoms include dysphonia, instability, glottic insufficiency, and tense voice. The aim of this study was to evaluate and compare voice quality disorder in a group of 15 LOPD patients who were first examined in 2014 and then re-examined in 2017.MethodsIn both 2014 and 2017, the same 15 LOPD patients, ranging in age from 15 to 57, from 10 different families, underwent the following examinations: perceptual assessment of voice quality on the RBH scale, electroglottographic recordings, and acoustic recordings. All the patients were on enzyme replacement therapy (ERT).ResultsThree years after the 2014 study, the LOPD patients demonstrated a deterioration in voice quality. A statistically significant increase in glottic insufficiency (p = 0.0399) and a shift towards tense voice (p = 0.0417) were observed. Two patients – out of three who had received presymptomatic treatment – demonstrated stable voice quality compared with 2014.ConclusionsThe results suggest increased muscle weakness and progression of LOPD. The parameters Closed Quotient (calculated on the basis of an electroglottographic signal) and Peak Slope (calculated on the basis of an acoustic signal) proved to be the most sensitive.

Approximately 4% of the world’s population suffers from rare diseases. A vast majority of these disorders have a genetic background. The number of genes that have been linked to human diseases is constantly growing, but there are still genetic syndromes that remain to be discovered. The diagnostic yield of genetic testing is continuously developing, and the need for testing is becoming more significant. Due to limited resources, including trained clinical geneticists, patients referred to clinical genetics units must be accurately selected. Around 30–40% of genetic disorders are associated with specific facial characteristics called dysmorphic features. As part of our research, we analyzed the performance of classifiers based on deep learning face recognition models in detecting dysmorphic features. We tested two classification problems: a multiclass problem (15 genetic disorders vs. controls) and a two-class problem (disease vs. controls). In the multiclass task, the best result reached an accuracy level of 84%. The best accuracy result in the two-class problem reached 96%. More importantly, the binary classifier detected disease features in patients with diseases that were not previously present in the training dataset. The classifier was able to generalize differences between patients and controls, and to detect abnormalities without information about the specific disorder. This indicates that a screening tool based on deep learning and facial recognition could not only detect known diseases, but also detect patients with diseases that were not previously known. In the future, this tool could help in screening patients before they are referred to the genetic unit.

No abstract