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BACKGROUND: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. METHOD: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. RESULTS: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. CONCLUSIONS: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders.

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This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject's voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F0), jitter and shimmer were calculated. The F0 standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.

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Arthroscopy of the temporomandibular joint is a minimally invasive surgical procedure. Nowadays, 3 levels are described depending on the complexity. Level I involves a single puncture with an anterior irrigating needle for outflow. Level II involves a double puncture using triangulation in order to be able to perform minor operative maneuvers. Subsequently, it is possible to progress to Level III and perform more advanced techniques, through multiple punctures, involving the arthroscopic canula and 2 or more working cannulas. However, in cases of advanced degenerative pathology or re-arthroscopy, it is common to observe advanced fibrillation, severe synovitis, adhesions, or articular obliteration which makes conventional triangulation difficult. On these cases, we propose a simple and effective technique that facilitates approach to the intermediate space by means of triangulation with transillumination reference.

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Healthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making, all tasks vital in a critical situation such as that of the COVID-19 pandemic. This paper presents a study about the possibility to support the early and non-invasive detection of COVID-19 through the analysis of voice signals by means of the main machine learning algorithms. If demonstrated, this detection capacity could be embedded in a powerful mobile screening application. To perform this important study, the Coswara dataset is considered. The aim of this investigation is not only to evaluate which machine learning technique best distinguishes a healthy voice from a pathological one, but also to identify which vowel sound is most seriously affected by COVID-19 and is, therefore, most reliable in detecting the pathology. The results show that Random Forest is the technique that classifies most accurately healthy and pathological voices. Moreover, the evaluation of the vowel /e/ allows the detection of the effects of COVID-19 on voice quality with a better accuracy than the other vowels.

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The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.

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BACKGROUND: Atherosclerosis is a progressive process responsible for most heart diseases and ischemic stroke. It constitutes, in fact, the most common cause of stroke in middle-aged people. To avoid or, at least, limit the disabling deficits that may derive from a carotid disease, a prompt and early diagnosis is necessary. The diagnostic technique used to detect a carotid disease is the eco-color Doppler. Unfortunately, this method is not free from errors, due to manufacturer mistakes or its operator dependence. METHODS: In this study, we propose an automated methodology capable of identifying the presence of a carotid disease from the Heart Rate Variability analysis of electrocardiographic signals. A Correlation-based Feature Selector for data reduction and Artificial Neural Networks are used to distinguish between pathological and healthy subjects. RESULTS: A series of tests has been realized to evaluate the proposed approach by using electrocardiographic signals selected from an available database in order to analyse the classification ability in comparison with other algorithms existing in literature. The results obtained show that the proposed approach provides values of accuracy, sensitivity, specificity, precision, F-measure and ROC area, respectively equal to 90.5%, 97.7%, 72.9%, 89.7%, 93.5% and 0.957, better than those achieved by other algorithms. CONCLUSIONS: Considering the achieved accuracy, our methodology is more effective than any of the main algorithm existing in literature. It is important to note that this approach is proposed as a support for the diagnosis of a carotid disorder through a non-invasive approach.

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OBJECTIVES: The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. MATERIALS AND METHODS: A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. RESULTS: Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). CONCLUSIONS: Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a "good screening tool" for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.

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BACKGROUND: Renal transplant (RTX) recipients seem to experience a better quality of life compared to dialysis patients. However, the factors responsible for this positive effect are not completely defined. Conceivably, a change in the physical performance of these patients could play a role. METHODS: To assess this, we measured: (1) waist circumference, fat mass and appendicular fat-free mass (aFFM) by dual-energy X-ray densitometry, (2) physical performance with the Short Physical Performance Battery, and (3) muscle strength with the handgrip test, in 59 male RTX, 11 chronic kidney disease in conservative treatment (CKD) and 10 peritoneal dialysis (PD) patients. RESULTS: Surprisingly, anthropometric characteristics and body composition were similar among the three groups. However, despite a low aFFM, muscle strength was higher in stable RTX recipients > 5 years after transplantation than in dialyzed patients. Instead, CKD (wait-listed for RTX) had similar muscle strength to RTX patients. Waist circumference in RTX recipients showed a redistribution of body fat with increased central adipose tissue allocation compared to PD. At linear regression analysis, age, weight, height, aFFM, hemoglobin and transplant age were independent predictors of handgrip strength, explaining about 37% of the variance. Age and transplant age accounted for 18 and 12% of variance, respectively. CONCLUSIONS: Our study demonstrates, for the first time, that clinically stable RTX recipients have greater muscle strength than dialyzed patients and suggests that the handgrip test could be an effective and easy-to-perform tool to assess changes in physical performance in this large patient population.

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