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This fully revised and extended second edition provides a comprehensive, most up-to-date overview of the investigation of quantitative measurement in the complex of voice. Important objective parameters of normal voice development are assessed, especially relevant when pathological deviations have to be recognized and defined. The description of different qualities of normal voice development in terms of measurable parameters is provided. The book highlights the hormonal changes that have a considerable influence on the physical development of boys and girls, and how it is possible to predict the voice transition statistically. The extent to which hormones affect voice development in the two genders are made clear in this work through the observation of a number of parameters. In this second edition, the focus is extended to include High-Speed Video images and further discussion. Possible interesting topics for further research are also emphasized. This book will be a valuable resource for laryngologists, phoniatricians, and teachers in their daily work. This is an open access book.

Otorhinolaryngology. --- Pediatrics. --- Voice disorders in children. --- Voice, Change of.

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A boy sings...a beautiful thing' (www.boychoirs.org), but is it? What kinds of boy, singing what kinds of music and to whom? Martin Ashley presents a unique consideration of boys' singing that shows the high voice to be historically, culturally and physiologically more problematic even than is commonly assumed. Through Ashley's extensive conversations with young performers and analysis of their reception by 'peer audiences', the research reveals that the common supposition that 'boys don't want to sound like girls' is far from adequate in explaining the 'missing males' syndrome that can perple

Singing --- Boys --- Voice, Change of. --- Singing and voice culture --- Vocal culture --- Music --- Beatboxing --- Throat singing --- Change of voice --- Puberty --- Social aspects. --- Psychology. --- Instruction and study --- Psychological aspects. --- Performance

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Speech therapy. --- Voice, Change of. --- Transgender people --- Transgender identity --- Gender identity --- Change of voice --- Puberty --- Speech correction --- Speech disorders --- Therapeutics --- Identity. --- Treatment

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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.

Medicine --- machine learning-enabled decision support system --- improving diagnosis accuracy --- Bayesian network --- bariatric surgery --- health-related quality of life --- comorbidity --- voice change --- larynx cancer --- machine learning --- deep learning --- voice pathology classification --- imputation --- electronic health records --- EHR --- laboratory measures --- medical informatics --- inflammatory bowel disease --- C. difficile infection --- osteoarthritis --- complex diseases --- healthcare --- artificial intelligence --- interpretable machine learning --- explainable machine learning --- septic shock --- clinical decision support system --- electronic health record --- cerebrovascular disorders --- stroke --- SARS-CoV-2 --- COVID-19 --- cluster analysis --- risk factors --- ischemic stroke --- outcome prediction --- recurrent stroke --- cardiac ultrasound --- echocardiography --- portable ultrasound --- aneurysm surgery --- temporary artery occlusion --- clipping time --- artificial neural network --- digital imaging --- monocytes --- promonocytes and monoblasts --- chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia --- concordance between hematopathologists --- mechanical ventilation --- respiratory failure --- ADHD --- social media --- Twitter --- pharmacotherapy --- stimulants --- alpha-2-adrenergic agonists --- non-stimulants --- trust --- passive adherence --- human factors

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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.

Medicine --- machine learning-enabled decision support system --- improving diagnosis accuracy --- Bayesian network --- bariatric surgery --- health-related quality of life --- comorbidity --- voice change --- larynx cancer --- machine learning --- deep learning --- voice pathology classification --- imputation --- electronic health records --- EHR --- laboratory measures --- medical informatics --- inflammatory bowel disease --- C. difficile infection --- osteoarthritis --- complex diseases --- healthcare --- artificial intelligence --- interpretable machine learning --- explainable machine learning --- septic shock --- clinical decision support system --- electronic health record --- cerebrovascular disorders --- stroke --- SARS-CoV-2 --- COVID-19 --- cluster analysis --- risk factors --- ischemic stroke --- outcome prediction --- recurrent stroke --- cardiac ultrasound --- echocardiography --- portable ultrasound --- aneurysm surgery --- temporary artery occlusion --- clipping time --- artificial neural network --- digital imaging --- monocytes --- promonocytes and monoblasts --- chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia --- concordance between hematopathologists --- mechanical ventilation --- respiratory failure --- ADHD --- social media --- Twitter --- pharmacotherapy --- stimulants --- alpha-2-adrenergic agonists --- non-stimulants --- trust --- passive adherence --- human factors --- machine learning-enabled decision support system --- improving diagnosis accuracy --- Bayesian network --- bariatric surgery --- health-related quality of life --- comorbidity --- voice change --- larynx cancer --- machine learning --- deep learning --- voice pathology classification --- imputation --- electronic health records --- EHR --- laboratory measures --- medical informatics --- inflammatory bowel disease --- C. difficile infection --- osteoarthritis --- complex diseases --- healthcare --- artificial intelligence --- interpretable machine learning --- explainable machine learning --- septic shock --- clinical decision support system --- electronic health record --- cerebrovascular disorders --- stroke --- SARS-CoV-2 --- COVID-19 --- cluster analysis --- risk factors --- ischemic stroke --- outcome prediction --- recurrent stroke --- cardiac ultrasound --- echocardiography --- portable ultrasound --- aneurysm surgery --- temporary artery occlusion --- clipping time --- artificial neural network --- digital imaging --- monocytes --- promonocytes and monoblasts --- chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia --- concordance between hematopathologists --- mechanical ventilation --- respiratory failure --- ADHD --- social media --- Twitter --- pharmacotherapy --- stimulants --- alpha-2-adrenergic agonists --- non-stimulants --- trust --- passive adherence --- human factors