Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Symptom Experience and Treatment Delay during Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Dissertation

Chronic obstructive pulmonary disease (COPD) is a major health problem in the United States. Acute exacerbations of COPD are primarily responsible for the physical, psychological and economic burden of this disease. Early identification and treatment of exacerbations is important to improve patient and healthcare outcomes. Little is known about how patients with COPD recognize an impending exacerbation and subsequently decide to seek treatment. The purpose of this qualitative descriptive study was to explore and describe symptom recognition and treatment delay in individuals experiencing an acute exacerbation of chronic obstructive pulmonary disease (COPD). Leventhal’s Common Sense Model of illness representation undergirded this study. Using semi-structured interviews, adults hospitalized with an acute exacerbation of COPD were asked to describe their symptom experience and self care behaviors, including treatment seeking, in the days to weeks prior to hospitalization. Data analysis revealed one main theme: Recognizing, responding and reacting to change, and six subthemes: Something’s coming, Here we go again, Seeking urgent treatment, Riding it out, Not in charge anymore and My last day that richly described the COPD exacerbation experience. The study revealed that patients experience an illness prodrome prior to exacerbation and have a recurrent exacerbation symptom pattern that was self-recognized. Treatment seeking was most influenced by the speed and acuity of exacerbation onset, severity of breathlessness, fears of death, nature of patient-provider relationship and the perception of stigmatization during prior healthcare encounters. These findings are important for the development of interventions to improve patient recognition and management of COPD exacerbations in the future

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Emergent measures and patterns of recovery during acute exacerbations of Chronic Obstructive Pulmonary Disease.

As exacerbações agudas da doença pulmonar obstrutiva crónica (EADPOC) são eventos frequentes e onerosos. Contudo, o conhecimento acerca da sua avaliação e curso de evolução é limitado. Este trabalho de investigação teve como objetivo compreender a avaliação e os padrões de recuperação das EADPOC geridas em contexto de ambulatório. Especificamente, pretendeu-se: i) aprofundar oconhecimento acerca das medidas de avaliação mais utilizadas na avaliação dedoentes com EADPOC e ii) explorar os padrões de recuperação durante as EADPOC utilizando diferentes medidas de avaliação. Foram realizados seis estudos. A Revisão Sistemática e os Estudos empíricos I e II responderam ao primeiro objetivo específico deste trabalho de investigação, sintetizando e explorando a fiabilidade, validade, capacidade de resposta e interpretabilidade de medidas de avaliação comummente utilizadas e de fácil acesso para a avaliação de doentes com EADPOC em contexto de ambulatório. Os resultados revelaram que apesar de existirem poucas medidas de avaliação com as suas propriedades métricas adequadamente estudadas, os seus valores de interpretabilidade parecem semelhantes aos estabelecidos em fases estáveis da DPOC. O segundo objetivo específico deste trabalho de investigação foi alcançado através de três Estudos empíricos (Estudos III, IV e V) que demonstraram que a recuperação de uma EADPOC é influenciada pelas características dos doentes no momento inicial da exacerbação. Estes Estudos mostraram ainda que as medidas reportadas pelos doentes e as medidas clínicas diferem nos seus padrões e tempos de recuperação durante as EADPOC. Os resultados deste trabalho de investigação constituem nova evidência acerca das medidas de avaliação e dos momentos mais adequados para avaliar, monitorizar e interpretar alterações no curso de EADPOC. É necessário realizar mais investigação com metodologias padrão, amostras maiores e desenhos de estudo longitudinais com avaliações pré e pós exacerbação de forma a consolidar estes resultados preliminares e aumentar o conhecimento acerca do curso de evolução das EADPOC geridas em contexto de ambulatório.Palavras-chave: DPOC, EXACERBAÇÕES, MEDIDAS DE RESULTADOS, PROPRIEDADES DE MEDIDA, RECUPERAÇÃO, EVOLUÇÃO.Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are frequent and burdensome events. However, knowledge about their assessment and course of evolution is limited. This research work focused on understanding the assessment and recovery pattern of AECOPD managed on an outpatient setting. Specifically, it aimed to: i) gain more insight on the outcome measures most used to assess patients with AECOPD and their measurement properties and ii) explore patterns of recovery of different outcomes and outcome measures during these events. Six studies were conducted. The Systematic Review and empirical Studies I and II addressed the first specific aim of this research work by synthetising and exploring the reliability, validity, responsiveness and interpretability of outcome measures commonly used and easily available to assess outpatients with AECOPD. Findings showed that although few outcome measures exist which measurement properties have been properly studied in patients with AECOPD, their interpretability values seem to be similar to those in stable patients. The second specific aim of this research work was addressed with three empirical Studies (Studies III, IV and V) which showed that the recovery from AECOPD is influenced by patients' characteristics assessed at the onset of the exacerbation. These Studies further evidenced different patterns and timings of recovery among patient-reported and clinical outcome measures. The findings of this research work constitute new evidence on the most adequate outcome measures and timings to assess, monitor and interpret changes during the course of AECOPD managed on an outpatient setting. Further research with standardised methodologies, larger samples and longitudinal pre-/post exacerbation designs is warranted to consolidate these preliminary findings and increase the scope of knowledge on the time course of AECOPD treated on an outpatient basis

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis

Automatic text filtering using limited supervision learning for epidemic intelligence

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