Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort.

Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression.

Methods: We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscopes from four auscultation positions in 2,124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography.

Interrater and intrarater agreement on heart murmurs.

Objective: To investigate interrater and intrarater agreement between physicians and medical students on heart sound classification from audio recordings, and factors predicting agreement with a reference classification.

Design: Intra- and interrater agreement study.

Subjects: Seventeen GPs and eight cardiologists from Norway and the Netherlands, eight medical students from Norway.

Inspiratory crackles-early and late-revisited: identifying COPD by crackle characteristics.

Background: The significance of pulmonary crackles, by their timing during inspiration, was described by Nath and Capel in 1974, with early crackles associated with bronchial obstruction and late crackles with restrictive defects. Crackles are also described as 'fine' or 'coarse'. We aimed to evaluate the usefulness of crackle characteristics in the diagnosis of chronic obstructive pulmonary disease (COPD).

Prediction of chronic heart failure and chronic obstructive pulmonary disease in a general population: the Tromsø study.

Aims: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are main causes of dyspnoea, and echocardiography and spirometry are essential investigations for these diagnoses. Our aim was to determine the prevalence of HF and COPD in a general population, also how the diseases may be identified, and to what extent their clinical characteristics differ.

Methods And Results: In the seventh survey of Tromsø study (2015-16), subjects aged 40 years or more were examined with echocardiography, spirometry, lung sound recordings, questionnaires, including the modified Medical Research Council (mMRC) questionnaire on dyspnoea, and N-terminal pro-brain natriuretic peptide analysis.

Convolutional Neural Network for Breathing Phase Detection in Lung Sounds.

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature.

Adventitious and Normal Lung Sounds in the General Population: Comparison of Standardized and Spontaneous Breathing.

Background: For clinical practice and research, it would be easier to auscultate lung sounds without simultaneously measuring air flow. This study evaluated whether the presence of adventitious lung sounds and the characteristics of normal lung sounds differ between spontaneous and standardized breathing in a general population.

Methods: A cross-sectional study was conducted with 116 subjects (53.

International perception of lung sounds: a comparison of classification across some European borders.

Introduction: Lung auscultation is helpful in the diagnosis of lung and heart diseases; however, the diagnostic value of lung sounds may be questioned due to interobserver variation. This situation may also impair clinical research in this area to generate evidence-based knowledge about the role that chest auscultation has in a modern clinical setting. The recording and visual display of lung sounds is a method that is both repeatable and feasible to use in large samples, and the aim of this study was to evaluate interobserver agreement using this method.

Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers.

Symptom checkers are software tools that allow users to submit a set of symptoms and receive advice related to them in the form of a diagnosis list, health information or triage. The heterogeneity of their potential users and the number of different components in their user interfaces can make testing with end-users unaffordable. We designed and executed a two-phase method to test the respiratory diseases module of the symptom checker Erdusyk.

Isolated rheumatoid arthritis-associated cerebral vasculitis: a diagnostic challenge.

Central nervous system involvement in rheumatoid arthritis is uncommon. In order of frequency, published cases have reported rheumatoid nodules, meningeal vasculitis, and cerebral vasculitis (CV). The frequency of vasculitic cerebral involvement in rheumatoid arthritis is unknown.