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. Phase I consisted of an online test with a large sample of users (n=53). In Phase I, users evaluated the system remotely and completed a questionnaire based on the Technology Acceptance Model. Principal Component Analysis was used to correlate each section of the interface with the questionnaire responses, thus identifying which areas of the user interface presented significant contributions to the technology acceptance. In the second phase, the think-aloud procedure was executed with a small number of samples (n=15), focusing on the areas with significant contributions to analyze the reasons for such contributions. Our method was used effectively to optimize the testing of symptom checker user interfaces. The method allowed kept the cost of testing at reasonable levels by restricting the use of the think-aloud procedure while still assuring a high amount of coverage. The main barriers detected in Erdusyk were related to problems understanding time repetition patterns, the selection of levels in scales to record intensities, navigation, the quantification of some symptom attributes, and the characteristics of the symptoms.

IntroductionLung 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.MethodsWith a microphone in a stethoscope tube, we collected digital recordings of lung sounds from six sites on the chest surface in 20 subjects aged 40 years or older with and without lung and heart diseases. A total of 120 recordings and their spectrograms were independently classified by 28 observers from seven different countries. We employed absolute agreement and kappa coefficients to explore interobserver agreement in classifying crackles and wheezes within and between subgroups of four observers.ResultsWhen evaluating agreement on crackles (inspiratory or expiratory) in each subgroup, observers agreed on between 65% and 87% of the cases. Conger’s kappa ranged from 0.20 to 0.58 and four out of seven groups reached a kappa of ≥0.49. In the classification of wheezes, we observed a probability of agreement between 69% and 99.6% and kappa values from 0.09 to 0.97. Four out of seven groups reached a kappa ≥0.62.ConclusionsThe kappa values we observed in our study ranged widely but, when addressing its limitations, we find the method of recording and presenting lung sounds with spectrograms sufficient for both clinic and research. Standardisation of terminology across countries would improve international communication on lung auscultation findings.

Background Wheezes and crackles are well-known signs of lung diseases, but can also be heard in apparently healthy adults. However, their prevalence in a general population has been sparsely described. The objective of this study was to determine the prevalence of wheezes and crackles in a large general adult population and explore associations with self-reported disease, smoking status and lung function. Methods We recorded lung sounds in 4033 individuals 40 years or older and collected information on self-reported disease. Pulse oximetry and spirometry were carried out. We estimated age-standardized prevalence of wheezes and crackles and associations between wheezes and crackles and variables of interest were analyzed with univariable and multivariable logistic regressions. Results Twenty-eight percent of individuals had wheezes or crackles. The age-standardized prevalence of wheezes was 18.6% in women and 15.3% in men, and of crackles, 10.8 and 9.4%, respectively. Wheezes were mostly found during expiration and crackles during inspiration. Significant predictors of expiratory wheezes in multivariable analyses were age (10 years increase - OR 1.18, 95%CI 1.09–1.30), female gender (1.45, 1.2–1.8), self-reported asthma (1.36, 1.00–1.83), and current smoking (1.70, 1.28–2.23). The most important predictors of inspiratory crackles were age (1.76, 1.57–1.99), current smoking, (1.94, 1.40–2.69), mMRC ≥2 (1.79, 1.18–2.65), SpO2 (0.88, 0.81–0.96), and FEV1 Z-score (0.86, 0.77–0.95). Conclusions Nearly over a quarter of adults present adventitious lung sounds on auscultation. Age was the most important predictor of adventitious sounds, particularly crackles. The adventitious sounds were also associated with self-reported disease, current smoking and measures of lung function. The presence of findings in two or more auscultation sites was associated with a higher risk of decreased lung function than solitary findings.

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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. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73–0.88) than expiration (0.63–0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings.