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Abstract Background Although crackles on chest auscultation represent a fundamental component of the diagnostic suspect for fibrotic interstitial lung disease (ILD), their reliability has not been properly studied. We assessed the agreement among respiratory physicians on the presence and changes over time of audible crackles collected in a prospective longitudinal cohort of patients with fibrotic ILD. Methods Lung sounds were digitally recorded at baseline and after 12 months at eight anatomical sites. Nine respiratory physicians blindly assessed randomized couples of recordings obtained from the same anatomical site at different timepoints. The physicians indicated the presence of crackles in individual recordings and which recording from each couple eventually had more intense crackles. Fleiss’ kappa coefficient was used to measure inter- and intra-rater agreement. Results Fifty-two patients, mostly with a diagnosis of IPF (n = 40, 76.9%) were prospectively enrolled between October 2019 and May 2021. The final acoustic dataset included 702 single recordings, corresponding to 351 couples of recordings from baseline and 12-months timepoints. Kappa coefficient was 0.57 (95% CI 0.55–0.58) for the presence of crackles and 0.42 (95% CI 0.41–0.43) for acoustic change. Intra-rater agreement, measured for three respiratory physicians on three repeated assessments, ranged from good to excellent for the presence of crackles (κ = 0.87, κ = 0.86, κ = 0.79), and from moderate to good for acoustic change (κ = 0.75, κ = 0.76, κ = 0.57). Conclusions Agreement between respiratory physicians for the presence of crackles and acoustic change was acceptable, suggesting that crackles represent a reliable acoustic finding in patients with fibrotic ILD. Their role as a lung-derived indicator of disease progression merits further studies.

Abstract Background Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. Objectives First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. Animals Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA−], and 5 in exacerbation [sEA+]). Methods Prospective multicenter case‐control study. Horses were categorized based on the horse owner‐assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One‐hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. Results Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P

Auscultation is a standard method of physical examination used by physicians and is widely accepted by doctors and patients for its simplicity, repeatability and non-invasiveness. Artificial intelligence is the 'new integrated frontier' of the thoracic examination, yet there are still concordance discrepancies in obstructive pulmonary diseases; on the contrary, for fibrotic diseases, the degree of concordance increases significantly, as shown by previous clinical studies conducted mainly in children. However, there are data in the literature that appear to be very discordant on certain types of lung noises, such as wet crackles and dry noises; therefore, the application of these devices in daily use in outpatient and hospital settings needs to be further expanded. The integrated data allowed us to make the right diagnosis, also avoiding costs for the national health system and possible invasive procedures such as bronchoscopy, which today remains the “gold standard” for the histological diagnosis of sarcoidosis with lung localisation. Integrated technology could improve the diagnostic capacity in restrictive lung diseases, as shown in this clinical case. Several randomised controlled trials are still needed to increase the significance of this initial integrated research work performed

Abstract 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.

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.

Due to the subjectivity involved currently in pulmonary auscultation process and its diagnostic to evaluate the condition of respiratory airways, this work pretends to evaluate the performance of clustering algorithms such as k-means and DBSCAN to perform a computational analysis of lung sounds aiming to visualize a representation of such sounds that highlights the presence of crackles and the energy associated with them. In order to achieve that goal, Wavelet analysis techniques were used in contrast to traditional frequency analysis given the similarity between the typical waveform for a crackle and the wavelet sym4. Once the lung sound signal with isolated crackles is obtained, the clustering process groups crackles in regions of high density and provides visualization that might be useful for the diagnostic made by an expert. Evaluation suggests that k-means groups crackle more effective than DBSCAN in terms of generated clusters.

Auscultation of the lung is an important part of the respiratory examination and is helpful in diagnosing various respiratory disorders. Auscultation assesses airflow through the trachea-bronchial tree. It is important to distinguish normal respiratory sounds from abnormal ones for example crackles, wheezes, and pleural rub in order to make correct diagnosis. It is necessary to understand the underlying pathophysiology of various lung sounds generation for better understanding of disease processes. Bedside teaching should be strengthened in order to avoid erosion in this age old procedure in the era of technological explosion.

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.

Abstract A major difficulty for novice students when learning how to use a stethoscope for the interpretation of breath sounds is auscultatory identification of the phases of the breathing cycle. This is particularly true when listening to recorded material rather than a live patient. To overcome this obstacle a combined graphic-acoustic presentation has been developed. This module contains a web-based presentation including sounds from the lungs, as heard with a stethoscope, from patients with various lung diseases and normal subjects. A four-step procedure for listening to and analyzing lung sounds is described in detail. In addition to the customary narrative explanations, chest X-ray film images, graphic displays of the acoustic signal, and animation are used. Physiological explanations are included with the self-test questions. The resource concludes with a set of nine lung sounds that students can use to assess their competence and ability to recognize abnormal lung sounds. Dynamic movement of a thorax, a moving cursor that identifies at what point in the breathing cycle the sound originates, and a graphic indicator of sound level are displayed along with the breath sounds.