Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

Alqudah, Ali Mohammad; Qazan, Shoroq; Obeidat, Yusra

doi:10.1007/s00500-022-07499-6

Cited by 23 publications

(9 citation statements)

References 59 publications

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“…Additionally, the use of machine learning or artificial intelligence (AI) incorporated into digital stethoscope applications improves the experience, quality of diagnosis, and care for both patients and healthcare professionals [34]. Alqudah et al compared different deep learning models for the detection of respiratory pathologies from unprocessed lung auscultation sounds [35]. Their performance suggested that the proposed sets of deep learning methods were successful and achieved high performance in classifying the unprocessed lung sounds.…”

Section: Digital Stethoscopementioning

confidence: 99%

“…The crackle detection positive percentage agreement showed promising results with the optimized AI detection thresholds, allowing the AI to detect crackles and wheezes with a reasonably high degree of accuracy based on breath sounds obtained from different digital stethoscope devices. The analysis of different deep learning models suggested that all the proposed deep learning methods were successful and achieved high performance in classifying the unprocessed lung sounds [35,38]. Similarly, there is research on the use of embedded stethoscopes designed to serve as a platform for the computer-aided diagnosis of cardiac sounds for the detection of cardiac murmurs [67], with other research advancing to a portable device with the capability to diagnose cardiac pathology in real time, employing the signal conversion of analogue acoustic signals into a digital signal that can simultaneously be displayed on a computer using a MATLAB graphic user interface for visual representation, thereby enabling a critical analysis of the interpreted data [68].…”

Section: Ai and Audio Data Comparison Analysismentioning

confidence: 99%

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Review on the Advancements of Stethoscope Types in Chest Auscultation

et al. 2023

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Stethoscopes were originally designed for the auscultation of a patient’s chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient’s illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

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Section: Digital Stethoscopementioning

confidence: 99%

Section: Ai and Audio Data Comparison Analysismentioning

confidence: 99%

Review on the Advancements of Stethoscope Types in Chest Auscultation

et al. 2023

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“… 33 In preprocessing the databases, especially in large-scale data sets, the ML algorithms are applied in analyzing the data set in this step using the feature extraction process, in addition to the pattern that can be extracted from the database. 32 , 34 , 35 Deep learning technique is one of the ML subfield named artificial neural network (ANN) and also the best technology that uses the deep neural configuration simulated from the human brain. Hence, they have structures specified by numerous hidden layer performing the extraction of the features and abstraction process at different levels.…”

Section: Machine Learning Techniquementioning

confidence: 99%

Developing a prediction model for successful aging among the elderly using machine learning algorithms

2023

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Objective The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

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“…However, the previous challenge was mitigated in 2019 with the emergence of the largest public database, the International Conference on Biomedical and Health Informatics (ICBHI) [ 60 , 61 ]. Therefore, research focused on different machine learning approaches has recently increased dramatically, such as Recurrent Neural Networks (RNN) [ 62 ], hybrid neural networks [ 63 , 64 , 65 , 66 , 67 ] and above all Convolutional Neural Networks (CNN) [ 64 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 ]. Thus, the use of these types of deep learning architectures provided promising performance improvements due to their ability that they are able to learn behaviour, both in time and frequency, from large datasets, eliminating the engineer intervention in feature extraction techniques, which reduces the likelihood of human error [ 100 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

Mang,

González Martínez,

Martinez Muñoz

et al. 2024

Sensors

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Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system’s condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer (ViT) architecture, since this input–classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self-attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state-of-the-art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant-Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field.

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Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

Cited by 23 publications

References 59 publications

Review on the Advancements of Stethoscope Types in Chest Auscultation

Review on the Advancements of Stethoscope Types in Chest Auscultation

Developing a prediction model for successful aging among the elderly using machine learning algorithms

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

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