A multichannel acoustic approach to define a pulmonary pathology as combined pulmonary fibrosis and emphysema syndrome

Santiago-Fuentes, Laura M.; Charleston-Villalobos, S.; González-Camarena, R.; Mejía-Ávila, Mayra Edith; Mateos-Toledo, Heidegger; Buendía-Roldán, Ivette

doi:10.1109/embc.2017.8037428

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…And experiments showed that the improved genetic BP neural network was prior to the traditional BP neural network. The linear parameterized method of multi-channel lung sound information was employed to recognize the lung sound signals in [20]. The neural network was used during the classification process.…”

Section: Introductionmentioning

confidence: 99%

Lung Sound Recognition Algorithm Based on VGGish-BiGRU

Shi

Chaozong

et al. 2019

IEEE Access

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Pulmonary breathing sound plays a key role in the prevention and diagnosis of the lung diseases. Its correlation with pathology and physiology has become an important research topic in the pulmonary acoustics and the clinical medicine. However, it is difficult to fully describe lung sound information with the traditional features because lung sounds are complex and nonstationary signals. And the traditional convolutional neural network cannot also extract the temporal features of the lung sounds. To solve the problem, a lung sound recognition algorithm based on VGGish-BiGRU is proposed on the basis of transfer learning, which combines VGGish network with the bidirectional gated recurrent unit neural network (BiGRU). In the proposed algorithm, VGGish network is pretrained using audio set, and the parameters are transferred to VGGish network layer of the target network. The temporal features of the lung sounds are extracted through retraining BiGRU network with the lung sound data. During retraining BiGRU network, the parameters in VGGish layers are frozen, and the parameters of BiGRU network are fine-tuned. The experimental results show that the proposed algorithm effectively improves the recognition accuracy of the lung sounds in contrast with the state-of-the-art algorithms, especially the recognition accuracy of asthma.INDEX TERMS BiGRU, lung sound recognition, Mel spectrogram, transfer learning, VGGish.

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Section: Introductionmentioning

confidence: 99%

Lung Sound Recognition Algorithm Based on VGGish-BiGRU

Shi

Chaozong

et al. 2019

IEEE Access

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Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals

Hassan,

Singhal

2023

IETE Journal of Research

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Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case–control study

Siebert,

Hartley,

Courvoisier

et al. 2023

BMC Pulm Med

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Background Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. Methods A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. Discussion This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. ClinicalTrials.gov Identifier: NCT05318599.

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A multichannel acoustic approach to define a pulmonary pathology as combined pulmonary fibrosis and emphysema syndrome

Cited by 3 publications

References 10 publications

Lung Sound Recognition Algorithm Based on VGGish-BiGRU

Lung Sound Recognition Algorithm Based on VGGish-BiGRU

Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case–control study

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