Lung sounds in pneumonia

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Key Lung Sounds in Pneumonia: Crackles, Rhonchi, and Adventitious Sounds

Lung sounds in pneumonia are notably different from those in healthy individuals. The most common abnormal sounds detected in pneumonia patients are inspiratory crackles, expiratory crackles, and rhonchi. Studies have shown that inspiratory crackles are present in about 81% of pneumonia patients compared to only 28% of healthy controls, while expiratory crackles are found in 65% of pneumonia cases versus 9% in controls. Rhonchi are also more frequent in pneumonia patients (19%) and absent in healthy individuals. These adventitious sounds are key indicators for diagnosing pneumonia using lung auscultation .

Automated and AI-Based Lung Sound Analysis for Pneumonia Detection

Recent advances in technology have enabled the use of automated systems and artificial intelligence (AI) to analyze lung sounds for pneumonia diagnosis. Computerized lung sound analyzers can objectively quantify and characterize pneumonia-related sounds, providing an "acoustic pneumonia score" that helps distinguish pneumonia patients from healthy controls with high sensitivity and specificity . Machine learning models, such as support vector machines (SVM) and gradient boosting algorithms, have demonstrated high accuracy (up to 97-99%) in classifying lung sounds and identifying pneumonia, especially when using features like Mel Frequency Cepstral Coefficients (MFCC) and energy parameters 347.

Deep learning approaches, including CNN-LSTM models, have also been used to analyze spectrograms of lung sounds, achieving promising results in distinguishing pneumonia from healthy cases . Additionally, AI models that incorporate both cardiopulmonary and lung sounds, after filtering out environmental noise, have shown improved diagnostic performance .

Digital Auscultation and Machine Learning in Pediatric Pneumonia

Digital auscultation, combined with automated machine learning algorithms, is being explored to improve pneumonia diagnosis in children, particularly in low-resource settings. These systems can classify lung sounds as normal, crackles, wheeze, or both, with moderate sensitivity and specificity compared to expert pediatricians . However, systematic reviews highlight that while digital auscultation shows potential, there is still limited robust evidence for its accuracy in pediatric pneumonia, and more well-designed studies are needed 106.

Special Considerations: COVID-19 and Interstitial Pneumonia

Automated lung sound analysis has also been applied to detect interstitial pneumonia, such as that seen in COVID-19 patients. Algorithms can identify characteristic "velcro-like" lung sounds associated with interstitial changes, achieving diagnostic accuracy rates around 75% when compared to imaging techniques .

Conclusion

Lung sounds in pneumonia are characterized by a high prevalence of adventitious sounds, especially crackles and rhonchi. Automated and AI-based analysis of lung sounds offers objective, accurate, and non-invasive tools for pneumonia diagnosis in both adults and children. While these technologies show high promise, especially in resource-limited settings, further research and standardization are needed to fully establish their clinical utility, particularly in pediatric populations.

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Most relevant research papers on this topic

Automated lung sound analysis in patients with pneumonia.

Lung sound analysis reveals significant differences between pneumonia patients and asymptomatic controls, supporting the diagnosis and aiding in teaching physical diagnosis.

Observational StudyHighly Cited

2004·

118citations

·R. Murphy et al.

CNN-LSTM-Based Lung Sound Analysis for Pneumonia Detection

The CNN-LSTM model accurately detects pneumonia from lung sound analysis, with an accuracy of 72.2, demonstrating its potential for automatic detection.

2025·

0citations

·Pandit Vivek Kumar Pandey et al.

Comparative Performance Analysis of Kernel Functions in Support Vector Machines in the Diagnosis of Pneumonia using Lung Sounds

The Radial Basis Function Kernel in Support Vector Machines provided the best accuracy for diagnosing pneumonia from lung sounds, with the energy parameter being a significant discriminatory feature.

2022·

6citations

·John Amose et al.

AI Based Diagnosis of Pneumonia

The gradient boosting algorithm accurately diagnoses pneumonia from lung sounds with 97 percent accuracy, outperforming other AI and deep learning techniques.

2022·

14citations

·B. Vidhya et al.

Evaluation of machine learning to detect adventitious lung sounds using digital auscultation to aid childhood pneumonia diagnosis

An automated machine learning algorithm with a digital stethoscope showed moderate sensitivity and specificity in classifying lung sounds as either abnormal or normal in children with pneumonia.

2022·

0citations

·S. Ahmed et al.

Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study

Computerised lung sound analysis (CLSA) may improve the specificity of paediatric pneumonia diagnosis in resource-poor settings by distinguishing pathologic lung sounds from normal sounds.

Observational StudyRigorous Journal

2012·

43citations

·L. E. Ellington et al.

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

This novel framework accurately diagnoses COPD and pneumonia using lung sound analysis, with an accuracy of 99.70% on selected fused features.

2020·

52citations

·Syed Zohaib Hassan Naqvi et al.

AI-assisted Viral Pneumonia Diagnosis Based on Cardiopulmonary Sounds

Cardiopulmonary sounds are more effective than lung sounds in aiding pneumonia diagnosis, with filtering out external environmental noise being crucial for accurate results.

Non-RCT

2024·

0citations

·Yuqing Gao et al.

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds

VECTOR, an algorithm for detecting COVID-19 pneumonia from velcro-like lung sounds, has a 75% diagnostic accuracy, offering a new approach for monitoring pulmonary implications in pauci-symptomatic patients.

2022·

14citations

·F. Pancaldi et al.

Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review

Digital auscultation with automated lung sound analysis shows limited evidence for diagnosing pneumonia in children.

Systematic ReviewRigorous Journal

2022·

1citation

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