Detect Respiratory Diseases Faster with AI-Powered Audio Analysis

Diagnosing respiratory conditions from auscultation audio has traditionally relied on expert ears and time-consuming reviews. We built a machine learning-based platform that classifies lung sounds with high precision, helping providers make faster, more consistent decisions with real clinical impact.

Highlights

• CNN model trained on the ICBHI dataset for five-class respiratory classification
• CVAE-powered synthetic spectrograms to balance and expand training data
• Melspectrogram preprocessing and frequency filtering for audio clarity
• 98.45% F1-score validated via 10-fold, patient-grouped cross-validation
• Designed for scalable deployment in telehealth or in-clinic diagnostic tools

From Audio to Actionable Diagnosis

We applied deep learning to lung auscultation recordings, specifically Convolutional Neural Networks (CNNs) for pattern detection and Convolutional Variational Autoencoders (CVAE) for generating synthetic spectrograms that reduce data imbalance. Together, these models elevate the accuracy and generalizability of diagnostic outputs.

To support rare case detection, the system generates synthetic training data, increasing its ability to recognize less frequent respiratory patterns without additional patient input.

A Workflow That Works at the Point of Care

1. Audio is uploaded and resampled to 4 kHz
2. Melspectrograms are generated after filtering for noise
3. Synthetic spectrograms are created using CVAE to augment rare class data
4. The CNN classifies the sound into one of five respiratory condition types
5. Performance is tracked using metrics including F1, sensitivity, and Cohen’s kappa

All patient data is validated using grouped splits to prevent overfitting and ensure real-world reliability.

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Results at a Glance

• 98.45% F1-score across five respiratory disease categories
• Improved classification of rare conditions using CVAE
• Scalable architecture ready for clinical environments
• Seamless integration with external apps via web API services

Built for Real Healthcare Use

This isn’t a research prototype. It’s a full system built for reliability, clarity, and long-term performance. Models are updated through secure, modular deployment flows and integrate with clinical dashboards or external EHR platforms.

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Why It Matters

Detecting respiratory conditions early can prevent complications, reduce hospitalizations, and support better treatment outcomes. By automating sound classification and enhancing underrepresented data, this system makes high-quality diagnostics accessible, without needing a specialist at every turn.