Enhancing Medical Diagnosis with AI: A Focus on Respiratory Disease Detection

BACKGROUND: Artificial intelligence (AI) is revolutionizing medical diagnosis and healthcare, providing constant support to medical practitioners. Intelligent systems alleviate workload pressure while optimizing practitioner performance. AI and deep learning have also improved medical imaging and audio analysis. MATERIAL AND METHODS: This research focuses on predicting respiratory diseases using audio recordings from an electronic stethoscope. A convolutional neural network (CNN) was trained on a Respiratory Sound Database, augmented to generate 1,428 audio files. Techniques such as pitch shifting, time stretching, noise addition, time and frequency masking, dynamic range compression, and resampling were employed to increase the diversity and size of the training data. RESULT: Features were extracted from mono audio files, creating a four layer CNN with 90% accuracy. The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhanci ng their performance. The study highlights AI’s potential in respiratory disease detection through audio analysis. CONCLUSION: The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhancing their performance.