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.

Electric field measurement of femtosecond time-resolved four-wave mixing signals in molecules.

We report an experiment to measure the femtosecond electric field of the signal emitted from an optical third-order nonlinear interaction in carbon dioxide molecules. Using degenerate four-wave mixing with femtosecond near infrared laser pulses in combination with the ultra-weak femtosecond pulse measurement technique of TADPOLE, we measure the nonlinear signal electric field in the time domain at different time delays between the interacting pulses. The chirp extracted from the temporal phase of the emitted nonlinear signal is found to sensitively depend on the electronic and rotational contributions to the nonlinear response. While the rotational contribution results in a nonlinear signal chirp close to the chirp of the input pulses, the electronic contribution results in a significantly higher chirp which changes with time delay. Our work demonstrates that electric field-resolved nonlinear spectroscopy offers detailed information on nonlinear interactions at ultrafast time scales.