Digital Stethoscope
Our project is a digital stethoscope app that leverages the power of Artificial Intelligence (AI) and deep learning to analyze heartbeat sounds. The app records heartbeats using a smartphone, filters out noise, analyzes the sounds for abnormalities, and provides insights into potential cardiovascular and respiratory diseases. Users can share recordings and results with doctors or chat directly with healthcare professionals within the app.
Inspiration:
Heart disease remains a leading cause of death worldwide, with early detection being crucial for effective treatment. Inspired by the need to improve healthcare accessibility and diagnostic accuracy, especially in low-cost and underserved areas, we developed this app to democratize healthcare and provide an affordable, portable, and reliable diagnostic tool.
What Does It Do?
It serves as a virtual guardian for your heart, offering a range of features tailored to promote proactive cardiovascular care: Heartbeat Recording: Utilize your smartphone as a digital stethoscope to record heartbeat sounds conveniently. Noise Removal: Advanced algorithms ensure accurate analysis by eliminating extraneous noise from recordings. AI Analysis: Cutting-edge machine learning algorithms analyze heartbeat patterns to detect abnormalities with 90% accuracy. If the heartbeat is abnormal, users are advised to seek medical assistance promptly. Save Recordings: Store heartbeat recordings for future reference and analysis. Share Feature: Share heartbeat recordings along with predicted results with medical professionals outside the app for further consultation and analysis. Direct Consultation: chat with doctors directly through the app for further consultation.
How We Built It:
The app was developed using Flutter for a seamless, cross-platform user experience. The backend is powered by Firebase for data storage and authentication. Our heartbeat analysis model, trained on an extensive dataset, uses cutting-edge machine learning techniques and has achieved state-of-the-art results with a 90% accuracy rate. Key technical challenges included integrating the AI model with Flutter and developing an efficient noise removal algorithm to ensure precise heart sound analysis.
Challenges We Ran Into:
Performing noise cancellation in Flutter was a significant struggle due to limited support or resources. We conducted extensive research and experimented with various techniques to overcome this challenge. With perseverance and dedication, we finally developed a noise removal algorithm that effectively filters out additional noise, thereby improving the accuracy of our predictions and reducing the risk of misleading results. Integrating the trained ML model with the Flutter app for real-time inference was another tricky task, as most preprocessing steps are not directly supported in Flutter. Despite these challenges, we persevered and successfully integrated the model for accurate predictions.
What’s Next:
Our future plans include: Inclusion of Lung Sound Analysis: We aim to advance the app by incorporating lung sound analysis for respiratory disease detection. UI/UX Enhancement: We'll improve the app's interface by leveraging animations for a more engaging user experience. Training Lung Anomaly Detection Model: We'll acquire lung sound data and train our model to detect anomalies, expanding the app's diagnostic capabilities. Built With:
- Flutter: For building the cross-platform mobile application.
- Firebase: For backend services, including authentication and data storage.
- TensorFlow: For developing and deploying the deep learning model.
- Digital Signal Processing (DSP): For effective noise removal from audio recordings.
Our AI-powered digital stethoscope app is a significant advancement in leveraging AI for accessible healthcare. By providing critical diagnostic capabilities, it aims to improve health outcomes and early detection of heart disease, particularly in regions with limited healthcare facilities.
Log in or sign up for Devpost to join the conversation.