AI Based E-Pulmonologist

Objectives

The primary objective of the "AI-Based E-Pulmonologist" project is to develop an advanced digital tool that leverages artificial intelligence and machine learning to revolutionize the early and accurate diagnosis of pneumonia, COVID-19, asthma, and tuberculosis, thereby addressing significant public health challenges and improving patient outcomes.

Socio-Economic Benefit

Improved Public Health: By enabling early and accurate diagnosis of respiratory diseases such as pneumonia, COVID-19, asthma, and tuberculosis, the project can significantly reduce the prevalence and severity of these conditions, leading to a healthier population. Increased Accessibility: The AI-based tool can provide diagnostic support in regions with limited access to healthcare professionals, particularly pulmonologists, thus bridging the healthcare gap in underserved areas. Enhanced Patient Outcomes: Timely diagnosis and treatment of respiratory diseases can lead to better patient outcomes, reducing morbidity and mortality rates associated with these conditions. Cost Savings: Early and accurate diagnosis can lead to more efficient use of healthcare resources, reducing the need for expensive treatments and hospitalizations, ultimately lowering healthcare costs for both patients and healthcare systems. Education and Awareness: The project can raise awareness about respiratory diseases and the importance of early diagnosis, encouraging more people to seek medical advice sooner and adhere to treatment plans. Data-Driven Insights: The AI system can generate valuable data and insights about respiratory disease patterns, contributing to public health research and policy-making. Workforce Support: By assisting healthcare professionals with diagnosis, the AI-based tool can alleviate some of the workload on medical staff, allowing them to focus on more complex cases and patient care. Global Health Impact: Given the global nature of respiratory diseases, the project has the potential to benefit populations worldwide, contributing to global health initiatives and the fight against pandemics like COVID-19.

Methodologies

Data Collection and Preprocessing Download X-Ray Images: Acquire a large dataset of chest X-ray images from public medical databases (e.g., NIH, Kaggle). Preprocess Data: Clean and normalize the images, ensuring consistent size and format. Perform data augmentation to enhance the dataset. Model Training Model Selection: Choose an appropriate machine learning model using TensorFlow. Training the Model: Split the dataset into training and validation sets. Train the model on the X-ray images, adjusting hyperparameters to optimize performance. Evaluation: Validate the model using metrics such as accuracy, precision, recall, and F1 score. Fine-tune the model based on validation results. Backend Development API Development with FastAPI and Flask: Use FastAPI to create a RESTful API for handling requests from the frontend, processing X-ray images through the trained model, and returning results. Integrate FastAPI with Flask for additional backend functionalities, if needed. Model Integration: Ensure the API can send images to the TensorFlow model for diagnosis and return the results to the frontend. Frontend Development User Interface: Create a web-based user interface using HTML, CSS, and JavaScript. Design the interface to allow users to upload X-ray images and view diagnostic results. Integration: Connect the frontend to the FastAPI backend, ensuring smooth communication and data exchange. Testing and Deployment Testing: Perform unit and integration testing to ensure all components work together seamlessly. Validate the entire system using additional test data. Deployment: Deploy the application on a web server, ensuring it is accessible to users. Monitor performance and address any issues that arise.

Outcome

AI-Based Diagnostic Tool: A functional software application capable of diagnosing pneumonia, COVID-19, asthma, and tuberculosis from chest X-ray images. Improved Diagnosis Accuracy: Enhanced precision and speed in identifying respiratory diseases, leading to better patient outcomes. Web Interface: A user-friendly web-based interface for medical professionals to upload images and receive diagnostic results. Integration with Healthcare Systems: Seamless integration with existing electronic health record (EHR) systems and other healthcare data sources. Comprehensive Documentation: Detailed reports on the development process, user manuals, and technical documentation. Healthcare Impact: Potential for reduced morbidity and mortality rates through early detection and intervention, especially in resource-limited settings.

Project Team Members

PROJECT GALLERY

AI Based E-Pulmonologist

23 May, 2024 02:09 pm