Early Detection of Tuberculosis in Rural India

Tuberculosis (TB) remains a significant public health challenge in India, with the country accounting for about 26% of the global TB burden. Rural areas are particularly affected due to limited access to healthcare facilities and diagnostic tools. Early detection is crucial for effective treatment and preventing the spread of the disease. However, the shortage of trained radiologists and the high cost of X-ray equipment in rural areas often lead to delayed diagnoses.

Key factors influencing TB detection and management in rural India include:

1. Limited access to healthcare facilities
2. Shortage of trained medical professionals, especially radiologists
3. High cost of diagnostic equipment
4. Stigma associated with TB, leading to delayed care-seeking
5. Variable quality of X-ray imaging in resource-constrained settings

Current TB screening methods in rural India often rely on symptom-based assessments and sputum tests, which can miss early-stage cases.

The core problem this project addresses is the delayed detection of tuberculosis in rural India, leading to poor treatment outcomes and continued disease transmission. The key issues are:

1. Diagnostic Gaps: Lack of access to quick, accurate TB screening in rural areas.
2. Resource Constraints: Shortage of trained radiologists to interpret chest X-rays.
3. Inconsistent Imaging Quality: Variable X-ray image quality due to equipment limitations and operator expertise.
4. Scalability Challenges: Difficulty in scaling up traditional screening methods to cover vast rural populations.
5. Integration with Existing Systems: Need to work within the constraints of current healthcare infrastructure.

• Develop a deep learning model for TB detection with sensitivity and specificity of at least 90% on a diverse set of chest X-rays.
• Create a mobile application that can run the TB detection model offline on smartphones or tablets.
• Deploy the system in at least 50 rural health centers across three states in India.
• Reduce the average time to TB diagnosis by 50% in the deployed areas within one year of implementation.

1. Data Collection and Preprocessing:

• Gather a diverse dataset of chest X-rays, including TB-positive and TB-negative cases from rural India.
• Implement data augmentation techniques to address class imbalance and improve model generalization.
• Develop protocols for data anonymization and ethical data handling.

2. Model Development:

• Implement and compare various deep learning architectures (e.g., CNNs, Vision Transformers) for X-ray classification.
• Utilize transfer learning techniques to leverage pre-trained models and address limited data scenarios.
• Implement techniques to handle low-quality and poorly positioned X-ray images.

3. Model Optimization and Mobile Deployment:

• Optimize the model for mobile deployment using techniques like model pruning and quantization.
• Develop methods for on-device inference without requiring internet connectivity.
• Implement efficient data storage and management for offline use.

4. User Interface Development:

• Design an intuitive mobile interface suitable for use by minimally trained healthcare workers.
• Implement features for X-ray capture guidance to improve image quality.
• Develop clear visualizations of AI predictions and confidence levels