|
--- |
|
license: cc-by-sa-4.0 |
|
dataset_info: |
|
features: |
|
- name: image |
|
dtype: 'jpg' |
|
- name: filename |
|
dtype: 'null' |
|
splits: |
|
- name: train |
|
download_size: 0 |
|
dataset_size: 0 |
|
task_categories: |
|
- image-classification |
|
language: |
|
- en |
|
tags: |
|
- medical |
|
- images |
|
- TB |
|
- tuberculosis |
|
- tb detection |
|
- models |
|
size_categories: |
|
- 10K<n<100K |
|
--- |
|
|
|
Project Overview |
|
Tuberculosis (TB) remains a major public health challenge, especially in rural India, which accounts for 26% of the global TB burden. Limited healthcare access, a shortage of medical professionals, and high diagnostic costs exacerbate the issue. This project aims to address the delayed detection of TB in rural India using AI-based chest X-ray analysis, enabling early detection and treatment. |
|
|
|
Key Problems |
|
1. Diagnostic Gaps: Lack of access to quick, accurate TB screening in rural areas. |
|
2. Resource Constraints: Shortage of trained radiologists. |
|
3. Inconsistent Imaging Quality: Variable X-ray quality from different machines. |
|
4. Scalability Challenges: Difficulty scaling traditional screening methods. |
|
5. Integration Issues: Working within existing healthcare infrastructure. |
|
|
|
Solution Approach |
|
Develop an AI-based system for early TB detection using chest X-rays, optimized for mobile devices and designed for use by minimally trained healthcare workers in rural areas. |
|
|
|
Key Components: |
|
1. Deep Learning Model: For detecting TB with high sensitivity and specificity. |
|
2. Mobile Application: Optimized for use offline on smartphones/tablets. |
|
3. Scalability: System deployment in rural health centers. |
|
4. Training Program: For rural healthcare workers to use the system effectively. |
|
|
|
Project Goal |
|
1. Model Development: Create a deep learning model for TB detection with 90% sensitivity and specificity. |
|
2. Mobile App: Build an offline-capable mobile app for use in rural areas. |
|
3. Deployment: Implement in 50 rural health centers across 3 states in India. |
|
4. Time Reduction: Decrease TB diagnosis time by 50% in targeted areas. |
|
|
|
Expected Outcomes |
|
1. Validated AI Model for TB detection optimized for mobile deployment. |
|
2. Training Program for healthcare workers on the AI system. |
|
3. Database of anonymized chest X-rays for ongoing research. |
|
4. Published Research on model development and real-world performance. |
|
|
|
Learners' Contributions |
|
Data Collection & Preprocessing |
|
Gather diverse datasets from rural India, implement data augmentation, and ensure data anonymization. |
|
|
|
Model Development |
|
Explore deep learning architectures (e.g., CNNs, Vision Transformers) and employ transfer learning techniques. |
|
Model Optimization & Mobile Deployment |
|
Optimize for mobile use with model pruning and quantization techniques for offline deployment. |
|
User Interface Development |
|
Design an intuitive interface for healthcare workers with minimal technical training. |
|
Validation & Testing |
|
Conduct rigorous testing and user acceptance trials with rural healthcare workers. |
|
|
|
Impact Assessment |
|
1. Health Impact: |
|
40-50% increase in early-stage TB detection. |
|
30-35% improvement in treatment success rates. |
|
|
|
2. Healthcare System Impact: |
|
50-60% reduction in time to diagnosis. |
|
70-80% increase in rural healthcare workers' capability to conduct TB screenings. |
|
|
|
3. Technological Impact: |
|
- Increased AI adoption in rural healthcare and better digital health record management. |
|
|
|
4. Social Impact: |
|
- Increased health-seeking behavior and TB awareness in target communities. |
|
|
|
5. Beneficiaries: |
|
- TB patients, families of TB patients, the Indian healthcare system. |
|
|
|
Conclusion |
|
This project seeks to bridge the diagnostic gaps in TB detection in rural India by leveraging AI and mobile technology, empowering healthcare workers and improving TB detection and treatment outcomes. |
