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AI in the Fight Against Tuberculosis (TB)
Context:
The fight against tuberculosis (TB), a treatable yet underdiagnosed disease, could see significant improvements through the use of artificial intelligence (AI), especially in countries like India, which accounts for about 25% of the global TB burden.
Current TB Burden in India:
- India reported 2.17 million TB cases between January and October this year, highlighting the need for improved diagnosis.
- Access to healthcare services remains a challenge, especially in rural areas, contributing to the underdiagnosis of TB.
AI’s Role in TB Diagnosis:
- Salcit Technologies has developed an AI product called Swaasa, which analyzes cough sounds to assess lung health and detect TB.
- Collaboration with Google: Salcit Technologies is exploring the use of Google’s Health Acoustic Representations (HeAR) bioacoustic foundation model to improve TB screening across India.
- HeAR is designed to help build AI models that can listen to human sounds and detect early signs of diseases, potentially extending TB screening.
- Collaboration with Google: Salcit Technologies is exploring the use of Google’s Health Acoustic Representations (HeAR) bioacoustic foundation model to improve TB screening across India.
- Swaasa can work on various devices like smartphones, tablets, and laptops, providing cost-effective and accessible tests for larger populations, especially in remote areas.
- Swaasa has already been deployed in some districts by the Karnataka Health Promotion Trust (KHPT) to enhance the quality of presumptive TB case screenings.
- The platform has conducted over 300,000 lung health assessments and is being explored for broader deployment, including in North East India.
AI’s Impact on Healthcare:
- AI is seen as a transformative technology in preventing, diagnosing, and treating diseases. Salcit Technologies and partners believe that AI can radically change the healthcare landscape by improving diagnostic and screening capabilities.
- Google’s AI models are also being used in mobile screenings for TB, lung cancer, and breast cancer, assisting radiologists with image recognition.
- Apollo Radiology International (ARI) uses AI for mobile screenings, significantly improving diagnostic accessibility.
Challenges in TB Diagnosis:
- Chest X-rays are commonly used for TB screening, but there is a shortage of trained radiologists to interpret images, especially in rural or underserved areas.
- Limited availability of diagnostic tests in these regions further complicates TB diagnosis.
Google’s Impact in Other Health Areas:
- Google has also licensed its diabetic retinopathy AI model to partners like Forus Health and AuroLab in India.
- The model supports AI-assisted screenings for diabetic retinopathy, aiming for six million screenings in India and Thailand over the next decade, with no cost to patients.
