How AI maps are helping India find hidden TB cases before it's too late, Pg19

Key Highlights:

• Vulnerability Mapping for Tuberculosis (VMTB), an AI-generated map, is being used in India to identify areas with a high likelihood of finding TB cases.
• VMTB uses nearly 30 open-source geospatial datasets to predict vulnerability at the village and ward levels.
• Haryana identified 2,111 high-risk areas using VMTB, allowing for focused screening during the 100-day campaign.
• Trials in Haryana and Assam showed a 251% and 53% relative increase, respectively, in the yield of TB cases.
• Over two lakh TB cases were diagnosed using this method, accounting for approximately 10% of the 25 lakh cases detected last year.

Detailed Insights:

• VMTB utilizes socio-demographic and geospatial data, including nutrition profiles and night lights, along with historical TB case data, to predict vulnerability down to a 500x500 metre tile.
• The AI helps health workers concentrate resources in high-risk areas, ensuring early diagnosis and reducing the number of people missing from the treatment net.
• In Haryana, the tool helped identify high-risk areas, enabling health workers to shift strategy to screening everyone in those villages and attempt 100% X-ray coverage of the adult population.
• Portable hand-held X-ray machines, aided by AI, provide quick screening, and routine tests for BP and blood sugar are conducted alongside TB screening to reduce stigma.
• Post the focused activity, the tool can be used to screen vulnerable populations in lower-risk areas to make those villages TB-free.
• The National TB Elimination Programme aims to eliminate TB in India, aligning with the global deadline of 2030 after missing the initial 2025 target.

Key Concepts Involved:

• Vulnerability Mapping: Using data to identify populations at higher risk for a specific disease.
• Geospatial Data: Information related to specific geographic locations.
• Active Screening: Proactively testing populations to identify cases of a disease.