9 months ago
AIIMS Study Highlights Limitations of Smear Microscopy for TB Detection
A recent study by AIIMS in Gorakhpur found that the common TB test, smear microscopy, misses many TB cases.
The study looked at over 4,000 samples and found that smear microscopy only detected TB in 4.3% of cases.
A better test called TrueNat found TB in 13.7% of the same samples.
The study suggests that India should use more of these better tests to catch TB earlier and help eliminate the disease.
Experts say that using both old and new tests together can help control TB better, especially in places with fewer resources.
Early detection of drug resistance is also important to start the right treatment quickly.
Smear microscopy, the most common TB diagnostic test in India, has low sensitivity and misses many TB cases, especially in early-stage disease and patients with co-morbid conditions.
AIIMS study analyzed 4,249 samples and found smear microscopy detected TB in only 4.3% of cases, while TrueNat detected it in 13.7% of cases.
TrueNat, a chip-based real-time PCR test made in India, can rapidly detect TB and identify rifampicin resistance, which was found in 5.6% of confirmed cases.
Experts recommend expanding rapid molecular testing under the National Tuberculosis Elimination Programme (NTEP) to improve early detection and treatment outcomes.
Combining conventional microscopy with molecular diagnostics can strengthen TB control efforts, especially in rural and underserved areas.
- Who
- All India Institute of Medical Sciences (AIIMS), Gorakhpur
- What
- A study highlighting the limitations of smear microscopy in detecting TB cases
- Where
- India, with a focus on high-burden and resource-limited areas
- When
- The study was released on Sunday (specific date not mentioned)
- Why
- To improve TB diagnosis and control efforts, especially in early-stage disease, extra-pulmonary TB, and patients with co-morbid conditions like HIV and diabetes
Key facts
- Study Conducted By
- All India Institute of Medical Sciences (AIIMS), Gorakhpur
- Journal Published In
- International Journal of Mycobacteriology
- Samples Analyzed
- 4,249 pulmonary and extra-pulmonary samples
- Smear Microscopy Detection Rate
- 4.3%
- TrueNat Detection Rate
- 13.7%
- Rifampicin Resistance Found
- 5.6% of confirmed cases
- Recommended Diagnostic Tool
- TrueNat Mycobacterium TB/rifampicin (RIF) test
- Programme for Expansion
- National Tuberculosis Elimination Programme (NTEP)
Quotes
Dr Radhika Tandon
Professor of ophthalmology at AIIMS and part of the team that developed the MadhuNetrAI app.
“Several primary health centres already have fundus cameras. The AI model can be plugged into any one of them. This means doctors have to invest their time only on patients who are likely to have the condition rather than screening every diabetic.”
indianexpress.com
“The idea is to have the device at primary health centres where patients are screened and treated for diabetes, so that they also get tested for diabetic retinopathy at the same location at the same time.”
indianexpress.com
Dr Himanshu Chauhan
Head of the Integrated Disease Surveillance Programme.
“Earlier, the model would also flag traffic crashes or deaths due to natural disasters. Over time, the algorithm has learnt what we are looking for and accurately flags news of several people getting diarrhoea or fainting.”
indianexpress.com
“This way we can use the skills of the officers for other activities such as planning a response instead of poring over newspapers.”
indianexpress.com
Health ministry sources
Sources from the health ministry discussing the AI-enabled Clinical Decision Support System (CDSS).
“With high accuracy in its top three recommendations, it acts as a smart filter that reduces doctors’ workload. Importantly, doctors maintain complete control; they can accept or reject AI suggestions and their feedback continuously improves the system.”
indianexpress.com
