PGI Develops AI-Powered QST Device for Neuropathic Pain Diagnosis

The AI-powered device is designed to provide quicker results than traditional QST methods, which require trained technicians and detailed analysis.

Researchers at PGI Chandigarh have developed a Quantitative Sensory Testing (QST) device that aims to improve neuropathic pain diagnosis, particularly for conditions like diabetic neuropathy and sciatica. 

Created by Dr. Babita Ghai from the Department of Anaesthesia & Intensive Care, the AI-powered device has received an Indian patent. It is expected to reduce costs and improve accessibility.

Currently, QST is considered the standard for diagnosing neuropathy, but its high cost—between INR 40-50 lakh for imported systems—has limited its use. 

The new India-made device is priced at INR 2 lakh, with potential further reductions through large-scale production.

AI Integration for Faster Diagnosis

The AI-powered device is designed to provide quicker results than traditional QST methods, which require trained technicians and detailed analysis. 

This device assesses seven key parameters, including temperature, pressure, and vibration, and delivers results within 20-30 minutes. 

The AI integration simplifies the process, making it possible for primary healthcare providers to conduct the tests with minimal training.

“Early diagnosis helps in preventing further nerve damage. By making this device affordable and easy to use, we can facilitate early intervention and improve patient management,” said Dr. Ghai.

Research & Funding Support

The project has received backing from the Center of Innovation and Biodesign (CBIOD) and Thapar Institute. 

Initial patent funding came through CBIOD, and efforts are underway to secure additional support from the Indian Council of Medical Research (ICMR).

QST devices are traditionally manufactured in Germany and have been financially out of reach for many healthcare providers. 

The development of this India-made alternative is expected to make neuropathic pain detection more widely accessible, supporting early diagnosis and treatment in various healthcare settings.

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