New Delhi: Researchers at the Indian Institute of Science (IISc) in Bengaluru have developed an artificial intelligence (AI) tool that can detect a nerve-related disorder known as carpal tunnel syndrome (CTS). The tool developed by IISc researchers in collaboration with Aster-CMI Hospital, Bengaluru detects CTS by identifying the median nerve in ultrasound videos.
CTS arises when the median nerve, which runs from the forearm into the hand, is compressed at the carpal tunnel part of the wrist, resulting in numbness, tingling or pain, the researchers said.
It is one of the most common nerve-related disorders, specifically affecting individuals who perform repetitive hand movements, such as office staff who work with keyboards, assembly line workers, and sportspersons, they said.
Doctors currently use ultrasound to visualise the median nerve, and assess its size, shape, and any potential abnormalities.
“But unlike X-rays and MRI scans, it is hard to detect what is going on in ultrasound images and videos,” said Karan R Gujarati, a former MTech student at IISc.
“At the wrist, the nerve is quite visible, its boundaries are clear, but if you go down to the elbow region, there are many other structures, and the boundaries of the nerve are not clear,” said Gujarati, first author of the study published in the journal IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
Tracking the median nerve is also important for treatments that require doctors to administer local anaesthesia to the forearm or block the median nerve to provide pain relief.
The team turned to a machine learning model based on transformer architecture, similar to the one powering ChatGPT.
The model was originally developed to detect dozens of objects simultaneously in YouTube videos.
The team stripped the model’s computationally expensive elements to speed it up, and cut down the number of objects it could track to just one – the median nerve, in this case.
They collaborated with Lokesh Bathala, Lead Consultant Neurologist at Aster-CMI Hospital, to collect and annotate ultrasound videos from both healthy participants and people with CTS, to train the model.
Once trained, the model was able to segment the median nerve in individual frames of the ultrasound video.
“Imagine a video of an autonomous car. If the car is moving on the road, you want to track the car,” said study corresponding author Phaneendra K Yalavarthy, Professor at CDS.
“In the same way, we are able to track the nerve throughout the video,” Yalavarthy said.
The model was also able to automatically measure the cross-sectional area of the nerve, which is used to diagnose CTS. This measurement is performed manually by a sonographer.
“The tool automates this process. It measures the cross-sectional area in real time,” said Bathala.
It was able to report the cross-sectional area of the median nerve with more than 95 per cent accuracy at the wrist region, the researchers added.