New computer program helps gauge eye spasm severity

Researchers at a US university have developed a computer program that can help clinicians gauge the severity of blepharospasm — an uncontrollable muscle contractions around the eye the rating scales of which are unreliable.


Developed by David Peterson and Terrence Sejnowski of California-based Salk Institute for Biological Studies, the computer program analyses videos of patients’ faces to provide a more objective scale for research and diagnosis.

“The field of neurology has a long tradition of making clinical decisions based on careful observations. We hope to supplement that expertise by leveraging advances in computer vision and machine learning,” said Sejnowski, head of Salk’s Computational Neurobiology Laboratory and senior author of the research.

Blepharospasm involves abnormal and involuntary twitching of the eyelid and surrounding muscles. Often, its cause is unknown, but the spasms can be triggered by fatigue or stress and are also associated with certain drugs, hormone changes and many other disorders, including multiple sclerosis.


Scientists have earlier developed three different rating scales that can be used to put a number on blepharosplasm severity, but studies have shown that the ratings have a high level of variability.

“They’re inherently subjective because they’re based on human judgment,” said Peterson, a project scientist at Salk and the University of California, San Diego and first author of the new paper published recently in the medical journal of the American Academy of Neurology.

Sejnowski and Peterson’s team customised existing facial analysis software called Computer Expression Recognition Toolbox (CERT) to make the ratings objective.

The team modified the program to quantify how often a patient’s eyes closed when they were instructed to keep them open. The program was tested using 49 existing videos of patients with blepharospasm.

The new program was able to find a patient’s face in 100 per cent of the video frames for 46 of the 49 patients–in the three other cases, it identified the face in more than 93 per cent of video frames.

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