Computer scientists have developed a new diagnostic tool using artificial intelligence (AI) and a digital camera to detect facial palsy, with 98% accuracy, including the patient’s gender and age.

Researchers from Iraq and Australia say the tool can reduce diagnostic errors that often occur with the common and treatable neurological disorder, caused by an impairment of the facial nerve, which results in temporary muscle weakness or paralysis on one side of the face, and affects about one in 60 people worldwide during their lifetime. Less commonly, paralysis of the face can be caused by tumour, infection or stroke.

In a new paper, published in BioMedInformatics, researchers from Middle Technical University (MTU) in Baghdad and the University of South Australia (UniSA), outline a real-time detection system for facial palsy using a microcomputer, digital camera, and deep learning algorithm.

Using a dataset of 26,000 images, containing 19,000 normal images and 1,600 facial palsy images, researchers used AI techniques to train computer vision systems to recognise the condition, differentiating them from healthy individuals. They then took photos of 20 patients with different degrees of facial palsy, using an algorithm to detect the condition in real time, as well as identifying their approximate age and gender.

“Using computer vision systems to detect facial palsy could not only prevent misdiagnosis, but also save patients and medical specialist time, effort and cost,” UniSA remote sensing engineer, Professor Javaan Chahl, said.

Read the full study here