BGU’s AI to predict ALS progress
BGN Technologies, the tech transfer company of Beersheba’s Ben-Gurion University, has unveiled an artificial intelligence-based platform to monitor and predict the progression of neurodegenerative diseases, such as Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s Disease.
The platform analyzes demographic and clinical data of ALS patients using machine learning and data mining algorithms to produce models that can predict the rate and pattern of ALS progression, identify factors essential for the prediction and generate reliable stratification to homogeneous sub-groups of the heterogeneous ALS population.
There is currently neither a cure nor an effective treatment to halt or reverse the progressive disease.
Developed by Prof. Boaz Lerner of the university’s Department of Industrial Engineering and Management, the platform is soon set to be expanded to benefit patients suffering from other diseases, including Alzheimer’s and Parkinson’s.
According to Lerner, the system provides three key benefits for those diagnosed with ALS or other neurodegenerative diseases, as well as caregivers and pharma companies.
The first benefit, Lerner told The Jerusalem Post, is the ability to reduce uncertainty and improve the patient’s and caregiver’s quality of life.
“If we can predict, for example, that the patient’s walking or speech ability will deteriorate in six months, he or she can organize the home to address their needs or move to a more appropriate environment, or start looking for a specific device to communicate with people. This will also enable physicians to know where to begin specific treatment; whether and when to focus on the respiratory system or physiotherapy.”
A second benefit is for pharma and medical device companies who can utilize the system to improve the design of clinical trials and record higher success rates due to its reliable patient stratification function.
By identifying markers of various patient sub-populations for which treatment is beneficial, thereby excluding other patients who would not benefit, clinical trials can be carried out much quicker, with higher statistical significance and less expense.
“The third benefit is that the tool that we have developed is quite generic, at least in the case of neurodegenerative diseases,” said Lerner.
“While ALS is a rare disease, Parkinson’s and Alzheimer’s diseases are much more prevalent. Using this algorithm, we can also extend our ability to treat these diseases.”