The initial symptoms of ALS are often mild and varied such as trouble grasping a pen, lifting a coffee cup, or a change in vocal pitch when speaking. Image Credit: imajinajib / Shutterstock.

Mitsubishi Tanabe Pharma Canada has developed an artificial intelligence (AI) programme to assist physicians in the early detection of amyotrophic lateral sclerosis (ALS).

The programme screens de-identified electronic medical records (EMRs) to identify individuals who require follow-up investigations and speciality centre referrals.

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AI in healthcare is a booming market, especially following the Covid-19 pandemic. GlobalData forecasts the AI market to generate $93bn in sales in 2023.

The “Process for Progress in ALS: An EMR-based Practice Enhancement Initiative” programme uses a clinical algorithm, MNd-5, to support physicians in timely decision-making.

ALS is a rare and terminal neurogenerative disease, the initial symptoms of which are often mild and varied such as trouble grasping a pen, lifting a coffee cup, or a change in vocal pitch when speaking. The disease is difficult to diagnose as there is no gold standard test and can only be diagnosed through a clinical examination and series of diagnostic tests, often ruling out other diseases that mimic ALS.

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“Early diagnosis and treatment of ALS can improve outcomes, but the disease can be difficult to diagnose in its early stages. The more time that passes before diagnosis, the less opportunities exist for disease management for someone living with ALS,” said Dr Angela Genge, executive director, ALS Centre of Excellence at The Neuro (Montreal Neurological Institute-Hospital) in Montreal.

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“Process for Progress in ALS is a unique advancement in AI medical technology which can help HCPs identify patients who present multiple warning signs for ALS so that they can receive expedited follow-ups, diagnoses, and treatment if needed.”

The MNd-5 algorithm chooses patients for priority review comparing their presenting characteristics and electromyography (EMG) findings to a reference population of ALS patients.

The algorithm was applied to the electronic health records at the Toronto Data Lab of Ensho Health. It will be made available to community neurologists as an integration service with Epic, Cerner, Accuro, OscarPro, Indivicare, Mediquest and other electronic medical record systems.