Researchers develop AI platform to detect neurodegenerative disorders

5 March 2019 (Last Updated December 23rd, 2019 10:23)

A research team at the Icahn School of Medicine has developed a new artificial intelligence (AI) platform to identify different neurodegenerative disorders using brain tissue samples.

Researchers develop AI platform to detect neurodegenerative disorders
The new AI platform is expected to facilitate targeted biomarkers and therapeutics for neurodegenerative diseases. Credit: geralt via Pixabay.

A research team at the Icahn School of Medicine has developed a new artificial intelligence (AI) platform to identify different neurodegenerative disorders using brain tissue samples.

The team combined advanced computer science and mathematical approaches with microscope technology, computer vision and AI to more accurately classify a variety of disorders.

Called the Precise Informatics Platform, this new solution is expected to facilitate targeted biomarkers and therapeutics for better diagnosis and outcomes in patients with complex brain conditions.

The platform applies machine learning techniques to digitised microscopic slides that are prepared with tissue samples. This helps generate a convolutional neural network that could accurately detect neurofibrillary tangles, a characteristic of a variety of neurodegenerative diseases, including Alzheimer’s and chronic traumatic encephalopathy.

“Ultimately, this project will lead to more efficient and accurate diagnosis of neurodegenerative diseases.”

Icahn School of Medicine pathology and neuroscience professor John Crary said: “Utilising artificial intelligence has great potential to improve our ability to detect and quantify neurodegenerative diseases, representing a major advance over existing labour-intensive and poorly reproducible approaches.

“Ultimately, this project will lead to more efficient and accurate diagnosis of neurodegenerative diseases.”

The Precise Informatics Platform provides data management, visual exploration, object outlining, multi-user review and assessment of deep learning algorithm results.

Icahn School of Medicine Oncological Sciences chair Carlos Cordon-Cardo said: “Mount Sinai is the largest academic pathology department in the country and processes more than 80 million tests a year, which offers researchers access to a broad set of data that can be used to improve testing and diagnostics, ultimately leading to better diagnosis and patient outcomes.”

The team conducted the research in partnership with Boston University School of Medicine, VA Boston Healthcare System and UT Southwestern Medical Center.

Data from the study has been published in the Nature medical journal.