Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. CSM is a chronic, progressive condition that can cause neck pain, muscle weakness, difficulty walking and other debilitating symptoms. While the diagnosis is sometimes clear, often the diagnosis can take years because symptoms aren’t recognized until the later stages, and by then, treatment options are limited.
A multidisciplinary team of surgeon-scientists, computer scientists and researchers at WashU developed an artificial intelligence (AI)-based approach that could help clinicians screen for and diagnose CSM up to 30 months earlier, opening new opportunities for earlier treatment. The findings were published online in January in npj Digital Medicine.
Salim Yakdan, MD, a postdoctoral research fellow in the Taylor Family Department of Neurosurgery at WashU Medicine, and Ben Warner, a doctoral student in computer science and engineering at the McKelvey School of Engineering, co-first authors on the research, used seven different AI models to analyze large datasets containing electronic health record data of more than 2 million people with and without CSM. The models examined patterns of health-care interactions, such as tests and diagnoses, recorded in electronic health records to spot patients whose medical histories resemble those already diagnosed with CSM, helping to flag individuals who may be at higher risk.
Read more on the McKelvey Engineering website.
