University of Pittsburgh School of Medicine data scientists and UPMC neurotrauma surgeons have created a prognostic model that uses automated brain scans and machine learning to inform outcomes in patients with severe traumatic brain injuries (TBI).
Their findings are published in the journal Radiology, in a paper titled, “Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.”
The researchers demonstrated that their advanced machine-learning algorithm can analyze brain scans and relevant clinical data from TBI patients to quickly and accurately predict survival and recovery six months after the injury.
“Every day, in hospitals across the United States, care is withdrawn from patients who would have otherwise returned to independent living,” said co-senior author David Okonkwo, MD, PhD, professor of neurological surgery at Pitt and UPMC. “The majority of people who survive a critical period in an acute care setting make a meaningful recovery—which further underscores the need to identify patients who are more likely to recover.”
It often takes two weeks for TBI patients to emerge from their coma and begin their recoveries—yet severe TBI patients are often taken off life support within the first 72 hours after hospital admission. The new predictive algorithm, validated across two independent patient cohorts, could be used to screen patients shortly after admission and can improve clinicians’ ability to deliver the best care at the right time.
Recognizing the need for better ways to assist clinicians, the team of data scientists at Pitt set out to leverage their expertise in advanced artificial intelligence to develop a sophisticated tool to understand the nature of each unique patient’s TBI.
“There is a great need for better quantitative tools to help intensive care neurologists and neurosurgeons make more informed decisions for patients in critical condition,” explained corresponding author Shandong Wu, PhD, associate professor of radiology, bioengineering, and biomedical informatics at Pitt. “This collaboration with Okonkwo’s team gave us an opportunity to use our expertise in machine learning and medical imaging to develop models that use both brain imaging and other clinically available data to address an unmet need.”
The researchers observed the model proved itself by accurately predicting patients’ risk of death and unfavorable outcomes at six months following the traumatic incident. To validate the model, Pitt researchers tested it with two patient cohorts: one of over 500 severe TBI patients previously treated at UPMC and the other an external cohort of 220 patients from 18 institutions across the country, through the TRACK-TBI consortium.
The researchers hope that AI can provide a tool to improve clinical decision-making early when a TBI patient is admitted to the emergency room. Their tool may pave the way for other tools to be developed using machine learning and artificial intelligence.
