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Algorithm Analyzes Head CT Scans and Clinical Data to Predict Outcomes in TBI Patients

By MedImaging International staff writers
Posted on 27 Apr 2022
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Image: ML model can steer TBI patients to life-saving care (Photo courtesy of Pexels)
Image: ML model can steer TBI patients to life-saving care (Photo courtesy of Pexels)

Traumatic brain injury (TBI) is one of the most pressing public health issues and remains a leading cause of death in people under the age of 45. Patients with TBI often takes two weeks to emerge from their coma and begin their recoveries, although severe TBI patients are often taken off life support within the first 72 hours after hospital admission. Recognizing the need for better ways to assist clinicians, a team of scientists has developed a prognostic model that is the first to use automated brain scans and machine learning to inform outcomes in patients with severe TBI.

The team of data scientists and neurotrauma surgeons at the University of Pittsburgh School of Medicine (UPMC, Pittsburgh, PA, USA) showed 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 at six-months after the injury. The team leveraged their expertise in advanced artificial intelligence (AI) to develop the sophisticated tool to understand the nature of each unique patient’s TBI. The team developed a custom AI model that processed multiple brain scans from each patient and combined it with an estimate of coma severity and information about the patient’s vital signs, blood tests and heart function.

Importantly, because brain imaging techniques evolve over time and image quality can vary dramatically from patient to patient, the researchers accounted for data irregularity by training their model on different image-taking protocols. 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, the 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 external cohort was critical to test the model’s prediction ability.

“We hope this research shows that AI can provide a tool to improve clinical decision-making early when a TBI patient is admitted to the emergency room, towards yielding a better outcome for the patients,” said the UPMC researchers.

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