Artificial Intelligence-Based Algorithm for Intensive Care of Traumatic Brain Injury

Traumatic brain injury (TBI) is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries. The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies.

Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care. In the ICU, many tens of variables are continuously monitored (e.g. intracranial pressure, mean arterial pressure and cerebral perfusion pressure) that indirectly give information regarding the condition of the patient.

However, only one variable, such as intracranial pressure, may yield hundreds of thousands of data points per day. Thus, it is impossible for the human brain to comprehend the resulting millions of daily collected data points from all monitored data. This is why researchers at Helsinki University Hospital (HUS) started to develop an artificial intelligence (AI) based algorithm that could help doctors treat patients with severe TBI. At its best, such an algorithm could predict the outcome of the individual patient and give objective data regarding the condition and prognosis of the patient and how it changes during treatment.

"A dynamic prognostic model like this has not been presented before. Although this is a proof-of-concept and it will still take some time before we can implement algorithms like this into daily clinical practice, our study reflects how and into what direction modern intensive care is evolving", says Rahul Raj, Adjunct Professor of Experimental Neurosurgery from HUS and one of the authors of the paper.

The algorithms can predict the probability of the patient dying within 30-days with accuracy of 80-85%.

"We have developed two separate algorithms. The first algorithm is simpler and is based only upon objective monitor data. The second algorithm is slightly more complex and includes data regarding the level of consciousness, measured by the widely used Glasgow Coma Scale score. As expected, the accuracy of the more complex algorithm is slightly better than for the simpler algorithm. Still, the accuracy of both algorithms is surprisingly good, considering that the simpler model is based upon only three main variables and the more complex upon five main variables", tells Eetu Pursiainen, Data Scientist from the Analytics and AI Development Department at HUS, one of the authors and main coders of the algorithms.

In the future, the algorithms still have to be validated in national and international external datasets.

"Finland is one of the world leaders in artificial intelligence solutions in specialized healthcare and Helsinki University Hospital, as one of the largest hospitals in Europe, plays an important role in bringing Finnish excellence into the world. Because of this, we think that it is important act ethically and share our algorithms openly and free of charge for further development, both nationally and internationally", states Miikka Korja, Chair of the HUS Artificial Intelligence Steering Group and Adjunct Professor of Neurosurgery at the University of Helsinki.

Rahul Raj, Teemu Luostarinen, Eetu Pursiainen, Jussi P Posti, Riikka SK Takala, Stepani Bendel, Teijo Konttila, Miikka Korja.
Machine learning-based dynamic mortality prediction after traumatic brain injury.
Sci Rep 9, 17672 (2019). doi: 10.1038/s41598-019-53889-6.

Most Popular Now

AI-Pathway Companion Prostate Cancer fro…

AI-Pathway Companion Prostate Cancer(2), a digital companion from Siemens Healthineers to support clinical decision-making, has recently received the CE mark for use in the clinical pathway of prostate cancer, the...

Philips Launches HealthSuite System of E…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced the HealthSuite System of Engagement, an integrated, modular set of standards-based capabilities that support the development...

AI may Help Spot Newborns at Risk for Mo…

An artificial intelligence (AI) device that has been fast-tracked for approval by the Food and Drug Administration may help identify newborns at risk for aggressive posterior retinopathy of prematurity (AP-ROP)...

Boehringer Ingelheim Launches Patient-Ce…

Boehringer Ingelheim and Carebox Healthcare Solutions announced the recent launch of MyStudyWindow, a digital platform empowering patients, families, caregivers, and doctors to learn about Boehringer Ingelheim's studies by offering information...

Best of Breed: Start with Data in Open P…

Better is attending this year's Digital Health Rewired conference and exhibition at Olympia London alongside Taunton and Somerset NHS Foundation Trust, which is using its open platform to develop an...

Highland Marketing is Sponsoring the Caf…

Highland Marketing will be returning to Digital Health Rewired this year, where it will be sponsoring the café that has been created with an expanded exhibition area. Digital Health Rewired launched...

Siemens Healthineers Introduces Teamplay…

Siemens Healthineers announces market introduction of the teamplay digital health platform. With the teamplay digital health platform Siemens Healthineers paves the way for healthcare providers' digital transformation - facilitating easy...

International Scientific Symposium DigiH…

13 November 2020, Pfarrkirchen, Germany DigiHealthDay @DIT-ECRI is going to be a daylong action-packed event targeting primarily academia - from established researchers, to young scientists and students. Following the theme "How...

Digital Heart Model will Help Predict Fu…

In recent times, researchers have increasing found that the power of computers and artificial intelligence is enabling more accurate diagnosis of a patient's current heart health and can provide an...