A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

Cerebral perfusion and neuropsychological follow up in mild traumatic brain injury: Acute versus chronic disturbances?

In a subgroup of patients with mild traumatic brain injury (TBI) residual symptoms, interfering with outcome and return to work, are found. With neuropsychological assessment cognitive deficits can be demonstrated although the pathological underpinnings of these cognitive deficits are not fully understood. As the admission computed tomography (CT) often is normal, perfusion CT imaging may be a useful indicator of brain dysfunction in the acute phase after injury in these patients.In the present study, directly after admission perfusion CT imaging was performed in mild TBI patients with follow-up neuropsychological assessment in those with complaints and a normal non-contrast CT. Neuropsychological tests comprised the 15 Words test Immediate Recall, Trailmaking test part B, Zoo Map test and the FEEST, which were dichotomized into normal and abnormal. Perfusion CT results of patients with normal neuropsychological test scores were compared to those with abnormal test scores.In total eighteen patients were included. Those with an abnormal score on the Zoo Map test had a significant lower CBV in the right frontal and the bilateral parieto-temporal white matter. Patients with an abnormal score on the FEEST had a significant higher MTT in the bilateral frontal white matter and a significant decreased CBF in the left parieto-temporal grey matter. No significant relation between the perfusion CT parameters and the 15 Words test and the Trailmaking test part B was present.In conclusion, impairments in executive functioning and emotion perception assessed with neuropsychological tests during follow up were related to differences in cerebral perfusion at admission in mild TBI. The pathophysiological concept of these findings is discussed.