Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans

Abstract

Midline shift (MLS) is a critical indicator of the severity of brain trauma and is even suggestive of changes in intracranial pressure. At present, radiologists have to manually measure the MLS using laborious techniques. Automatic detection of MLS using artificial intelligence can be a cutting-edge solution for emergency health-care personnel to help in prompt diagnosis and treatment. In this study, we sought to determine the accuracy and the prognostic value of our screening tool that automatically detects MLS on computed tomography (CT) images in patients with traumatic brain injuries (TBIs). The study enrolled TBI cases, who presented at the Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi. Institutional ethics committee permission was taken before starting the study. The data collection was carried out for over nine months, i.e., from January 2020 to September 2020. The data collection included head CT scans, patient demographics, clinical details as well as radiologist’s reports. The radiologist’s reports were considered the “gold standard” for evaluating the MLS. A deep learning-based three dimensional (3D) convolutional neural network (CNN) model was developed using 176 head CT scans. The developed 3D CNN model was trained using 156 scans and was tested on 20 head CTs to determine the accuracy and sensitivity of the model. The screening tool was correctly able to detect 7/10 MLS cases and 4/10 non-MLS cases. The model showed an accuracy of 55% with high specificity (70%) and moderate sensitivity of 40%. An automated solution for screening the MLS can prove useful for neurosurgeons. The results are strong evidence that 3D CNN can assist clinicians in screening MLS cases in an emergency setting.