A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage
Salita Angkurawaranon, Nonn Sanorsieng, Kittisak Unsrisong, Papangkorn Inkeaw, Patumrat Sripan, Piyapong Khumrin, Chaisiri Angkurawaranon, Tanat Vaniyapong, Imjai Chitapanarux
Abstract
Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports. Our study examines the diagnostic performance of a deep learning model and compares the performance of that with detection, localization and classification of traumatic ICHs involving radiology, emergency medicine, and neurosurgery residents. Our results demonstrate that the high level of accuracy achieved by the deep learning model, (0.89), outperforms the residents with regard to sensitivity (0.82) but still lacks behind in specificity (0.90). Overall, our study suggests that the deep learning model may serve as a potential screening tool aiding the interpretation of head CT scans among traumatic brain injury patients.