Application of Artificial Intelligence in Early Detection of Epidural Hematoma

Abstract

Head injury is an emergency condition that is still the leading cause of death. Manifestations of head injury may be accompanied by intracranial hemorrhage, one of which is epidural hemorrhage or epidural hematoma (EDH). This study aims to explore the potential application of artificial intelligence in the early detection of epidural hematoma with the main objective of improving early diagnosis and management of this condition. This paper uses a literature review study method derived from the analysis of various references. The references used have inclusion criteria in the form of full text type, related to the topic of discussion using "Artificial Intelligence", "Deep learning", and "epidural hematome". The result of this study is that EWS finds the presence of EDH in two or more images from a set of CT-Scans, the system will send an email to the medical practitioner with an attachment of images showing EDH for immediate action. In previous tests, the system successfully diagnosed 13 out of 27 patients with EDH, with 85% of the diagnoses having a high level of importance. This shows that the EWS has the potential to improve early detection and management of EDH cases, as well as provide medical practitioners with important information for appropriate action.