Artificial intelligence for detection of intracranial haemorrhage on head computed tomography scans: diagnostic accuracy in Hong Kong

• Published in: Hong Kong medical journal = Xianggang yi xue za zhi Vol. 29; no. 2; p. 112
• Main Authors: Abrigo, Jill M, Ka-long, Ko, Chen, Qianyun, Lai, Billy MH, Cheung, Tom CY, Winnie CW Chu, Yu, Simon CH
• Format: Journal Article
• Language: Chinese; English
• Published: Hong Kong Hong Kong Academy of Medicine 01-04-2023
• Subjects: Accuracy; Artificial intelligence; Datasets; Emergency medical care; Hemorrhage; Hospitals; Medical imaging; Physicians; Software; Statistical analysis; Tomography; United Kingdom > UK; China; Wales; Hong Kong China
• ISSN: 1024-2708; 2226-8707
• DOI: 10.12809/hkmj209053

Introduction: The use of artificial intelligence (AI) to identify acute intracranial haemorrhage (ICH) on computed tomography (CT) scans may facilitate initial imaging interpretation in the accident and emergency department. However, AI model construction requires a large amount of annotated data for training, and validation with real-world data has been limited. We developed an algorithm using an open-access dataset of CT slices, then assessed its utility in clinical practice by validating its performance on CT scans from our institution. Methods: Using a publicly available international dataset of >750 000 expert-labelled CT slices, we developed an AI model which determines ICH probability for each CT scan and nominates five potential ICH-positive CT slices for review. We validated the model using retrospective data from 1372 non-contrast head CT scans (84 [6.1%] with ICH) collected at our institution. Results: The model achieved an area under the curve of 0.842 (95% confidence interval=0.791-0.894; P<0.001