Point-of-Care Ultrasound Imaging for Automated Detection of Abdominal Haemorrhage: A Systematic Review.
Authors
Affiliations (3)
Affiliations (3)
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove Campus, Brisbane, Queensland, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Gardens Point Campus, Brisbane, Queensland, Australia.
- Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Herston Queensland, Australia; School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove Campus, Brisbane, Queensland, Australia.
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove Campus, Brisbane, Queensland, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Gardens Point Campus, Brisbane, Queensland, Australia. Electronic address: [email protected].
Abstract
Abdominal haemorrhage is a life-threatening condition requiring prompt detection to enable timely intervention. Conventional ultrasound (US) is widely used but is highly operator-dependent, limiting its reliability outside clinical settings. In anatomical regions, in particular Morison's Pouch, US provides a higher detection reliability due to the preferential accumulation of free fluid in dependent areas. Recent advancements in artificial intelligence (AI)-integrated point-of-care US (POCUS) systems show promise for use in emergency, pre-hospital, military, and resource-limited environments. This systematic review evaluates the performance of AI-driven POCUS systems for detecting and estimating abdominal haemorrhage. A systematic search of Scopus, PubMed, EMBASE, and Web of Science (2014-2024) identified seven studies with sample sizes ranging from 94 to 6608 images and patient numbers ranging between 78 and 864 trauma patients. AI models, including YOLOv3, U-Net, and ResNet50, demonstrated high diagnostic accuracy, with sensitivity ranging from 88% to 98% and specificity from 68% to 99%. Most studies utilized 2D US imaging and conducted internal validation, typically employing systems such as the Philips Lumify and Mindray TE7. Model performance was predominantly assessed using internal datasets, wherein training and evaluation were performed on the same dataset. Of particular note, only one study validated its model on an independent dataset obtained from a different clinical setting. This limited use of external validation restricts the ability to evaluate the applicability of AI models across diverse populations and varying imaging conditions. Moreover, the Focused Assessment with Sonography in Trauma (FAST) is a protocol drive US method for detecting free fluid in the abdominal cavity, primarily in trauma cases. However, while it is commonly used to assess the right upper quadrant, particularly Morison's pouch, which is gravity-dependent and sensitive for early haemorrhage its application to other abdominal regions, such as the left upper quadrant and pelvis, remains underexplored. This is clinically significant, as fluid may preferentially accumulate in these areas depending on the mechanism of injury, patient positioning, or time since trauma, underscoring the need for broader anatomical coverage in AI applications. Researchers aiming to address the current reliance on 2D imaging and the limited use of external validation should focus future studies on integrating 3D imaging and utilising diverse, multicentre datasets to improve the reliability and generalizability of AI-driven POCUS systems for haemorrhage detection in trauma care.