Computer-aided simple triage
- 182 Downloads
Computer-aided detection (CAD) established its role in medical imaging as second reader aimed to boost the diagnostic accuracy of human interpreter. As the diagnostic performance of CAD systems improves and more imaging modalities are covered, CAD steps forward to fill new, more demanding positions in medical practice. In this paper, we investigate how the introduction of CAD for emergency diagnostic imaging shifts the use case paradigm from second reader to initial interpreter and triage tool.
We start from extracting common characteristics of exiting CAD systems and compare them to those for emergency diagnostic imaging modalities. Based on the deduced requirements, we define a new class of CAD systems—Computer-aided simple triage (CAST) and explore its properties, use case scenarios and clinical benefits. We also discuss the differences between the CAST, CAD, and automated computer diagnosis.
A CAST system should serve as a simple triage tool performing a fully automatic analysis and providing initial classification at “per study” level. Positive studies are then immediately analyzed by expert reader, thus reducing delay for patients with critical conditions, while negative studies can be initially dealt with by less experienced staff. Automatic image quality and study complexity assessment can serve as reading prioritization key. CAST system should exhibit sufficiently high specificity, while not compromising the high sensitivity per study.
CAST systems have a potential to become an “enabling technology” allowing introduction of advanced imaging techniques into the emergency workflow protocols by addressing the reader unavailability and reading prioritization problems.
KeywordsCAD Computer-aided simple triage Emergency diagnostic imaging
- 2.Karacan Ö et al (2002) Chest radiography and the solitary pulmonary nodule: inter-observer variability, and reliability for detecting nodules and calcification. J Radiol. http://www.jradiology.com/arts/45.pdf
- 5.Houssami N et al (2003) Sydney breast imaging accuracy study: comparative sensitivity and specificity of mammography and sonography in young women with symptoms. Am J Roentgenol 180(4): 935–940Google Scholar
- 9.Hudyma E, Terlikowski G (2008) Computer-aided detection of early strokes and its evaluation on the base of ct images. In: International multiconference on computer science and information technology, IMCSIT 2008, pp 251–254Google Scholar
- 10.Chawla M et al (2009) A method for automatic detection and classification of stroke from brain CT images. In: Proceedings of the IEEE engineering in medicine and biology society (EMBC’ 09) Minnesota, USA, 2009, pp 3581–3584Google Scholar
- 12.Lee N, Tek H, Laine AF (2008) True–false Lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative–discriminative model matching. Proc SPIE Med Imaging 6915(2):69152V.1–69152V.11Google Scholar
- 18.Donnelley M, Knowles G, Hearn T (2008) A CAD system for long-bone segmentation and fracture detection. In: Proceedings of the 3rd international conference on image and signal processing, Cherbourg-Octeville, France, pp 153–162Google Scholar