Automated identification of retained surgical items in radiological images

Gady Agam; Lin Gan; Mario Moric; Vicko Gluncic

doi:10.1117/12.2082384

17 March 2015 Automated identification of retained surgical items in radiological images

Gady Agam, Lin Gan, Mario Moric, Vicko Gluncic

Proceedings Volume 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations; 94180X (2015) https://doi.org/10.1117/12.2082384
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

Citation Download Citation

Gady Agam, Lin Gan, Mario Moric, and Vicko Gluncic "Automated identification of retained surgical items in radiological images", Proc. SPIE 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, 94180X (17 March 2015); https://doi.org/10.1117/12.2082384

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