Modified Map-Seeking Circuit: Use of Computer-Aided Detection in Locating Postoperative Retained Foreign Bodies

• Published in: The Journal of surgical research Vol. 175; no. 2; pp. e47 - e52
• Main Authors: Asiyanbola, Bolanle, M.D., M.R.C.S, Cheng-Wu, Chao, Ph.D, Lewin, Jonathan S., M.D, Etienne-Cummings, Ralph, Ph.D
• Format: Journal Article
• Language: English
• Published: United States 15.06.2012
• Subjects: Abdomen - surgery; Algorithms; Diagnostic Errors; Foreign Bodies - diagnosis; Foreign Bodies - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Needles; Postoperative Complications - diagnosis; Postoperative Complications - diagnostic imaging; Radiography - methods; Radiography, Abdominal; Surgery; map seeking circuit; retained foreign bodies; machine learning; computer aided detection
• ISSN: 0022-4804; 1095-8673; 1095-8673
• DOI: 10.1016/j.jss.2011.11.1018

Background More than 98% of intra-operative X-rays taken to search for postoperative retained foreign bodies (RFBs) have negative findings; in over 30% of cases of such X-rays, the finding is a false negative. Newer technologies created to find RFBs must not only reduce the false-negative rate, but also must not increase the burden of detecting RFBs. We have introduced the use of computer-aided detection (CAD) to facilitate the detection of RFBs on X-rays utilizing a modified version of map-seeking circuit (MSC) algorithm the referenced map-seeking circuit (RMSC), for our proof-of-concept study for detection of needles in plain abdominal X-rays. Methods Images were obtained by using a portable cassette-based X-ray machine and a C-arm (digital) machine, both of which are commonly used in the operating room. The images obtained using these machines were divided into subimages of approximately 250 × 250 pixels each, for a total of 455 subimages from the cassette-based machine (A) and 365 from the digital machine (B) for use as test samples. Images obtained from A and B were analyzed separately using our modified MSC algorithm with a minimum (τ = 0) and a maximum threshold (τ = 0.5). Results The automated detection rate (positive predictive value) was 86%, with a false positive/negative rate of 10% to 15% when τ was zero. Conclusion The CAD-based RMSC algorithm has the potential to improve the accuracy with which RFBs can be found in X-rays. Further research is needed to optimize the detection rate and to identify a wider range of RFBs.