Computer-aided detection of retained surgical needles from postoperative radiographs.

PURPOSE

Foreign objects, such as surgical sponges, needles, sutures, and other surgical instruments, retained in the patient's body can have dire consequences in terms of patient mortality as well as legal and financial penalties. We propose computer-aided detection (CAD) on postoperative radiographs as a potential solution to reduce the chance of retained foreign objects (RFOs) after surgery, thus alleviating one of the major concerns for patient safety in the operation room. A CAD system can function as a second pair of eyes or a prescreener for the surgeon and radiologist, depending on the CAD system design and the workflow. In this work, we focus on the detection of surgical needles on postoperative radiographs. As needles are frequently observed RFOs, a CAD system that can offer high sensitivity and specificity toward detecting surgical needles will be useful.

METHODS

Our CAD system incorporates techniques such as image segmentation, image enhancement, feature analysis, and curve fitting to detect surgical needles on radiographs. A dataset consisting of 108 cadaver images with a total of 116 needles and 100 cadaver "normal" images without needles was acquired with a portable digital x-ray system. A reference standard was obtained by marking the needle locations using an in-house developed graphical user interface. The 108 cadaver images with the needles were partitioned into a training set containing 53 cadaver images with 59 needles and a test set containing 55 cadaver images with 57 needles. All of the 100 cadaver normal images were reserved as a part of the test set and used to estimate the false-positive detection rate. Two operating points were chosen from the CAD system such that it can be operated in two modes, one with higher specificity (mode I) and the other with higher sensitivity (mode II).

RESULTS

For the training set, the CAD system with the rule-based classifier achieved a sensitivity of 74.6% with 0.15 false positives per image (FPs/image) in mode I and a sensitivity of 89.8% with 0.36 FPs/image in mode II. For the test set, the CAD system achieved a sensitivity of 77.2% with 0.26 FPs/image in mode I and a sensitivity of 84.2% with 0.6 FPs/image in mode II. For comparison, the CAD system with the neural network classifier achieved a sensitivity of 74.6% with 0.08 FPs/image in mode I and a sensitivity of 88.1% with 0.28 FPs/image in mode II for the training set, and a sensitivity of 75.4% with 0.23 FPs/image in mode I and a sensitivity of 86.0% with 0.57 FPs/image in mode II for the test set.

CONCLUSION

A novel CAD system has been developed for automated detection of needles inadvertently left behind in a patient's body from postsurgery radiographs. The pilot system offers reasonable performance in both the high sensitivity and high specificity modes. This preliminary study shows the promise of CAD as a low-cost and efficient aid for reducing retained surgical needles in patients.