Modified map-seeking circuit: use of computer-aided detection in locating postoperative retained foreign bodies

Artificial intelligence in the detection of non-biological materials

Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Presently, there is a strong reliance on radiologists to accurately diagnose and characterize foreign bodies in a timely fashion, a task that can be readily augmented with AI tools. This article will first explore the most common clinical scenarios involving foreign bodies, such as retained surgical instruments, open and penetrating injuries, catheter and tube malposition, and foreign body ingestion and aspiration. By initially exploring the existing imaging techniques employed for diagnosing these conditions, the potential role of AI in detecting non-biological materials can be better elucidated. Yet, the heterogeneous nature of foreign bodies and limited data availability complicates the development of computer-aided detection models. Despite these challenges, integrating AI can potentially decrease radiologist workload, enhance diagnostic accuracy, and improve patient outcomes.

Risk factors and preventive strategies for unintentionally retained surgical sharps: a systematic review

BACKGROUND

A retained surgical item (RSI) is defined as a never-event and can have drastic consequences on patient, provider, and hospital. However, despite increased efforts, RSI events remain the number one sentinel event each year. Hard foreign bodies (e.g. surgical sharps) have experienced a relative increase in total RSI events over the past decade. Despite this, there is a lack of literature directed towards this category of RSI event. Here we provide a systematic review that focuses on hard RSIs and their unique challenges, impact, and strategies for prevention and management.

METHODS

Multiple systematic reviews on hard RSI events were performed and reported using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and AMSTAR (Assessing the methodological quality of systematic reviews) guidelines. Database searches were limited to the last 10 years and included surgical "sharps," a term encompassing needles, blades, instruments, wires, and fragments. Separate systematic review was performed for each subset of "sharps". Reviewers applied reciprocal synthesis and refutational synthesis to summarize the evidence and create a qualitative overview.

RESULTS

Increased vigilance and improved counting are not enough to eliminate hard RSI events. The accurate reporting of all RSI events and near miss events is a critical step in determining ways to prevent RSI events. The implementation of new technologies, such as barcode or RFID labelling, has been shown to improve patient safety, patient outcomes, and to reduce costs associated with retained soft items, while magnetic retrieval devices, sharp detectors and computer-assisted detection systems appear to be promising tools for increasing the success of metallic RSI recovery.

CONCLUSION

The entire healthcare system is negatively impacted by a RSI event. A proactive multimodal approach that focuses on improving team communication and institutional support system, standardizing reports and implementing new technologies is the most effective way to improve the management and prevention of RSI events.

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.