Computer-aided detection of retained surgical needles from postoperative radiographs

Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review

Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method's strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards.

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.

Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items

BACKGROUND

Retained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence of adverse events. To date, no computer-aided diagnosis software specialized for detecting retained surgical sponges has been reported. We developed a software program that enables easy and effective computer-aided diagnosis of retained surgical sponges with high sensitivity and specificity using the technique of deep learning, a subfield of artificial intelligence.

STUDY DESIGN

In this study, we developed the software by training it through deep learning using a dataset and then validating the software. The dataset consisted of a training set and validation set. We created composite x-rays consisting of normal postoperative x-rays and surgical sponge x-rays for a training set (n = 4,554) and a validation set (n = 470). Phantom x-rays (n = 12) were prepared for software validation. X-rays obtained with surgical sponges inserted into cadavers were used for validation purposes (formalin: Thiel's method = 252:117). In addition, postoperative x-rays without retained surgical sponges were used for the validation of software performance to determine false-positive rates. Sensitivity, specificity, and false positives per image were calculated.

RESULTS

In the phantom x-rays, both the sensitivity and specificity in software image interpretation were 100%. The software achieved 97.7% sensitivity and 83.8% specificity in the composite x-rays. In the normal postoperative x-rays, 86.6% specificity was achieved. In reading the cadaveric x-rays, the software attained both sensitivity and specificity of >90%.

CONCLUSIONS

Software with high sensitivity for diagnosis of retained surgical sponges was developed successfully.

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.

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

In digital breast tomosynthesis (DBT), the high-attenuation metallic clips marking a previous biopsy site in the breast cause errors in the estimation of attenuation along the ray paths intersecting the markers during reconstruction, which result in interplane and inplane artifacts obscuring the visibility of subtle lesions. We proposed a new metal artifact reduction (MAR) method to improve image quality. Our method uses automatic detection and segmentation to generate a marker location map for each projection (PV). A voting technique based on the geometric correlation among different PVs is designed to reduce false positives (FPs) and to label the pixels on the PVs and the voxels in the imaged volume that represent the location and shape of the markers. An iterative diffusion method replaces the labeled pixels on the PVs with estimated tissue intensity from the neighboring regions while preserving the original pixel values in the neighboring regions. The inpainted PVs are then used for DBT reconstruction. The markers are repainted on the reconstructed DBT slices for radiologists' information. The MAR method is independent of reconstruction techniques or acquisition geometry. For the training set, the method achieved 100% success rate with one FP in 19 views. For the test set, the success rate by view was 97.2% for core biopsy microclips and 66.7% for clusters of large post-lumpectomy markers with a total of 10 FPs in 58 views. All FPs were large dense benign calcifications that also generated artifacts if they were not corrected by MAR. For the views with successful detection, the metal artifacts were reduced to a level that was not visually apparent in the reconstructed slices. The visibility of breast lesions obscured by the reconstruction artifacts from the metallic markers was restored.