Study Design. Experimental in-vivo animal study. Objective. The aim of this study was to evaluate an Artificial Intelligence (AI)-enabled ultrasound imaging system's ability to detect, segment, classify, and display neural and other structures during trans-psoas spine surgery. Summary of Background Data. Current methodologies for intraoperatively localizing and visualizing neural structures within the psoas are limited and can impact the safety of lateral lumbar interbody fusion (LLIF). Ultrasound technology, enhanced with AI-derived neural detection algorithms, could prove useful for this task. Methods. The study was conducted using an in vivo porcine model (50 subjects). Image processing and machine learning algorithms were developed to detect neural and other anatomic structures within and adjacent to the psoas muscle while using an ultrasound imaging system during lateral lumbar spine surgery (SonoVision,™ Tissue Differentiation Intelligence, USA). The imaging system's ability to detect and classify the anatomic structures was assessed with subsequent tissue dissection. Dice coefficients were calculated to quantify the performance of the image segmentation. Results. The AI-trained ultrasound system detected, segmented, classified, and displayed nerve, psoas muscle, and vertebral body surface with high sensitivity and specificity. The mean Dice coefficient score for each tissue type was >80%, indicating that the detected region and ground truth were >80% similar to each other. The mean specificity of nerve detection was 92%; for bone and muscle, it was >95%. The accuracy of nerve detection was >95%. Conclusion. This study demonstrates that a combination of AI-derived image processing and machine learning algorithms can be developed to enable real-time ultrasonic detection, segmentation, classification, and display of critical anatomic structures, including neural tissue, during spine surgery. AI-enhanced ultrasound imaging can provide a visual map of important anatomy in and adjacent to the psoas, thereby providing the surgeon with critical information intended to increase the safety of LLIF surgery. Level of Evidence: N/A

Chest radiograph (CXR) is the most common imaging performed for both inpatients and outpatients. With advances in medicine and technology, newer devices/prosthesis are being used in the treatment of cardiothoracic conditions. Some of these are common while others are seen only in a handful of cases, especially in patients being treated or referred from tertiary care centers. It is important to know about these devices, their functionality, and radiographic appearances. Many of these devices also help us in understanding the clinical condition of the patient, as some are only used in unstable patients. Newer methods of life support are now available in intensive care units and these also can be seen on CXRs. In this review, we present various iatrogenic devices that we come across on a CXR and highlight important features to determine their correct placement and potential complications. The review looks at cardiac temporary and permanent pacing devices, cardiac interventional devices used to treat congenital heart disease, newer cardiac monitoring devices, and unusual surgical devices that one may come across on a CXR. We also suggest a stepwise algorithm to assess these devices on a CXR.

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