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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. [ABSTRACT FROM AUTHOR]

Soil emissivity signatures were constructed using the digital imaging and remote sensing image generation (DIRSIG) model and Blender three-dimensional (3-D) graphic design software. Using these tools, the geometry, radiometry, and chemistry of quartz were exploited to model the presence of particle size effects in the thermal spectra of disturbed soil. Using the physics engines within the Blender 3-D graphic design software, a physical representation of a granular soil scene was created. Chemical and optical properties of pure quartz were assigned to particles in the scene based on particle size. The spectral signature of disturbed soil was modeled by the physical mixture of small fine particles (50 μm diameter) and larger grains (500 μm diameter). The study demonstrated that by combining realistic target geometry and spectral measurements of pure quartz, emissivity of complex soil mixtures could be modeled without functional data fitting or rigorous analysis of material dynamics. [ABSTRACT FROM AUTHOR]