Cotton balls are a versatile and efficient tool commonly used in neurosurgical procedures to absorb fluids and manipulate delicate tissues. However, the use of cotton balls is accompanied by the risk of accidental retention in the brain after surgery. Retained cotton balls can lead to dangerous immune responses and potential complications, such as adhesions and textilomas. In a previous study, we showed that ultrasound can be safely used to detect cotton balls in the operating area due to the distinct acoustic properties of cotton compared with the acoustic properties of surrounding tissue. In this study, we enhance the experimental setup using a 3D-printed custom depth box and a Butterfly IQ handheld ultrasound probe. Cotton balls were placed in variety of positions to evaluate size and depth detectability limits. Recorded images were then analyzed using a novel algorithm that implements recently released YOLOv4, a state-of-the-art, real-time object recognition system. As per the radiologists’ opinion, the algorithm was able to detect the cotton ball correctly 61% of the time, at approximately 32 FPS. The algorithm could accurately detect cotton balls up to 5mm in diameter, which corresponds to the size of surgical balls used by neurosurgeons, making the algorithm a promising candidate for regular intraoperative use.
Efficiency and patient safety are top priorities in any surgical operation. One effective way to achieve these objectives is automating many of the logistical and routine tasks that occur in the operating room. Inspired by smart assistant technology already commonplace in the consumer sector, we engineered the Smart Hospital Assistant (SHA), a smart, voice-controlled virtual assistant that handles natural speech recognition while executing a plurality of functions to aid surgery. Simulated surgeries showed that the SHA reduced operating time, optimized surgical staff resources, and reduced the number of major touch points that can lead to surgical site infections. The SHA not only shows its potential in the operating room, but also in other healthcare environments that may benefit from having virtual smart assistant technology.
Spinal cord injury (SCI) affects approximately 2.5 million people worldwide. The primary phase of SCI is initiated by mechanical trauma to the spinal cord, while the secondary phase involves the ensuing tissue swelling and ischemia that worsen tissue damage and functional outcome. Optimizing blood flow to the spinal cord after SCI can mitigate injury progression and improve outcome. Accurate, sensitive, real-time monitoring is critical to assessing the spinal cord perfusion status and optimizing management, particularly in those with injuries severe enough to require surgery. However, the complex anatomy of the spinal cord vasculature and surrounding structures present significant challenges to such a monitoring strategy. In this study, Doppler ultrasound was hypothesized to be a potential solution to detect and monitor spinal cord tissue perfusion in SCI patients who required spinal decompression and/or stabilization surgeries. This approach could provide real-time visual blood flow information and pulsatility of the spinal cord as biomarkers of tissue perfusion. Importantly, Doppler ultrasound could be readily integrated into the surgical workflow, because the spinal cord was exposed during surgery, thereby allowing easy access for Doppler deployment, while keeping the dura intact. Doppler ultrasound successfully measured blood flow in single and bifurcated microfluidic channels at physiologically relevant flow rates and dimensions in both in-vitro and in-vivo porcine SCI models. Furthermore, perfusion was quantified from the obtained images. Our results provide a promising and viable solution to intraoperatively assess and monitor blood flow at the SCI site to optimize tissue perfusion and improve functional recovery in SCI patients.
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