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.
Cotton balls are used in neurosurgical procedures to assist with hemostasis and improve vision within the operative field. Although the surgeon can reshape pieces of cotton for multiple intraoperative uses, this customizability and scale also places them at perpetual risk of being lost, as blood-soaked cotton balls are visually similar to raw brain tissue. Retained surgical cotton can induce potentially life-threatening immunologic responses, impair postoperative imaging, lead to a textiloma or misdiagnosis, and/or require reoperation. This study investigated three imaging modalities (optical, acoustic, and radiographic) to find the most effective method of identifying foreign bodies during neurosurgery. First, we examined the use of dyes to increase contrast between cotton and surrounding parenchyma (optical approach). Second, we explored the ability to distinguish surgical cotton on or below the tissue surface from brain parenchyma using ultrasound imaging (acoustic approach). Lastly, we analyzed the ability of radiography to differentiate between brain parenchyma and cotton. Our preliminary testing demonstrated that dark-colored cotton is significantly more identifiable than white cotton on the surface level. Additional testing revealed that cotton has noticeable different acoustic characteristics (eg, speed of sound, absorption) from neural tissue, allowing for enhanced contrast in applied ultrasound imaging. Radiography, however, did not present sufficient contrast, demanding further examination. These solutions have the potential to significantly reduce the possibility of intraoperative cotton retention both on and below the surface of the brain, while still providing surgeons with traditional cotton material properties without affecting the surgical workflow.
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