| Literature DB >> 35233128 |
Smruti Mahapatra1, Manish Balamurugan2, Kathryn Chung2, Venkat Kuppoor3, Eli Curry1, Fariba Aghabaglau1, Tarana Parvez Kaovasia4, Molly Acord4, Ana Ainechi1, Jeong Hun Kim1,5, Yohannes Tshey1, Christina Diana Ghinda1, Jennifer K Son6, Aliaksei Pustavoitau7, Betty Tyler1, Shenandoah D Robinson1, Nicholas Theodore1,4, Henry Brem1,4, Judy Huang1, Amir Manbachi1,4.
Abstract
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.Entities:
Keywords: Deep learning; neuroimaging; object detection; recognition system; retained foreign object; ultrasound
Year: 2021 PMID: 35233128 PMCID: PMC8883358 DOI: 10.1117/12.2580887
Source DB: PubMed Journal: Proc SPIE Int Soc Opt Eng ISSN: 0277-786X