Automatic detection of foreign body objects in neurosurgery using a deep learning approach on intraoperative ultrasound images: From animal models to first in-human testing

Haley G Abramson; Eli J Curry; Griffin Mess; Rasika Thombre; Kelley M Kempski-Leadingham; Shivang Mistry; Subhiksha Somanathan; Laura Roy; Nancy Abu-Bonsrah; George Coles; Joshua C Doloff; Henry Brem; Nicholas Theodore; Judy Huang; Amir Manbachi

doi:10.3389/fsurg.2022.1040066

Automatic detection of foreign body objects in neurosurgery using a deep learning approach on intraoperative ultrasound images: From animal models to first in-human testing

Front Surg. 2022 Nov 30:9:1040066. doi: 10.3389/fsurg.2022.1040066. eCollection 2022.

Authors

Haley G Abramson¹, Eli J Curry², Griffin Mess³, Rasika Thombre³, Kelley M Kempski-Leadingham², Shivang Mistry⁴, Subhiksha Somanathan³, Laura Roy⁵, Nancy Abu-Bonsrah², George Coles⁶, Joshua C Doloff^{1

7}, Henry Brem^{1

2}, Nicholas Theodore^{1

2}, Judy Huang^{1

2}, Amir Manbachi^{1

2

8

9

10}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
² Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
⁴ Department of Engineering Science, University of Toronto, Toronto, ON, Canada.
⁵ Roy Illustration, Des Moines, IA, United States.
⁶ Applied Physics Lab, Johns Hopkins University, Laurel, MD, United States.
⁷ Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, United States.
⁸ Department of Electrical Engineering and Computer Science, Johns Hopkins University, Baltimore, MD, United States.
⁹ Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, United States.
¹⁰ Department of Anesthesiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Abstract

Objects accidentally left behind in the brain following neurosurgical procedures may lead to life-threatening health complications and invasive reoperation. One of the most commonly retained surgical items is the cotton ball, which absorbs blood to clear the surgeon's field of view yet in the process becomes visually indistinguishable from the brain parenchyma. However, using ultrasound imaging, the different acoustic properties of cotton and brain tissue result in two discernible materials. In this study, we created a fully automated foreign body object tracking algorithm that integrates into the clinical workflow to detect and localize retained cotton balls in the brain. This deep learning algorithm uses a custom convolutional neural network and achieves 99% accuracy, sensitivity, and specificity, and surpasses other comparable algorithms. Furthermore, the trained algorithm was implemented into web and smartphone applications with the ability to detect one cotton ball in an uploaded ultrasound image in under half of a second. This study also highlights the first use of a foreign body object detection algorithm using real in-human datasets, showing its ability to prevent accidental foreign body retention in a translational setting.

Keywords: computer vision; deep learning; foreign body object detection; neurosurgery; ultrasound.