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
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 30, 2022
- Source ID
- 10.3389/fsurg.2022.1040066
Entities
People
- Amir Manbachi
- Eli J Curry
- George Coles
- Griffin Mess
- Haley G. Abramson
- Henry Brem
- Joshua C. Doloff
- Judy Huang
- Kelley M. Kempski-leadingham
- Laura Roy
- Nancy Abu-bonsrah
- Nicholas Theodore
- Rasika Thombre
- Shivang Mistry
- Subhiksha Somanathan
Organizations
- Defense Advanced Research Projects Agency
- National Institutes of Health
- National Science Foundation