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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
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 proces...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748097/ https://www.ncbi.nlm.nih.gov/pubmed/36532130 http://dx.doi.org/10.3389/fsurg.2022.1040066 |
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author | Abramson, Haley G. Curry, Eli J. Mess, Griffin Thombre, Rasika Kempski-Leadingham, Kelley M. Mistry, Shivang Somanathan, Subhiksha Roy, Laura Abu-Bonsrah, Nancy Coles, George Doloff, Joshua C. Brem, Henry Theodore, Nicholas Huang, Judy Manbachi, Amir |
author_facet | Abramson, Haley G. Curry, Eli J. Mess, Griffin Thombre, Rasika Kempski-Leadingham, Kelley M. Mistry, Shivang Somanathan, Subhiksha Roy, Laura Abu-Bonsrah, Nancy Coles, George Doloff, Joshua C. Brem, Henry Theodore, Nicholas Huang, Judy Manbachi, Amir |
author_sort | Abramson, Haley G. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9748097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97480972022-12-15 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 Abramson, Haley G. Curry, Eli J. Mess, Griffin Thombre, Rasika Kempski-Leadingham, Kelley M. Mistry, Shivang Somanathan, Subhiksha Roy, Laura Abu-Bonsrah, Nancy Coles, George Doloff, Joshua C. Brem, Henry Theodore, Nicholas Huang, Judy Manbachi, Amir Front Surg Surgery 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. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748097/ /pubmed/36532130 http://dx.doi.org/10.3389/fsurg.2022.1040066 Text en © 2022 Abramson, Curry, Mess, Thombre, Kempski-Leadingham, Mistry, Somanathan, Roy, Abu-Bonsrah, Coles, Doloff, Brem, Theodore, Huang and Manbachi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Surgery Abramson, Haley G. Curry, Eli J. Mess, Griffin Thombre, Rasika Kempski-Leadingham, Kelley M. Mistry, Shivang Somanathan, Subhiksha Roy, Laura Abu-Bonsrah, Nancy Coles, George Doloff, Joshua C. Brem, Henry Theodore, Nicholas Huang, Judy Manbachi, Amir 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Surgery |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748097/ https://www.ncbi.nlm.nih.gov/pubmed/36532130 http://dx.doi.org/10.3389/fsurg.2022.1040066 |
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