OGAWA Shimpei    Department   School of Medicine(Tokyo Women's Medical University Hospital), School of Medicine    Position   Associate Professor
Article types	Original article
Language	English
Peer review	Non peer reviewed
Title	Automated recognition of objects and types of forceps in surgical images using deep learning.
Journal	Formal name：Scientific reports Abbreviation：Sci Rep ISSN code：20452322/20452322
Domestic / Foregin	Foregin
Volume, Issue, Page	11(1),pp.22571
Author and coauthor	BAMBA Yoshiko†, OGAWA Shimpei, ITABASHI Michio, KAMEOKA Shingo, OKAMOTO Takahiro, YAMAMOTO Masakazu
Authorship	2nd author
Publication date	2021/11
Summary	Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons. Identification of objects in videos recorded during surgery can be used for surgical skill assessment and surgical navigation. The objectives of this study were to recognize objects and types of forceps in surgical videos acquired during colorectal surgeries and evaluate detection accuracy. Images (n = 1818) were extracted from 11 surgical videos for model training, and another 500 images were extracted from 6 additional videos for validation. The following 5 types of forceps were selected for annotation: ultrasonic scalpel, grasping, clip, angled (Maryland and right-angled), and spatula. IBM Visual Insights software was used, which incorporates the most popular open-source deep-learning CNN frameworks. In total, 1039/1062 (97.8%) forceps were correctly identified among 500 test images. Calculated recall and precision values were as follows: grasping forceps, 98.1% and 98.0%; ultrasonic scalpel, 99.4% and 93.9%; clip forceps, 96.2% and 92.7%; angled forceps, 94.9% and 100%; and spatula forceps, 98.1% and 94.5%, respectively. Forceps recognition can be achieved with high accuracy using deep-learning models, providing the opportunity to evaluate how forceps are used in various operations.
DOI	10.1038/s41598-021-01911-1
PMID	34799625