Automated recognition of objects and types of forceps in surgical images using deep learning
Abstract 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 thi...
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Nature Portfolio
2021
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oai:doaj.org-article:990396cdcb9f4a97abaae6b80d4de5892021-11-21T12:17:18ZAutomated recognition of objects and types of forceps in surgical images using deep learning10.1038/s41598-021-01911-12045-2322https://doaj.org/article/990396cdcb9f4a97abaae6b80d4de5892021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01911-1https://doaj.org/toc/2045-2322Abstract 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.Yoshiko BambaShimpei OgawaMichio ItabashiShingo KameokaTakahiro OkamotoMasakazu YamamotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q Yoshiko Bamba Shimpei Ogawa Michio Itabashi Shingo Kameoka Takahiro Okamoto Masakazu Yamamoto Automated recognition of objects and types of forceps in surgical images using deep learning |
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Abstract 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. |
format |
article |
author |
Yoshiko Bamba Shimpei Ogawa Michio Itabashi Shingo Kameoka Takahiro Okamoto Masakazu Yamamoto |
author_facet |
Yoshiko Bamba Shimpei Ogawa Michio Itabashi Shingo Kameoka Takahiro Okamoto Masakazu Yamamoto |
author_sort |
Yoshiko Bamba |
title |
Automated recognition of objects and types of forceps in surgical images using deep learning |
title_short |
Automated recognition of objects and types of forceps in surgical images using deep learning |
title_full |
Automated recognition of objects and types of forceps in surgical images using deep learning |
title_fullStr |
Automated recognition of objects and types of forceps in surgical images using deep learning |
title_full_unstemmed |
Automated recognition of objects and types of forceps in surgical images using deep learning |
title_sort |
automated recognition of objects and types of forceps in surgical images using deep learning |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/990396cdcb9f4a97abaae6b80d4de589 |
work_keys_str_mv |
AT yoshikobamba automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning AT shimpeiogawa automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning AT michioitabashi automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning AT shingokameoka automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning AT takahirookamoto automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning AT masakazuyamamoto automatedrecognitionofobjectsandtypesofforcepsinsurgicalimagesusingdeeplearning |
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