Using Deep Learning for Exploration and Recognition of Objects Based on Images

Abstract Background: 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.Methods: 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.Results: 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.Conclusions: Forceps recognition can be achieved with high accuracy using deep-learning models, providing the opportunity to evaluate how forceps are used in various operations.

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Deep Learning-based Estimation of Observation Error Area and Recognition of Objects hidden by Obstacles for Growth Information Collection in Smart farms

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2021.21.0147 ◽

2021 ◽

Vol 27 (12) ◽

pp. 984-990

Author(s):

Jin-Seong Park ◽

Sang-Cheol Kim

Keyword(s):

Deep Learning ◽

Information Collection ◽

Observation Error ◽

Recognition Of Objects

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Recognition of Objects in the Urban Environment using R-CNN and YOLO Deep Learning Algorithms

2020 9th Mediterranean Conference on Embedded Computing (MECO) ◽

10.1109/meco49872.2020.9134080 ◽

2020 ◽

Author(s):

Rijad Saric ◽

Markus Ulbricht ◽

Milos Krstic ◽

Jasmin Kevric ◽

Dejan Jokic

Keyword(s):

Deep Learning ◽

Urban Environment ◽

Learning Algorithms ◽

Recognition Of Objects

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Automated recognition of objects and types of forceps in surgical images using deep learning

Scientific Reports ◽

10.1038/s41598-021-01911-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yoshiko Bamba ◽

Shimpei Ogawa ◽

Michio Itabashi ◽

Shingo Kameoka ◽

Takahiro Okamoto ◽

...

Keyword(s):

Deep Learning ◽

Surgical Navigation ◽

Skill Assessment ◽

Detection Accuracy ◽

Surgical Skill Assessment ◽

Ultrasonic Scalpel ◽

Automated Recognition ◽

Recognition Of Objects ◽

Model Training ◽

Surgical Videos

AbstractAnalysis 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.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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