Publication: Real-time tool detection for workflow identification in open cranial vault remodeling
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2021-07
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MDPI
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Abstract
Deep learning is a recent technology that has shown excellent capabilities for recognition
and identification tasks. This study applies these techniques in open cranial vault remodeling
surgeries performed to correct craniosynostosis. The objective was to automatically recognize
surgical tools in real-time and estimate the surgical phase based on those predictions. For this
purpose, we implemented, trained, and tested three algorithms based on previously proposed
Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one
new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically
developed to implement these networks and recognize surgical tools in real time via video streaming.
The training and test data were acquired during a surgical simulation using a 3D printed patientbased realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest
accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2
model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding
phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas
MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the
feasibility of applying deep learning architectures for real-time tool detection and phase estimation
in craniosynostosis surgeries.
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Keywords
Artificial intelligence, Craniosynostosis surgery, Deep learning, Phase estimation, Tool detection
Bibliographic citation
Pose Díez De La Lastra, A., García-Duarte Sáenz, L., García-Mato, D., Hernández-Álvarez, L., Ochandiano, S., & Pascau, J. (2021). Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling. Entropy, 23(7), 817.