Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling

Entropy

Alicia Pose Díez de la Lastra¹  Lucía García-Duarte Sáenz¹  David García-Mato¹  Javier Pascau¹  Luis Hernández-Álvarez²  Santiago Ochandiano³ 

[1] Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Leganés, Spain;Departamento de Tecnologías de la Información y las Comunicaciones (TIC), Instituto de Tecnologías Físicas y de la Información (ITEFI), Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain;Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;

关键词: Artificial Intelligence;    deep learning;    craniosynostosis surgery;    phase estimation;    tool detection;   

DOI  :  10.3390/e23070817

来源: DOAJ

【 摘 要 】

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 patient-based 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.

【 授权许可】

Unknown