About this Research Topic
As reported by the World Health Organization (WHO), unsafe surgical care has emerged as a significant public health concern, motivated by a high percentage of major complications happening during surgery that are determined to be preventable.
Adverse events are often linked to the experience and skills of the surgeons, their capability to integrate pre-operative data within the intra-operative scenario, ability to be aware about the patient anatomical peculiarities and surgical site situation, and reactivity in the decision process. Even if the advent of Robot-assisted Surgery has brought enormous advantages in terms of precision, ergonomics and immersive visualization, the core of the surgery still relies on the surgeons’ degree of expertise and experience, making the outcome of the surgery vary according to the surgeons’ skills.
Surgical robotics is reaching an unprecedented technological level in most of its facets. One of the next challenges is the integration of the concept of surgical awareness attributed to both the robot and the surgeon in the operating room.
Recent advances including Computer Vision, Machine-Deep Learning, Artificial Intelligence and Smart Sensors have been explored to retrieve context and situation awareness from the surgical field. Empowering the surgeon with such information will lead to personalized supervisory and decision-support systems capable of enhancing and improving their capabilities at different levels of expertise.
The same information can be also exploited to increase shared autonomy and fluent human-robot interaction in the operating room (OR) while improving the robot’s awareness of the surgical procedure, the OR, and the surgeons’ workflow, and laying the foundations for the surgical autonomous systems of the future.
Awareness and Automation in surgery will provide the ability to reach advanced safety and quality standards through cutting-edge control for specific procedures.
Awareness in robotic surgery is an emerging topic, which has been barely explored to its full potential. To this end, this Research Topic aims to explore the main research fields required for the future development of awareness in robotic surgery.
Topic of interest (but not limited to):
• Machine learning and Deep Learning for surgical vision and perception
• Cognitive architecture for surgical systems
• Decision and supervisory system as surgical assistant
• Intra-operative imaging for robotic surgery
• Workflow analysis, action recognition and episode segmentation
• Context awareness on operating room
• New materials, sensors and actuators
• Surgical robot action, perception and control
• Registration, segmentation, modelling, and data mining
• Surgical navigation and augmented reality systems
• Motion compensation and active guidance
• Human-robot interaction, ergonomics and shared control
• Multimodal data (video, sensor, kinematics, etc.) analysis for robotic surgery
• Emerging, multi-specialty applications of robotic technology
Adverse events are often linked to the experience and skills of the surgeons, their capability to integrate pre-operative data within the intra-operative scenario, ability to be aware about the patient anatomical peculiarities and surgical site situation, and reactivity in the decision process. Even if the advent of Robot-assisted Surgery has brought enormous advantages in terms of precision, ergonomics and immersive visualization, the core of the surgery still relies on the surgeons’ degree of expertise and experience, making the outcome of the surgery vary according to the surgeons’ skills.
Surgical robotics is reaching an unprecedented technological level in most of its facets. One of the next challenges is the integration of the concept of surgical awareness attributed to both the robot and the surgeon in the operating room.
Recent advances including Computer Vision, Machine-Deep Learning, Artificial Intelligence and Smart Sensors have been explored to retrieve context and situation awareness from the surgical field. Empowering the surgeon with such information will lead to personalized supervisory and decision-support systems capable of enhancing and improving their capabilities at different levels of expertise.
The same information can be also exploited to increase shared autonomy and fluent human-robot interaction in the operating room (OR) while improving the robot’s awareness of the surgical procedure, the OR, and the surgeons’ workflow, and laying the foundations for the surgical autonomous systems of the future.
Awareness and Automation in surgery will provide the ability to reach advanced safety and quality standards through cutting-edge control for specific procedures.
Awareness in robotic surgery is an emerging topic, which has been barely explored to its full potential. To this end, this Research Topic aims to explore the main research fields required for the future development of awareness in robotic surgery.
Topic of interest (but not limited to):
• Machine learning and Deep Learning for surgical vision and perception
• Cognitive architecture for surgical systems
• Decision and supervisory system as surgical assistant
• Intra-operative imaging for robotic surgery
• Workflow analysis, action recognition and episode segmentation
• Context awareness on operating room
• New materials, sensors and actuators
• Surgical robot action, perception and control
• Registration, segmentation, modelling, and data mining
• Surgical navigation and augmented reality systems
• Motion compensation and active guidance
• Human-robot interaction, ergonomics and shared control
• Multimodal data (video, sensor, kinematics, etc.) analysis for robotic surgery
• Emerging, multi-specialty applications of robotic technology
Keywords: Cognitive architectures, Machine and Deep Learning for surgical vision and perception, Surgical Robot Action and Control, Autonomous Surgery
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
