This technology uses movement data from laparoscopic instruments to detect stress levels in surgeons in near real-time. An AI model analyzes the data, identifying movement patterns associated with stress, and can be integrated into robotic surgical platforms for feedback and guidance.
Background
Laparoscopic surgery is a complex and demanding field that requires surgeons to perform intricate tasks with precision and accuracy. The inherent limitations of laparoscopic procedures, such as reduced visualization, limited workspace, and the need for advanced hand-eye coordination, contribute to a high-stress environment for surgeons.
Excessive stress can negatively impact surgical performance, potentially leading to errors, reduced efficiency, and compromised patient safety. Therefore, there is a critical need for technology that can effectively detect and mitigate surgeon stress during laparoscopic procedures.
Current approaches to stress detection primarily rely on physiological sensors, such as heart rate monitors or skin conductance sensors. These methods, while providing direct measurements of stress responses, often require additional equipment and can be invasive or cumbersome for surgeons to wear during procedures. Moreover, these techniques typically provide delayed feedback, making it difficult to address stress in real-time.
Self-report questionnaires, another common method, are subjective and may not accurately reflect the surgeon’s true stress level. Consequently, there is a pressing need for a non-invasive, real-time stress detection system that can seamlessly integrate into the surgical workflow and provide immediate feedback to surgeons.
Technology description
This technology involves a system for detecting stress levels in surgeons using kinematic data obtained from laparoscopic instrument movements. The system utilizes an attention-based Long Short-Term Memory (LSTM) recurrent neural network to classify movements as either normal or stressed.
It processes kinematic data, such as velocity, acceleration, and jerk, to identify specific movement patterns associated with stress. An attention mechanism within the LSTM model highlights the importance of each time step in the data sequence, allowing the system to focus on movements most indicative of stress.
The model is trained using backpropagation to minimize prediction errors, iteratively adjusting weights and biases. The system can operate in near real-time, providing a non-invasive method to monitor surgeon stress without requiring additional physiological sensors.
This technology is differentiated by its use of kinematic data and an attention-based LSTM network for real-time stress detection. Unlike traditional methods relying on physiological sensors, this approach is non-invasive and avoids the need for additional equipment.
The attention mechanism within the LSTM model allows the system to focus on specific movement patterns that are most indicative of stress, improving the accuracy and efficiency of detection. This real-time capability enables the integration of the system with robotic-assisted surgical platforms to provide feedback or guidance to surgeons, potentially mitigating stress-related impacts on surgical performance.
Benefits
- Non-invasive stress detection: The system uses kinematic data from laparoscopic instrument movements, eliminating the need for additional physiological sensors.
- Real-time monitoring: The system operates in near real-time, providing continuous feedback on surgeon stress levels.
- Improved surgical performance: By identifying and mitigating stress, the system could potentially improve surgical outcomes.
- Integration with robotic surgery: The technology can be integrated with robotic-assisted surgical platforms to offer real-time feedback and guidance to surgeons.
- Objective stress assessment: The system provides an objective measure of stress, unlike subjective self-reporting methods.
Commercial applications
- Surgical training evaluation
- Robotic surgery assistance
- Stress management training
- Performance optimization tools
Links
https://patents.google.com/patent/US20240289626A1/en?oq=+18%2f436%2c313
https://link.springer.com/article/10.1007/s11548-022-02568-5
