18092026. SURGICAL COMPUTING SYSTEM WITH INTERMEDIATE MODEL SUPPORT simplified abstract (Cilag GmbH International)
Contents
SURGICAL COMPUTING SYSTEM WITH INTERMEDIATE MODEL SUPPORT
Organization Name
Inventor(s)
Frederick E. Shelton, Iv of Hillsboro OH (US)
Jason L. Harris of Lebanon OH (US)
SURGICAL COMPUTING SYSTEM WITH INTERMEDIATE MODEL SUPPORT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18092026 titled 'SURGICAL COMPUTING SYSTEM WITH INTERMEDIATE MODEL SUPPORT
Simplified Explanation: The patent application describes a surgical computing device that utilizes two neural networks, one focused on procedures and the other on patients, to improve surgical outcomes.
Key Features and Innovation:
- Surgical computing device with two neural networks
- Primary neural network trained on procedure focus
- Support neural network trained on patient focus
- Input patient data, target procedure, and proposed procedure plan
- Support neural network generates patient-specific mapping
- Primary neural network outputs modified procedure plan for improved outcomes
Potential Applications: This technology could be used in various surgical procedures to enhance planning and execution, leading to better patient outcomes.
Problems Solved: This technology addresses the need for personalized surgical planning and optimization of procedure plans for improved patient outcomes.
Benefits:
- Enhanced surgical planning
- Improved patient outcomes
- Personalized procedure plans
Commercial Applications: The technology could be applied in surgical settings to optimize procedures and improve patient care, potentially leading to increased efficiency and better outcomes.
Questions about Surgical Computing Device: 1. How does the two-network approach improve surgical outcomes? 2. What are the potential limitations of using neural networks in surgical planning?
Frequently Updated Research: Stay updated on advancements in neural network technology and its applications in the healthcare industry for potential improvements in surgical procedures.
Original Abstract Submitted
A surgical computing device may include a processor configured to implement two neural networks, a primary neural network trained with a procedure focus and support neural network trained with a patient focus. Data indicative of a surgical patient, a target procedure, and a proposed procedure plan may be input to the support neural network. The support neural network may generate a patient specific mapping from this data. The patient specific mapping and the data indicative of a surgical patient, a target procedure, and a proposed procedure plan may be input to the primary neural network. The primary neural network may output a modified procedure plan that is different from the proposed procedure plan. In an example, the support neural network is trained to isolate anatomical elements. And the primary neural network is trained to identify procedure plans associated with improved patient outcomes. Such a two-network approach may facilitate the use of diverse training data sets to better refine procedure plans for a variety of uses.
