18092038. ADAPTIVE SURGICAL DATA THROTTLE simplified abstract (Cilag GmbH International)
Contents
ADAPTIVE SURGICAL DATA THROTTLE
Organization Name
Inventor(s)
Frederick E. Shelton, Iv of Hillsboro OH (US)
Aaron Chow of Cincinnati OH (US)
David C. Yates of Marrow OH (US)
Kevin M. Fiebig of Cincinnati OH (US)
Shane R. Adams of Lebanon OH (US)
ADAPTIVE SURGICAL DATA THROTTLE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18092038 titled 'ADAPTIVE SURGICAL DATA THROTTLE
Simplified Explanation: The patent application describes a surgical computer-implemented system that utilizes machine learning models to affect the operation of a surgical device.
Key Features and Innovation:
- Surgical computing system (surgical hub)
- Surgical data sources in communication with the system
- Surgical device in communication with the system
- Processor to receive and analyze data from surgical data sources
- Training a machine learning model (e.g., neural network) with the data
- Deploying the machine learning model to impact the operation of the surgical device
Potential Applications: The technology can be applied in various surgical procedures to enhance precision and efficiency.
Problems Solved: This technology addresses the need for advanced tools in surgical settings to improve outcomes and streamline processes.
Benefits:
- Improved surgical outcomes
- Enhanced precision and accuracy
- Increased efficiency in surgical procedures
Commercial Applications: The technology can be utilized in hospitals, surgical centers, and other medical facilities to optimize surgical processes and outcomes.
Prior Art: Readers can explore existing patents and research related to surgical computer-implemented systems and machine learning in surgical settings.
Frequently Updated Research: Stay informed about the latest advancements in machine learning applications in surgery for continuous improvement in surgical practices.
Questions about the Technology: 1. What are the potential limitations of using machine learning models in surgical devices? 2. How can this technology impact the future of surgical procedures and patient care?
Original Abstract Submitted
A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). The ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.
