18092036. DATA VOLUME DETERMINATION FOR SURGICAL MACHINE LEARNING APPLICATIONS simplified abstract (Cilag GmbH International)

DATA VOLUME DETERMINATION FOR SURGICAL MACHINE LEARNING APPLICATIONS

Organization Name

Cilag GmbH International

Inventor(s)

Frederick E. Shelton, Iv of Hillsboro OH (US)

Shane R. Adams of Lebanon OH (US)

Kevin M. Fiebig of Cincinnati OH (US)

DATA VOLUME DETERMINATION FOR SURGICAL MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18092036 titled 'DATA VOLUME DETERMINATION FOR SURGICAL MACHINE LEARNING APPLICATIONS

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 (e.g., surgical hub)
• Surgical data sources in communication with the computing system
• Surgical device in communication with the computing system
• Processor to receive and analyze data from surgical data sources
• Training of machine learning models (e.g., neural networks) using the data
• Deployment of 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 efficiency
• Streamlined surgical procedures

Commercial Applications: The technology can be utilized in hospitals, surgical centers, and other healthcare facilities to improve surgical practices and patient care.

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 updated on advancements in machine learning applications in surgery and related technologies.

Questions about Surgical Computer-Implemented Systems: 1. How does machine learning enhance the operation of surgical devices? 2. What are the potential challenges in implementing machine learning in surgical settings?

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.