18092025. METHOD FOR ADVANCED ALGORITHM SUPPORT simplified abstract (Cilag GmbH International)

METHOD FOR ADVANCED ALGORITHM SUPPORT

Organization Name

Cilag GmbH International

Inventor(s)

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

Aaron Chow of Cincinnati OH (US)

Kevin M. Fiebig of Cincinnati OH (US)

Shane R. Adams of Lebanon OH (US)

David C. Yates of Marrow OH (US)

Jason L. Harris of Lebanon OH (US)

Taylor W. Aronhalt of Loveland OH (US)

Jacqueline Corrigan Aronhalt of Loveland OH (US)

METHOD FOR ADVANCED ALGORITHM SUPPORT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18092025 titled 'METHOD FOR ADVANCED ALGORITHM SUPPORT

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 model (e.g., neural network) using the data
• Deployment of the machine learning model to impact the operation of the surgical device

Potential Applications: This technology could be used in various surgical procedures to enhance precision and efficiency.

Problems Solved: This technology addresses the need for advanced systems that can improve surgical outcomes through the use of machine learning.

Benefits:

• Improved surgical precision
• Enhanced efficiency in surgical procedures
• Potential for better patient outcomes

Commercial Applications: Potential commercial uses include medical device companies, hospitals, and surgical centers looking to adopt advanced technologies for better surgical outcomes.

Prior Art: Readers can start searching for prior art related to this technology in the field of surgical robotics, artificial intelligence in healthcare, and medical device innovation.

Frequently Updated Research: Stay updated on advancements in machine learning applications in surgery and the integration of AI in healthcare for improved patient care.

Questions about Surgical Computer-Implemented Surgical System: 1. How does the use of machine learning models impact the operation of surgical devices? 2. What are the potential long-term implications of integrating machine learning into surgical systems?

1. A relevant generic question not answered by the article, with a detailed answer: How does the surgical computing system communicate with the surgical data sources and the surgical device? The surgical computing system communicates with the surgical data sources and the surgical device through established communication protocols and interfaces, allowing for seamless data transfer and control of the surgical device based on the analyzed data.

2. Another relevant generic question, with a detailed answer: What are the key advantages of using machine learning models in surgical procedures? Machine learning models offer the potential for enhanced precision, real-time decision-making capabilities, and personalized treatment plans based on data analysis, ultimately leading to improved surgical outcomes 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). 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.