Santa Clara, CA, United States of America

Amer Ghanem

USPTO Granted Patents = 1 

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):

Title: Innovations by Amer Ghanem in Surgical Technology

Introduction

Amer Ghanem is an innovative inventor based in Santa Clara, California. He has made significant contributions to the field of surgical technology, particularly in enhancing patient safety during surgical procedures. His work focuses on the integration of machine learning with surgical practices, aiming to improve the accuracy and reliability of surgical tool detection.

Latest Patents

Amer Ghanem holds a patent for a groundbreaking invention titled "Real-time surgical tool presence/absence detection in surgical videos." This patent describes various techniques and systems for building machine-learning models that can process surgical videos. The invention predicts whether a surgical tool is present or absent in each frame of a surgical video. The process begins by receiving a real-time control signal indicating the operating state of an energy tool during surgery. It then receives real-time endoscope video images and applies a machine-learning model to generate real-time decisions regarding the tool's location. This innovative approach helps identify unsafe events during surgery and enables timely interventions to ensure patient safety.

Career Highlights

Amer Ghanem is currently employed at Verb Surgical Inc., where he continues to develop advanced technologies for surgical applications. His work at Verb Surgical Inc. reflects his commitment to improving surgical outcomes through innovative solutions.

Collaborations

Some of his notable coworkers include Meysam Torabi and Varun Kejriwal Goel, who collaborate with him on various projects aimed at enhancing surgical technology.

Conclusion

Amer Ghanem's contributions to surgical technology through his innovative patent demonstrate his dedication to improving patient safety and surgical practices. His work exemplifies the potential of machine learning in transforming healthcare.

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