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❯Autonomous Robot Improves Surgical Precision Using AI
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How Gesture Recognition authentication uncovered mortgage licensing fraud.
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Deaf and hard of hearing (DHH), and non-native speaking academic students.
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Captioning lecturer's speech to improve the learning experience of Deaf and Hard of Hearing students.
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Chromosome segmentation and deep classification
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Karyotyping of chromosomes is restricted to healthy patients.
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Realizing the value of historical sporting digital media assets by using Artificial Intelligence (AI) to assist in doing the work of 15 interns.
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Empowering course creators to focus on complex decision-making and creativity with Computer Vision and Natural Language Processing.
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Freight and passenger trains operate autonomously, excluding any crew
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AI sign language interpretation system for people with hearing impairment
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Increase the convenience of public services to people with hearing impairment
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Detecting image anomalies.
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Social humanoid technology capable of multi-modal context recognition and expression
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Autonomous Robot Improves Surgical Precision Using AI
For:
Hospitals using Autonomous robotic surgery via the STAR system
Goal:
Improve Operation Efficiency
Autonomous Robot Improves Surgical Precision Using AI
For:
Hospitals using Autonomous robotic surgery via the STAR system
Goal:
Improve Operation EfficiencyProblem addressed
Performing laparoscopic operations without the guidance of a surgeon’s hand.
Anastomosis—which involves connecting two tubular structures such as blood vessels or intestines—is often performed laparoscopically and is one of the most intricate and delicate tasks in surgery to automate: the reconnection of two ends of an intestine.
Despite being minimally invasive, the surgery has the potential for serious complications to the patient if any leakage occurs due to flawed suturing.
Because the patient is breathing, the two ends of the intestine tend to move. When this movement is larger than 3mm, and the autonomous robot can not adjust the suturing plan, leakage and imperfect suturing can occur.
Description
Researchers developed machine learning, computer vision, and advanced control techniques to track the target tissue movement in response to patient breathing, detect the tissue deformations between different suturing steps, and operate the robot under motion constraints.
They trained CNNs using 9,294 examples of motion profiles from anastomosis procedures, to learn tissue motion based on breathing patterns and other tissue motion during surgery.
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Image
AI: Perceive
Computer Vision
Deep Learning
AI: Understand
Automate Process
Robotics
AI: Act