Updated: Jun 28
Deep learning is an algorithm of machine learning that is rapidly gaining traction in surgery and surgical education, especially for its powerful data-driven problem-solving.
Deep learning (DL) is a subset of machine learning (ML) based on multiple layers of artificial neural networks (ANN) to extract features and find patterns from complex, unstructured, and large volumes of data.
Machine learning ML is a subset of artificial intelligence (AI) that teaches computers to learn and make predictions based on data.
DL teaches computers to process data in a way inspired by the human brain using ANN.
Artificial neural networks (ANN) simulate human nervous systems.
Deep learning or deep neural networks use ANN to model and solve complex problems.
DL is applied in surgery to optimize preoperative planning, intraoperative performance, and postoperative predictions.
Updated: Jun 28
A Nature study found that between 2010 and 2020, publications regarding AI in the field of healthcare had grown exponentially.
The introduction of AI in surgical training has the potential to ease the current transition of surgical training to a competency-based model.
AI platforms can provide automated feedback and assessment, allowing trainees to practice on their own time and without the need for the physical presence of an expert.
Although the use of AI has been rapidly increasing in the medical field, it is still relatively new in the context of surgical education.
5 Applications of AI in Surgical Education :
1. Personalized learning
AI can be used to create personalized learning experiences for surgical trainees. The technology can assess a trainee’s strengths and weaknesses and provide targeted training modules to improve specific skills.
This approach could potentially reduce training time and improve the efficiency of surgical education.
2. Simulation-based training
AI can be used to create realistic surgical simulations that mimic real-world scenarios.
Trainees can practice surgical procedures in a virtual environment, allowing them to gain experience without putting patients at risk.
This approach can also help to reduce the cost associated with surgical training.
3. Predictive modeling
AI can be used to create predictive models that can be used to identify surgical complications before they occur.
This technology can help surgeons to take preventative measures and reduce the risk of complications during surgery.
4. Augmented reality
AI can be used to develop augmented reality tools that can provide surgeons with real-time information during surgery.
This technology can help surgeons to make more accurate decisions and improve patient outcomes.
5. Remote training
AI can be used to provide remote training to surgical trainees.
This technology can help to overcome geographical barriers and provide access to surgical training to individuals in remote locations.
AI can provide a safe and controlled environment for trainees to practice surgical skills and can analyze large datasets of surgical procedures to identify areas for improvement.
Updated: Jun 28
4 applications of machine learning (ML) in surgical robotics :
Automation of Suturing
Evaluation of Surgical Skills
Improving Surgical Robotic Materials
Surgical Workflow Modeling
Automation of suturing
In 2013, a team of researchers at the University of California at Berkeley published research on the application of an algorithm for automated suturing performed by robots.
The algorithm was tested and simulated on two robot models: the Raven II robot and the PR2 robot.
The Raven robot is designed for laparoscopic surgery, while the PR2 platform appears to be adaptable across various robotic applications.
The Berkeley research team reported an overall success rate of 87 percent of successful suturing. However, the increased complexity of the suturing scenarios tended to correspond with decreased robot accuracy.
These results are encouraging because suturing has been identified as a key factor limiting the use of laparoscopy among surgeons.
Using a pig model, the robot’s performance was compared to the work of five human surgeons in three different procedures: “open surgery, laparoscopic, and robot-assisted surgery.” Overall, the researchers reported comparable or better results to standard surgical performance.
Evaluation of surgical skills
A study presented at the 2016 World Congress on Engineering and Computer Science discussed using machine learning to evaluate surgeon performance in robot-assisted minimally invasive surgery.
The research team evaluated data collected from suturing performance and classified surgeons into two categories: novice and expert.
The machine learning algorithm was developed to measure the following six features:
Completion time
Path length
Depth perception
Speed
Smoothness
Curvature
This is a promising result, offering the possibility of more standardized evaluation methods.
Improving surgical robotic materials
In the case of neurosurgery, where particularly sensitive maneuvering is required, robots often lack the necessary dexterity to operate effectively and prevent injury.
Researchers at the University of California, San Diego (UCSD) Advanced Robotics and Controls Lab are exploring machine learning applications to improve surgical robotics, especially “continuum robots”.
Surgical workflow modeling
To improve how clinical reports are processed, a team of researchers developed a clinical information extraction system called IDEAL-X.
The manual process is frequently time-consuming and does not provide automatic user feedback on how to improve the process.
The IDEAL-X adaptive learning platform uses machine learning to understand how a user generates reports and to predict patterns to improve the speed and efficiency of the process.