Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Developing an Artificial Intelligence (AI)-based self-training platform for laparoscopic surgery.


   School of Medicine

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr Benjie Tang  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Various research throughout the years have been devoted to exploring different and more effective ways of training for laparoscopy, within the conventional frameworks of using box trainers and virtual reality simulators. However, the incremental ameliorations within these frameworks have not been sufficient to meet the medical need of training a greater number of laparoscopy surgeons in shorter times. The laparoscopic training method with Artificial Intelligence (AI) would address the core challenges that have not been met with the current training platforms: i) wide accessibility, ii) self-training with effective and immediate feedback, iii) scalability (low cost) to be widely applied in any country (low- and middle-income countries). This project will be involved in developing a fully autonomous laparoscopy self-training system that provides insightful, intuitive, and timely feedback for improvement. We will develop machine vision and machine learning techniques and integrate those with physical box trainers. The autonomous and Intelligent Laparoscopy (AILap) Trainer with Real-Time Feedback system will be tested on commonly performed laparoscopic procedures associated with proficiency-gain/learning curve and that involves laparoscopic suturing as an unreplaceable skill (e.g., laparoscopic fundoplication, laparoscopic gastric-bypass of bariatric surgery, and laparoscopic closure of common bile duct after common bile exploration).

For informal enquiries about the project, contact Dr Benjie Tang, [Email Address Removed]

For general enquiries about the University of Dundee, contact [Email Address Removed]

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research. We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.

QUALIFICATIONS

Applicants must have obtained, or expect to obtain, a UK honours degree at 2.1 or above (or equivalent for non-UK qualifications), and/or a Masters degree in a relevant discipline. For international qualifications, please see equivalent entry requirements here: www.dundee.ac.uk/study/international/country/.

English language requirement: IELTS (Academic) overall score must be at least 6.5 (with not less than 5.5 in reading, listening, speaking and 6.0 in writing). The University of Dundee accepts a variety of equivalent qualifications and alternative ways to demonstrate language proficiency; please see full details of the University’s English language requirements here: www.dundee.ac.uk/guides/english-language-requirements.

 

APPLICATION PROCESS

Step 1: Email Dr Benjie Tang, [Email Address Removed] to (1) send a copy of your CV and (2) discuss your potential application and any practicalities (e.g. suitable start date).

Step 2: After discussion with Dr Benjie Tang, formal applications can be made via our direct application system. When applying, please follow the instructions below:

Candidates must apply for the Doctor of Philosophy (PhD) degree in Medicine (3 year) using our direct application system:

Please select the study mode (full-time/part-time) and start date agreed with the lead supervisor.

In the Research Proposal section, please:

-         Enter the lead supervisor’s name in the ‘proposed supervisor’ box

-         Enter the project title listed at the top of this page in the ‘proposed project title’ box

In the ‘personal statement’ section, please outline your suitability for the project selected.

Computer Science (8) Medicine (26)

Funding Notes

There is no funding attached to this project. The successful applicant will be expected to provide the funding for tuition fees and living expenses, via external sponsorship or self-funding.


Where will I study?