Benchmarking Expert Trainer Surgeons’ Path for Evaluating a Trainee Surgeons’ Performance

Malik Anas Ahmad

School of Electrical Engineering & Computer Science, National University of Sciences & Technology, Pakistan 1

Malik Anas Ahmad1, Zohaib Amjad,2 , Shamyl Bin Mansoor3 , Shahroze Humayun Kabir4

Affiliation of Authors: 1. Research Assistant , SEECS-NUST 2. Software Team Lead, SEECS-NUST 3. Assistant Professor, SEECS-NUST 4. BEE Student, SEECS-NUST

Introduction• Computer Simulator[1]

– A software program• Model a real-life situation on a computer• To study to see how the system behaves

– Interactive» Visualization» Practise

– Non-interactive» Visualization

1. http://en.wikipedia.org/wiki/Computer_simulator2. http://www.vision.ee.ethz.ch/research/projects_medical.cgi

Courtesy CV-Lab, Department of IT & EE, ETH, Zurich, Switzerland[2]

Introduction– Surgical Computer Simulator[3]

• Virtual reality simulation– surgical procedures

– Laparoscopic Surgery Simulator• Train the surgeon

– Hand eye coordination– Working in the confined spaces….etc

• Advantages– Independent learning– Practice a surgical operations

• Multiple times• Without the use of cadavers or animals• Evaluation of a surgeon• Recreate rare pathological cases• Simulate the interaction with several organs• Complications can be introduced during the surgery testing the user on real world scenarios.

– Virtually trained students• More proficient• Make fewer errors• Better prepared to assist during surgery

3. http://en.wikipedia.org/wiki/Virtual_surgery

Introduction•Independent learning

–Advantage blunted [4]

•Without useful instructional feedback•Instructor will have to supervise the trainee

–Continued need for instructor feedback with most existing simulators»A primary reason for the reluctance of many medical schools

•Efficient Simulators–Incorporate relevant and intuitive metrics

•Constructive feedback–Facilitate independent learning

4. Christopher Sewell: automatic performance evaluation in surgical simulation. PhD Desertion submitted to DOC of Stanford University, March 2007

Challenge•Evaluating

–Trainee surgeon’s performance–Trainer surgeon’s desire–Minimal invasive surgery simulators

•A novel metric is proposed–Performance evaluation–Machine learning algorithms

Methodology•Basis

–Train•Trainee surgeons•As per expert surgeons’ desire

•Expert trainer surgeons’ Instrument paths–(In a specific type of simulation exercise)

•Optimal paths

–Recorded/Saved

•Train a machine learning algorithm–Artificial Neural Network

Methodology•Performance judgement

–How good he followed the expert trainer surgeons’ paths•Trainee surgeons’ Instrument coordinates•Next optimal move coordinates•Deviation from the expert trainer surgeons’ paths is penalized

–Euclidian distance»in between desired position (i.e. expert trainer surgeons’ paths)»and trainee surgeon’s instrument position

Findings• Based on our proposal

– Multiple trainer surgeons can teach the art of surgery • as per their desire• to multiple students• at a same time• in a more convenient way.

Discussion•Problem

–Too much dictation•Discouragement

–Improvised learning of a trainee surgeon

•Proposed Solution–Allowance or less penalization

•In a way predicted by •Genetic algorithms

–A machine learning algorithm–Support evolution

»New possible positions of the instrument

Discussion•Future of surgery

–Non-invasive–Robotic surgery

•We first need to train–Equipment/device

•Autonomous•Semi-autonomous

–A human surgeon•Gets trained

–Practising and benchmarking his Trainer Surgeon’s work–Along with some improvisation

–Our work is just imposing this effort•In a more automated way

–Same efforts can be evolved into•Training a robot for

–Autonomous –Semi-autonomous surgery–Other medical procedures.

Thank you