Scientific Research Project Number: MA 2021 03
Place: Amsterdam UMC, afdeling Chirurgische Specialismen - Orthopedie
Osteochondral defects (OCD) in the ankle are pathologic lesions of the cartilage and its subchondral bone. These lesions can occur in up to 70% of acute ankle fractures and sprains2,3. Currently, evidence-based treatment of these defects is based on low-quality small-numbered clinical case series, making it impossible to individualize patient care at a high level1,4. We recently built a database including >1500 patients with such an OCD in the ankle and filled this with CT-scan images of the patients with an OCDs. In order to answer clinically and radiologically relevant daily questions from the field, it is our goal to apply highly sophisticated innovative algorithms to improve and individualize patient care in an evidence-based manner through a big CT-Driven database for ankle OCDs.
You will be working in a very enthusiastic team consisting of two experienced orthopedic surgeons (Prof. Kerkhoffs and Dr. Stufkens) and two PhD candidates. The team consists of members of the radiological department as well as these are our collaborative partners that we work closely together with. The team has a high-quality background in clinical and radiological research with a high output of publications over the past 5 to 10 years.
This is a fruitful collaboration between the Department of Orthopaedic Surgery of the Amsterdam UMC and the Department of Medical Informatics at the University of Amsterdam. Your essential role in our team will be to be to help develop and fill our CT-Driven database through an important role how to best design the content and build-up of the database from your background. For your internship specifically, you will answer clinically relevant questions that are yet unanswered through the application of sophisticated algorithms based on artificial intelligence procedures. Through your dedicated contribution, you will be able to help our future patients!
Predictive Modelling: Which Treatment is Best for Our Individual Patient?
The project is on Predictive Modelling. Our current database exists of more than 1500 patients, and currently, there is no golden evidence-based and big-data-driven answer to the question “Which Treatment Works Best for You?”. At the outpatient department, we are able to present the patients with general success rates of a specific treatment option for the patient in question, but the goal is to make this shared-decision making process individualized and tailor-made to each patient that comes to our clinic, providing the patient with success rates and long-term sports/work outcomes based on his or her own individual characteristics. Through the application of Machine Learning, we wish to build a code in collaboration with you in order to accurately predict outcomes of different treatment strategies for patients with cartilage damage in the ankle based on the evidence currently available from our Database as well as the present prognostic factors for each and every patient. You will design an accurate and reliable code based on already extracted parameters that are essential prognostic factors for the outcome of treatment so that the model can be trained, validated, tested and (most importantly) applied thereafter.
- Strong analytical and quantitative skills and strong team-working spirit
- Strong interest in artificial intelligence (machine learning, natural-language processing, etc.)
- Programming experience in R/Python/etc.
- High interest of a student to answer daily clinical questions at our Orthopaedic Department with high-quality database research
If you are interested in this project and you wish to apply for a position in our multi-disciplinary clinical research team, please send an e-mail to Dr. Sjoerd Stufkens and Jari Dahmen (email@example.com & firstname.lastname@example.org) including a brief motivation letter and a CV.
1. Dahmen J, Lambers KTA, Reilingh ML, van Bergen CJA, Stufkens SAS, Kerkhoffs G. No superior treatment for primary osteochondral defects of the talus. Knee Surg Sports Traumatol Arthrosc 2018;26:2142-2157.
2. Hintermann B, Regazzoni P, Lampert C, Stutz G, Gachter A. Arthroscopic findings in acute fractures of the ankle. J Bone Joint Surg Br 2000;82:345-351.
3. Martijn HA, Lambers KTA, Dahmen J, Stufkens SAS, Kerkhoffs G. High incidence of (osteo)chondral lesions in ankle fractures. Knee Surg Sports Traumatol Arthrosc 2020;Aug 6; [Epub ahead of print] PMID: 32761358.
4. Rikken QGH, Kerkhoffs GMMJ. Osteochondral Lesions of the Talus: An Individualized Treatment Paradigm from the Amsterdam Perspective. Foot and Ankle Clinics 2020;in press, available online 11 December, 2020. doi:10.1016/j.fcl.2020.10.002.
Dr. Sjoerd Stufkens , Amsterdam UMC, afdeling Chirurgische Specialismen - Orthopedie , email@example.com
Jari Dahmen , Amsterdam UMC, afdeling Chirurgische Specialismen - Orthopedie , firstname.lastname@example.org