Master Thesis: Multimodal Machine Learning-Based Decision Support in Orthopedic Implant Surgery

04.06.2025, Abschlussarbeiten, Bachelor- und Masterarbeiten

Chair for Orthopaedics and Sports Orthopaedics
Institute for AI and Informatics in Medicine
Technical University of Munich

Abstract

Accurate planning and evaluation of orthopaedic procedures such as knee arthroplasty are essential for optimal surgical outcomes. Radiographs remain the most widely used and accessible imaging modality in orthopaedic diagnostics and follow-up. However, the interpretation of radiographs and the associated planning or validation steps often depend heavily on the experience of clinicians and can vary across institutions. In particular, the selection of appropriate implant size and the postoperative identification of implanted devices are tasks that require precision and consistency.

With the increasing availability of digital imaging and structured metadata in medical formats, modern machine learning algorithms offer new ways to support decision-making in clinical orthopaedics. These techniques can potentially assist clinicians in both preoperative planning and postoperative assessment while maintaining a high level of automation and reproducibility. In this project, both pre- and postoperative radiographic data will be available and shall be jointly analyzed to improve prediction and validation performance through integrated machine learning models.

Environment

The project will be conducted in close collaboration with the Department of Orthopaedics and Sports Orthopaedics at the University Hospital and the Institute for AI and Informatics in Medicine. It will be supervised within the AI in Orthopaedics (AIO) group, an interdisciplinary team of clinicians and computer scientists affiliated with both institutions.

Tasks

• Developing machine learning models for orthopedic planning assessment
• Handling real-world multimodal medical data
• Dealing with uncertainty, bias, and variability in clinical datasets
• Visualizing, presenting, and interpreting model outputs in a clinical context

Offer

• Rare medical data with high potential for publication
• Highly educated & interdisciplinary environment
• Top level hardware for scientific computing
• Constant feedback from medical and computer science experts

Prerequisites

• Advanced knowledge of deep learning with imaging data
• Beneficial but not necessary: experience in medicine, radiographs, etc.

References

• Wilhelm, N.J., et al. (2024). Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis. Artificial Intelligence in Medicine, 150, 102843.
• Rajpurkar P, Irvin J, Ball RL, et al. (2020). Deep Learning for Radiographic Detection of Osteoarthritis. Radiology.
• Tiulpin A, Klein S, Bierma-Zeinstra S, Thevenot J, Saarakkala S. (2019). Deep Learning for Predicting Knee Replacement from Radiographs. Sci Rep.

Contact: florian.hinterwimmer@tum.de

Application: Please include a short CV and a recent transcript of records with your inquiry.

Kontakt: florian.hinterwimmer@tum.de