In the last two years we have begun to see the first successful applications of AI in healthcare that aid physicians with their daily tasks. Currently AI has surpassed expert radiologists in detecting breast tumors in mammography scans and eye diseases from optical topography scans (Fauw et al., 2018; McKinney et al., 2020). However, the field of orthopaedics has been lagging on adopting AI.
In this context, we have started research to learn how we can use AI to help orthopaedic surgeons in their daily practice, starting with the most commonly performed procedure: total knee arthroplasty (TKA).
Consequently, we have created an algorithm that is able to learn from the surgeon’s previously planned cases. Our goal with this study was to evaluate if the AI model is indeed capable of proposing improved preoperative plans compared to the current defaults.
In this webinar you will learn:
The basics of preoperative planning for total knee arthroplasty (TKA)
Variations and challenges in preoperative planning
Major clinical takeaways from the study
How AI can help to improve preoperative planning
Other areas of exploration that this study will include as it evolves
Adriaan Lambrechts is a Research Engineer at Materialise specialized in artificial intelligence for new medical applications.
His field of expertise investigates state-of-the-art machine learning algorithms for improving preoperative planning, as well as automating medical image-centric workflows.
He is a recurrent guest speaker about AI-based preoperative planning in several orthopaedic conferences.