REPAIR
Recommendation for Evidence-based Preoperative AI-controlled virtual Reduction and osteosynthesis of complex fractures
AI-based recommendations for the anatomical reduction and osteosynthetic treatment of complex fractures of the extremities to support trauma surgeons
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Motivation
Complex fractures with joint involvement represent a major challenge for surgeons. If a comprehensive treatment with anatomical reduction and stable osteosynthesis is not carried out adequately, there is a risk of prolonged therapy and significantly earlier development of osteoarthritis. Among other aspects, knowledge of the correct positioning of implants is crucial for successful healing, and mistakes are often made in the wrong choice of implants or surgical techniques. In one study, for example, 31% of tibial plateau fractures were surgical failures, mainly due to inadequate reduction (84%) and inadequate implant positioning (76%). Today, there is still a lack of support tools to comprehensively and quickly plan the reduction of complex fractures and their osteosynthetic treatment preoperatively on the basis of existing guidelines and recommendations.
Innovations and perspectives
Aim of the REPAIR project is to support trauma surgeons with AI-based recommendations for the anatomical reduction and osteosynthetic treatment of complex fractures of the extremities. Through an analysis of preoperative CT datasets, we will first provide an AI-guided virtual interactive anatomic reduction recommendation for multi-fragment fractures. Through an algorithm-based analysis of current existing guidelines and scientific evidence-based guideline publications, a 3D imaging recommendation with concrete implants (plates/screws/wires) will then be given for the optimally suitable osteosynthesis procedure. In the long term, this should reduce the number of complications and achieve a better patient outcome through improved repositioning results.
