Previsão de resultados clínicos da reconstrução do ligamento cruzado anterior através da simulação in silico conjugada com a inteligência artificial

To predict ACLR outcomes is challenging and machine learning has the potential to improve our predictive capability. The project represents an innovative and novel approach to estimate subjective failure risk at a patient-specific level when discussing outcome expectations preoperatively, as well as enhancing surgical decision-making and optimizing patient care in orthopaedic surgery. By leveraging AI technologies and computational modelling, the project aims to improve surgery efficiency and personalize ACLR procedures for benefiting patients and advancing the field of orthopaedic surgery in the inevitable AI environment that cannot be ignored. The objectives are in line with the questions previously presented. However, by introducing in silico simulations it is possible to complement and widen the objectives of the project. Therefore, the complementary objectives include those that will result from the development and implementation of AI in ACLR. The AI algorithms/models and finite element models will enhance ACLR management. The tool as result of the AI-driven finite element analysis can be used to analyse patient-specific characteristics, injury severity, and other relevant biomechanical factors (graft selection, tunnel orientation, tibia and femur fixation devices) and pre-surgery conditions for surgery (BTB, 4 ST/G, All-inside, other) decision-making and surgical planning to improve post-operative outcomes to minimise the risk of complications. The complementary objectives are: - Improve patient outcomes and long-term prognosis; - Refine surgical techniques and personalize rehabilitation protocols; - Reduce complications and failures by identifying potential risk factors of ACLR procedures; - Build a platform for ongoing research and innovation of ACLR by enabling large-scale data analysis and modelling with AI techniques; - Gain deeper insight into the biomechanical factors influencing ACLR outcomes through AI-driven simulations; - Contribute to the advancement of knowledge in ACLR orthopaedic surgery. Due to the nature of the project, ethics, patient privacy, and algorithmic transparency will be considered to ensure the responsible and beneficial application of AI-driven clinical data and silico analysis in clinical practice. In a future perspective, the objective is to integrate AI tools into clinical practice to streamline workflow processes related to ACLR, such as preoperative planning, postoperative monitoring, and outcome assessment, as well as to foster collaboration among researchers, health professionals, and other stakeholders to leverage collective expertise and data resources for continuous improvement in ACLR treatment strategies. To enhance post-operative management and rehabilitation it is necessary to monitor patient progress, predict recovery trajectories, and identify early warning signs of complications to facilitate timely interventions. Ultimately, improve patient outcomes is an objective to be pursued to improve the quality of life and patient satisfaction following ACLR surgery. The contribution to scientific knowledge and advancements in the field of orthopaedic surgery is an objective that is achieved by disseminating research findings, publishing articles, and presenting study results at scientific conferences, as well as sharing results with the scientific community and healthcare professionals. A website of the project is assumed to play an important role in the dissemination of the project.