ACLART

Teaching a knee to predict its own future

The aim of the project is to use artificial intelligence (AI), through machine learning algorithms applied to clinical and in silico data, to predict the likelihood of an anterior cruciate ligament reconstruction (ACLR) revision and to improve the pre- and post-operative management of these patients. By harnessing the potential of clinical data, computational simulations (in silico) and AI-based analyses, the project has the potential to significantly improve patients’ clinical outcomes and help transform the future of orthopaedic surgery.

The project combines Anterior Cruciate Ligament Reconstruction (ACLR) with machine learning based on artificial intelligence and computational simulation (in silico), proposing a predictive model for the clinical outcomes of ACLR. The integration of patients’ clinical data and the specific characteristics of the surgery into machine learning models and algorithms, together with computational simulations, will enable the generation of knowledge capable of improving patient selection, supporting surgical decision-making and optimising pre- and post-operative management, thereby increasing the effectiveness of anterior cruciate ligament reconstruction procedures.

The incorporation of artificial intelligence-guided in silico simulations into ACLR management is an innovative feature of this approach, as it enables an integrated analysis of different aspects of anterior cruciate ligament reconstruction surgery and supports clinical decision-making in a way that would not be possible by relying solely on the analysis of clinical data via machine learning. This approach proves particularly useful in surgical planning, especially in complex cases that require difficult decisions on the part of the surgical team. In this context, the results obtained through computational models based on the finite element method, including results from previously published studies, will be integrated into machine learning models and algorithms, with the aim of correlating the predictions derived from clinical data with the biomechanical factors derived from in silico simulations.

Project: ACLART – Prediction of clinical outcomes of anterior cruciate ligament reconstruction using in silico simulation combined with artificial intelligence

Project Code: COMPETE2030-FEDER-00867200

Call for Proposals: MPr-2023-12 – SACCCT – Scientific Research and Technological Development (IC&DT) Projects – Individual and Jointly Funded Operations

Créditos