Towards real-time finite element simulations with machine learning for spinal surgical pre-operative planning

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Our partner, Spinologics Inc. is a small company specialized in spine related medical device development using finite element modelling (FEM). Over the years, they have developed a biomechanical numerical laboratory to simulate the interactions between the human body and medical devices, such as implants or prosthesis. Spinologics considers using artificial intelligence and machine learning to predict the surgical correction of scoliosis patients in a timely manner. Such expertise in machine learning and artificial intelligence is not available in their research staff. Thus, they seek our expertise in machine learning of the spine. Spinal correction is dependent on the biomechanics of the patient's spine. Such a correction is linked to the patient's spinal range of motion, which is inherent to spinal intervertebral flexibility as well as the shape of the deformed spine. A large number of simulations would be generated from different patient anatomy and from the set of simulations; features would be extracted to learn the parameters of the simulation. Such technique would be of high interest, since it would learn the biomechanical model from the simulations and provide clinicians with a visual update within seconds. Deep neural networks (DNN) will be introduced for the prediction of the output shape. DNNs are capable of modelling complex, nonlinear relationships between input and output variables. In our project, we wish to learn which forces to apply to obtain the output profile. The input parameters to train the model would include: the patient's pre-operative spinal shape, spinal flexibility, and target profiles. The output of the learning model is the feasible spinal shape. The trained model would then serve to predict the plausible spinal shape for a specific patient given a target profile. The surgeon could then manipulate the target spinal profile and have a near real-time feedback on the feasible spinal shape. This project may pave the road for a fully automatic patient-specific modelling and it could lead to significant advances in spine surgery prediction and it could improve health care to Canadian patients, while being useful to spine surgeons.