Artificial intelligence and CT-based 3D statistical modeling to assess transsacral corridors and plan implant positioning

Abstract

Transsacral corridors at levels S1 and S2 represent complex osseous spaces allowing percutaneous fixation of non- or minimally-displaced fragility fractures of the sacrum. To safely place transsacral implants, they must be completely intraosseous. However, standard radiographs and CT do not properly demonstrate the corridor's intricate configuration. Our goal was to facilitate the three-dimensional assessment of transsacral corridors using artificial intelligence and the planning of transsacral implant positioning. In total, 100 pelvic CTs (49 women, mean age: 58.6 ± SD 14.8 years; 51 men, mean age: 60.7 ± SD 13 years) were used to compute a 3D statistical model of the pelvic ring. On the basis of morphologic features (=predictors) and principal components scores (=response), regression learners were interactively trained, validated, and tuned to predict/sample personalized 3D pelvic models. They were matched via thin-plate spline transformation to a series of 20 pelvic CTs with fragility fractures of the sacrum (18 women and 2 men, age: 69–9.5 years, mean age: 78.65 ± SD 8.4 years). These models demonstrated the availability, dimension, cross-section, and symmetry of transsacral corridors S1 and S2, as well as the planned implant position, dimension, axes, and entry and exit points. The complete intraosseous pathway was controlled in CT reconstructions. We succeeded to establish a workflow determining transsacral corridors S1 and S2 using artificial intelligence and 3D statistical modeling.