Authors:	Kamer, Lukas Noser, Hansrudi Arand, Charlotte Handrich, Kristin Rommens, Pol Maria Wagner, Daniel
Title:	Artificial intelligence and CT-based 3D statistical modeling to assess transsacral corridors and plan implant positioning
Online publication date:	12-Sep-2022
Year of first publication:	2021
Language:	english
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.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7726
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY-NC
Information on rights of use:	https://creativecommons.org/licenses/by-nc/4.0/
Journal:	Journal of orthopaedic research 39 12
Pages or article number:	2681 2692
Publisher:	Wiley
Publisher place:	Hoboken, NJ u.a.
Issue date:	2021
ISSN:	1554-527X
Publisher DOI:	10.1002/jor.25010
Appears in collections:	JGU-Publikationen