Orthopedic digital imaging developer EOS Imaging announced a new partnership with Montreal-based Spinologics to develop biomechanical simulation software dedicated to spine surgery planning.
The software will be integrated into EOS Imaging's cloud-based 3D planning software and will allow surgeons to plan treatment from EOS 3D datasets, taking into account patient physiological parameters, the firms said.
The joint development effort will use patient-specific 3D datasets from EOS exams to simulate in situ bending, vertebral derotation, and contraction-distraction, as well as gravitational effects. The new capability should allow surgeons to better understand and anticipate the effects of forces on the spine while planning initial and revision surgeries, the firms added.
EOS imaging will have the exclusive rights to sell the new software worldwide with an anticipated release date of mid-2017.
Spinologics' team has been exploring the biomechanics of the spine for more than 25 years within academic and corporate research and development, and has developed dedicated software to simulate the biomechanical response to various approaches of spinal treatment.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
