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Objective To explore decision support systems (DSS) used in spine-pelvic surgery, analyze its functional possibilities and approaches that allow the surgeon to make a correct decision. Material and methods Functional possibilities with modern DSSs used in surgery of spine-pelvic complex were reviewed with unified criteria using websites of DSS manufacturers and publications in scientific journals. Results MediCAD, TraumaCAD, Surgimap, Sectra AB and OrthoView are most common DSSs used for spine and hip surgery planning. The above systems can be applied in orthopedic surgery of several independent anatomical regions (e.g., spine, pelvis, femur, tibia, foot). But none of the systems can be applied to spine-pelvic complex. DSS facilitates only geometrical planning with geometric measurements, simulation of physiologically normal location of anatomical elements as well as selection and semi-automatic implant positioning. Conclusion Both geometrical planning and biomechanical simulation are required to achieve positive long-term follow-up of surgical treatment. Biomechanical simulation allows assessment of an extent and pattern of injury caused by malalignment of spine-pelvic complex and surgical intervention planning with the help of reconstruction options offered. The use of DSS should involve geometric planning, biomechanical simulation of the expected surgical outcome and prediction of a long-term follow-up. Introduction of DSS into clinical practice will facilitate the quality of medical care and rehabilitation with concurrent optimization of the national expenditure on health care.

International audience; We are combining symbolic and geometric planning to synthesize human-aware plans in order to deal with the complex and highly intricate planning problems induced by Human-Robot collaborative object manipulation. In this paper, we summarize our previous contributions - refining symbolic actions at geometric level, during the symbolic planning, in order to assess their feasibility and computing the geometric side effects-, then we present the current contributions meant to tighten the cooperation between the symbolic planning and the geometric planning: the symbolic planner helps the geometric one by providing it with constraints and domain-expert knowledge making the geometric planner more efficient, and the geometric planner helps the symbolic one to find the best plan based on social costs computed at geometric level. We also propose different examples, highlighting the interest of such cooperation between the planners in simulation and on our PR2 robot.