Self-learning computers for surgical planning and prediction of postoperative alignment.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date:

Abstract

PURPOSE: In past decades, the role of sagittal alignment has been widely demonstrated in the setting of spinal conditions. As several parameters can be affected, identifying the driver of the deformity is the cornerstone of a successful treatment approach. Despite the importance of restoring sagittal alignment for optimizing outcome, this task remains challenging. Self-learning computers and optimized algorithms are of great interest in spine surgery as in that they facilitate better planning and prediction of postoperative alignment. Nowadays, computer-assisted tools are part of surgeons' daily practice; however, the use of such tools remains to be time-consuming.

Authors

  • Renaud Lafage
    Spine Research Laboratory, Hospital for Special Surgery, 535 E 71st Street, New York, NY, 10021, USA.
  • Sébastien Pesenti
    Spine Research Laboratory, Hospital for Special Surgery, 535 E 71st Street, New York, NY, 10021, USA. sebastien.pesenti@ap-hm.fr.
  • Virginie Lafage
    Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, 306 E 15th Street, Suite 1F, New York, NY 10003, USA.
  • Frank J Schwab
    Spine Research Laboratory, Hospital for Special Surgery, 535 E 71st Street, New York, NY, 10021, USA.