RT Journal Article
SR Electronic
T1 Future Data Points to Implement in Adult Spinal Deformity Assessment for Artificial Intelligence Modeling Prediction: The Importance of the Biological Dimension
JF International Journal of Spine Surgery
JO Int J Spine Surg
FD International Society for the Advancement of Spine Surgery
SP S34
OP S44
DO 10.14444/8502
VO 17
IS S1
A1 Sleiman Haddad
A1 Javier Pizones
A1 Riccardo Raganato
A1 Michael M. Safaee
A1 Justin K. Scheer
A1 Ferran Pellisé
A1 Christopher P. Ames
YR 2023
UL https://www.ijssurgery.com/content/17/S1/S34.abstract
AB Adult spinal deformity (ASD) surgery is still associated with high surgical risks. Machine learning algorithms applied to multicenter databases have been created to predict outcomes and complications, optimize patient selection, and improve overall results. However, the multiple data points currently used to create these models allow for 70% of accuracy in prediction. We need to find new variables that can capture the spectrum of probability that is escaping from our control. These proposed variables are based on patients’ biological dimensions, such as frailty, sarcopenia, muscle and bone (tissue) sampling, serological assessment of cellular senescence, and circulating biomarkers that can measure epigenetics, inflammaging, and -omics. Many of these variables are proven to be modifiable and could be improved with proper nutrition, toxin avoidance, endurance exercise, and even surgery. The purpose of this manuscript is to describe the different future data points that can be implemented in ASD assessment to improve modeling prediction, allow monitoring their response to prerehabilitation programs, and improve patient counseling.