To train and validate an algorithm mimicking experienced surgeons' decision making regarding upper instrumented vertebra (UIV) selection in the surgical correction of thoracolumbar adult spinal deformity (ASD).A retrospective review was conducted of a single center database of ASD patients who underwent fusion of at least the lumbar spine (UIV>L1 to pelvis) from 2013-2018. Data collection included demographic and radiographic information. The sample was stratified into 3 groups: 70% for training, 15% for validation and 15% for performance testing. Using a deep learning algorithm, a neural network model was trained to select between upper thoracic (UT: T1-T6) and lower thoracic (LT: T7-T12) UIV. Parameters used in the deep learning algorithm included demographic, coronal, and sagittal pre-operative alignment and post-operative PI-LL.143 patients (mean age: 63.3yo±10.6, 81.8% F) with moderate to severe deformity (MaxCobb: 43°±22; TPA: 27°±14 ; PI-LL: 22°±21) were included. Patients underwent a significant change in lumbar alignment (ΔPI-LL: 21°±16 p<0.001); 35.0% had UIV in the UT, and 65.0% in the LT. At 1Y, revision rate was 11.9% and rate of radiographic proximal junctional kyphosis (PJK) was 29.4%. Neural network was composed of 8 inputs, 10 hidden neurons and 1 output (UT or LT). After training, results demonstrated an accuracy of 81.0%, precision of 87.5%, and recall of 87.5% on testing.An artificial neural network successfully mimicked two lead surgeons' decision making in the selection of UIV for ASD correction. Future models integrating surgical outcomes should be developed.