P37. Artificial intelligence clustering of adult spinal deformity morphology predicts surgical characteristics, alignment, and outcomes

BACKGROUND CONTEXT AI algorithms have shown substantial promise in medical image analysis. Previous studies of ASD clusters have analyzed alignment metrics – this study sought to complement these efforts by analyzing images of anatomical landmarks. PURPOSE We hypothesized that a neural-network-based artificial intelligence (AI) algorithm would cluster preoperative lateral radiographs of into groups with distinct morphology. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE A total of 915 patients with adult spinal deformity and preoperative lateral radiographs. OUTCOME MEASURES Schwab modifiers for SVA and PI-LL, three-column osteotomy, upper instrumented vertebrae, baseline Oswestry Disability Index, and 2-year likelihood of reaching MCID in ODI (set at -12.8). Proximal junctional kyphosis and proximal junctional failure were defined using previously published radiographic criteria. METHODS Vertebral locations for C3-L5, sacral endplate, and femoral heads were measured on lateral radiographs. Pixel locations were used to create a black-and-white overlay to the image, which was subsequently standardized for size and position using the femoral heads and sacral endplate. These images were used to train a self-organizing map (SOM). SOMs are a form of artificial neural network frequently employed in unsupervised classification tasks. RESULTS In total, 915 preoperative lateral radiographs were analyzed. A 2 × 3, toroidal SOM was trained. The mean spine shape was plotted for each cluster. Alignment, surgical characteristics, and outcomes were compared between clusters. Clusters C and D exhibited a particularly high proportion of patients with optimal (ie, modifier 0) values of PI-LL (65.0% and 68.5%) and SVA (72.8% and 53.1%). Conversely, clusters B, E, and F tended to have poor (ie, modifier ++) PI-LL (74.8%, 66.9%, and 74.6%) and SVA (75.5%, 48.6%, and 58.7%). 3-CO was most common among cluster A (26.8%), cluster B (32.6%), and cluster F (32.7%). UIV at T7-T12 was most common among cluster B (51.1%) and cluster F (60.3%). ODI CONCLUSIONS This study developed a self-organizing map that clustered preoperative lateral radiographs of ASD patients into groups with highly distinct overall spinal morphology. These clusters predicted alignment, surgical characteristics, and HRQOL. Further studies of this classification approach will expand to compare pre- and postoperative radiographs. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.