Abstract
Owing to rapidly changing global demographics, adult spinal deformity (ASD) now accounts for a significant proportion of the Global Burden of Disease. Sagittal imbalance caused by age-related degenerative changes leads to back pain, neurological deficits, and deformity, which negatively affect the health-related quality of life (HRQoL) of patients. Along with the recognized regional, global, and sagittal spinopelvic parameters, poor paraspinal muscle quality has recently been acknowledged as a key determinant of the clinical outcomes of ASD. Although the Scoliosis Research Society-Schwab ASD classification system incorporates the radiological factors related to HRQoL, it cannot accurately predict the mechanical complications. With the rapid advances in surgical techniques, many surgical options for ASD have been developed, ranging from minimally invasive surgery to osteotomies. Therefore, structured patient-specific management is important in surgical decision-making, selecting the proper surgical technique, and to prevent serious complications in patients with ASD. Moreover, utilizing the latest technologies such as robotic-assisted surgery and machine learning, should help in minimizing the surgical risks and complications in the future.