TY - JOUR
T1 - Current Concepts on Imaging and Artificial Intelligence of Osteosarcopenia in the Aging Spine
T2 - A Review for Spinal Surgeons by the SRS Adult Spinal Deformity Task Force on Senescence
AU - Walker, Corey T.
AU - Babadjouni, Robin
AU - Gibbs, Wende
AU - Lord, Elizabeth
AU - Gausper, Adeesya
AU - Osorio, Joseph
AU - Molina, Camilo
AU - Jones, Kristen
AU - Van Hooff, Miranda
AU - Theologis, Alexander
AU - Yagi, Mitsuru
AU - Blakemore, Laurel
AU - Shah, Suken
AU - Hu, Serena
AU - De Kleuver, Marinus
AU - Pizones, Javier
AU - Kelly, Michael
AU - Pellise, Ferran
AU - Ames, Christopher
AU - Eastlack, Robert
N1 - Publisher Copyright:
© 2025 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2025/9/15
Y1 - 2025/9/15
N2 - Study Design. Narrative review. Objective. To explore the intersection of osteoporosis, sarcopenia, radiomics, and machine learning in spine surgery, with a focus on clinical applications and opportunities for advancing assessment and predictive modeling methods. Summary of Background Data. Osteoporosis and sarcopenia are significant contributors to negative outcomes in the aging adult spine. Current methodologies for evaluating these disease states remain limited, with significant variability and poor standardization. Advances in computational medicine provide a novel opportunity to improve quantitative assessment of osteosarcopenia, as demonstrated in other areas of medicine. Using radiomic approaches for predictive outcome modeling in spine surgery remains largely untapped. Materials and Methods. A comprehensive literature search was performed. Articles were identified using the search terms "osteoporosis,""sarcopenia,""osteosarcopenia,""radiomics,""spine surgery,"and "machine learning."Relevant studies were selected based on their focus on the intersection of these topics, emphasizing clinical, imaging, and computational methodologies in spine surgery. Results. This review highlights the existing conventional and research methods of assessing both osteoporosis and sarcopenia, particularly regarding their clinical application in spine surgery. Areas of research within the radiomic space for both conditions are also discussed to describe opportunities for growth of future research and areas of focus needed to advance the field of spine surgery alongside the rapid growth of artificial intelligence. Conclusion. Understanding the relationship between osteoporosis, sarcopenia, and frailty is essential to improving outcomes in spine surgery. Advanced imaging and machine learning approaches offer the potential for more precise assessments and tailored interventions. The Scoliosis Research Society Adult Spinal Deformity Task Force on Senescence has identified this as an area of maximal importance for strategic growth and development of the field.
AB - Study Design. Narrative review. Objective. To explore the intersection of osteoporosis, sarcopenia, radiomics, and machine learning in spine surgery, with a focus on clinical applications and opportunities for advancing assessment and predictive modeling methods. Summary of Background Data. Osteoporosis and sarcopenia are significant contributors to negative outcomes in the aging adult spine. Current methodologies for evaluating these disease states remain limited, with significant variability and poor standardization. Advances in computational medicine provide a novel opportunity to improve quantitative assessment of osteosarcopenia, as demonstrated in other areas of medicine. Using radiomic approaches for predictive outcome modeling in spine surgery remains largely untapped. Materials and Methods. A comprehensive literature search was performed. Articles were identified using the search terms "osteoporosis,""sarcopenia,""osteosarcopenia,""radiomics,""spine surgery,"and "machine learning."Relevant studies were selected based on their focus on the intersection of these topics, emphasizing clinical, imaging, and computational methodologies in spine surgery. Results. This review highlights the existing conventional and research methods of assessing both osteoporosis and sarcopenia, particularly regarding their clinical application in spine surgery. Areas of research within the radiomic space for both conditions are also discussed to describe opportunities for growth of future research and areas of focus needed to advance the field of spine surgery alongside the rapid growth of artificial intelligence. Conclusion. Understanding the relationship between osteoporosis, sarcopenia, and frailty is essential to improving outcomes in spine surgery. Advanced imaging and machine learning approaches offer the potential for more precise assessments and tailored interventions. The Scoliosis Research Society Adult Spinal Deformity Task Force on Senescence has identified this as an area of maximal importance for strategic growth and development of the field.
KW - artificial intelligence
KW - frailty
KW - machine learning
KW - osteoporosis
KW - osteosarcopenia
KW - radiomics
KW - sarcopenia
KW - sarcopenic obesity
KW - senescence
KW - spine surgery
UR - https://www.scopus.com/pages/publications/105008792465
U2 - 10.1097/BRS.0000000000005426
DO - 10.1097/BRS.0000000000005426
M3 - Review article
C2 - 40511548
AN - SCOPUS:105008792465
SN - 0362-2436
VL - 50
SP - 1278
EP - 1289
JO - Spine
JF - Spine
IS - 18
ER -