Current Concepts on Imaging and Artificial Intelligence of Osteosarcopenia in the Aging Spine: A Review for Spinal Surgeons by the SRS Adult Spinal Deformity Task Force on Senescence

Corey T. Walker, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA.
Robin Babadjouni, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA.
Wende Gibbs, Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ.
Elizabeth Lord, Department of Orthopedics, University of California-Los Angeles, Los Angeles, CA.
Adeesya Gausper, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA.
Joseph Osorio, Department of Neurosurgery, University of California-San Diego, San Diego, CA.
Camilo Molina, Department of Neurosurgery, Washington University in St. Louis, St. Louis, MO.
Kristen Jones, Department of Neurosurgery, Duke University, Durham, NC.
Miranda van Hooff, Department for Scientific Research, Sint Maartenskliniek, Ubbergen, Netherlands.
Alexander Theologis, Department of Orthopedic Surgery, University of California-San Francisco, San Francisco, CA.
Mitsuru Yagi, Department of Orthopedic Surgery, International University of Health and Welfare, Tochigi, Japan.
Laurel Blakemore, Department of Orthopedic Surgery, Pediatric Specialists of Virginia, Fairfax, VA.
Suken Shah, Department of Orthopedic Surgery, Nemours Children's Hospital, Wilmington, DE.
Serena Hu, Department of Orthopedic Surgery, Stanford University, Redwood City, CA.
Marinus de Kleuver, Department of Orthopedic Surgery, Radboud University, Nijmegen, The Netherlands.
Javier Pizones, Department of Orthopedic Surgery, La Paz University Hospital, Madrid, Spain.
Michael Kelly, Department of Orthopedic Surgery, Rady Children's Hospital, San Diego, CA.
Ferran Pellise, Department of Orthopedic Surgery, Barcelona Spine Institute, Barcelona, Spain.
Christopher Ames, Department of Neurosurgery, University of California-San Francisco, San Francisco, CA.
Robert Eastlack, Department of Orthopedic Surgery, Scripps Clinics, La Jolla, CA.

Document Type

Article

Abstract

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.

Medical Subject Headings

Humans; Sarcopenia (diagnostic imaging, surgery); Osteoporosis (diagnostic imaging, surgery); Artificial Intelligence; Aging (pathology); Spine (diagnostic imaging, surgery); Machine Learning

Publication Date

9-15-2025

Publication Title

Spine

E-ISSN

1528-1159

Volume

50

Issue

18

First Page

1278

Last Page

1289

PubMed ID

40511548

Digital Object Identifier (DOI)

10.1097/BRS.0000000000005426

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