Spine · 2025
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.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Walker Corey T, Babadjouni Robin, Gibbs Wende, Lord Elizabeth, Gausper Adeesya, Osorio Joseph, Molina Camilo, Jones Kristen, van Hooff Miranda, Theologis Alexander, Yagi Mitsuru, Blakemore Laurel, Shah Suken, Hu Serena, de Kleuver Marinus, Pizones Javier, Kelly Michael, Pellise Ferran, Ames Christopher, Eastlack Robert, Scoliosis Research Society Adult Spinal Deformity Task Force Senescence Committee
Аннотация
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.
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.