Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy - Takeaways - MDSpire

Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy

  • By

  • Lukas Schönnagel

  • Tom Folkerts

  • Ali Guven

  • Erika Chiapparelli

  • Jiaqi Zhu

  • Gaston Camino-Willhuber

  • Thomas Caffard

  • Artine Arzani

  • Paul Köhli

  • Marco D. Burkhard

  • Jennifer Shue

  • Andrew A. Sama

  • Federico P. Girardi

  • Frank P. Cammisa

  • Alexander P. Hughes

  • March 28, 2026

  • 0 min

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  • 1

    The study aims to identify and prioritize risk factors for atrophy of lumbar paraspinal muscles using machine learning models.

  • 2

    Two predictive models were developed: a linear logistic regression model and an advanced extreme gradient boosting model.

  • 3

    Factors analyzed included demographic, comorbidities, and radiologic parameters related to spinal degeneration.

  • 4

    The study assessed multifidus muscle atrophy by measuring fatty infiltration using MRI and specialized software.

  • 5

    Findings may enhance understanding of paraspinal muscle degeneration and improve individual patient assessments.

Original Source(s)

Machine learning–based identification and ranking of risk factors for lumbar paraspinal muscle atrophy

  • by Lukas Schönnagel, Tom Folkerts, Ali Guven, Erika Chiapparelli, Jiaqi Zhu, Gaston Camino-Willhuber, Thomas Caffard, Artine Arzani, Paul Köhli, Marco D. Burkhard, Jennifer Shue, Andrew A. Sama, Federico P. Girardi, Frank P. Cammisa, Alexander P. Hughes

  • March 28, 2026

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