SpineCloud: Image analytics for predictive modeling of spine surgery outcomes

T. De Silva, S. S. Vedula, A. Perdomo-Pantoja, R. Vijayan, S. A. Doerr, A. Uneri, R. Han, M. D. Ketcha, R. L. Skolasky, B. Caffo, G. Hager, T. Witham, N. Theodore, J. H. Siewerdsen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Spinal degeneration and deformity present an enormous healthcare burden, with spine surgery among the main treatment modalities. Unfortunately, spine surgery (e.g., lumbar fusion) exhibits broad variability in the quality of outcome, with ∼20-40% of patients gaining no benefit in pain or function ("failed back surgery") and earning criticism that is difficult to reconcile versus rapid growth in frequency and cost over the last decade. Vital to advancing the quality of care in spine surgery are improved clinical decision support (CDS) tools that are accurate, explainable, and actionable: Accurate in prediction of outcomes; explainable in terms of the physical / physiological factors underlying the prediction; and actionable within the shared decision process between a surgeon and patient in identifying steps that could improve outcome. This technical note presents an overview of a novel outcome prediction framework for spine surgery (dubbed SpineCloud) that leverages innovative image analytics in combination with explainable prediction models to achieve accurate outcome prediction. Key to the SpineCloud framework are image analysis methods for extraction of high-level quantitative features from multi-modality peri-operative images (CT, MR, and radiography) related to spinal morphology (including bone and soft-tissue features), the surgical construct (including deviation from an ideal reference), and longitudinal change in such features. The inclusion of such image-based features is hypothesized to boost the predictive power of models that conventionally rely on demographic / clinical data alone (e.g., age, gender, BMI, etc.). Preliminary results using gradient boosted decision trees demonstrate that such prediction models are explainable (i.e., why a particular prediction is made), actionable (identifying features that may be addressed by the surgeon and/or patient), and boost predictive accuracy compared to analysis based on demographics alone (e.g., AUC improved by ∼25% in preliminary studies). Incorporation of such CDS tools in spine surgery could fundamentally alter and improve the shared decisionmaking process between surgeons and patients by highlighting actionable features to improve selection of therapeutic and rehabilitative pathways.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
ISBN (Electronic)9781510633971
StatePublished - 2020
EventMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States


  • Clinical decision support
  • Image analysis
  • Predictive modeling
  • Spine surgery
  • Surgical outcomes

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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