Dr. Richard L. Skolasky Profile

Dr. Richard L. Skolasky

at Johns Hopkins Univ

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Publications (2)

Proceedings Article | 16 March 2020 Paper

SpineCloud: image analytics for predictive modeling of spine surgery outcomes

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

Proceedings Volume 11315, 113151O (2020) https://doi.org/10.1117/12.2566372

KEYWORDS: Surgery, Spine, Image analysis, Data modeling, Analytics, Cadmium sulfide, Feature extraction, Medicine, Performance modeling, Medical imaging

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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.

SPIE Journal Paper | 18 February 2020 Open Access

SpineCloud: image analytics for predictive modeling of spine surgery outcomes

Tharindu De Silva, Satyanarayana Vedula, Alexander Perdomo-Pantoja, Rohan Vijayan, Sophia Doerr, Ali Uneri, Runze Han, Michael Ketcha, Richard Skolasky, Timothy Witham, Nicholas Theodore, Jeffrey Siewerdsen

JMI, Vol. 7, Issue 03, 031502, (February 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.3.031502

KEYWORDS: Surgery, Spine, Analytics, Data modeling, Image analysis, Performance modeling, Binary data, Computed tomography, Brain-machine interfaces, Image segmentation

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