Dr. Alexander Perdomo-Pantoja Profile

Dr. Alexander Perdomo-Pantoja

at Johns Hopkins Univ

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Publications (3)

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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Proceedings Article | 8 March 2019 Presentation + Paper

Automatic trajectory and instrument planning for robot-assisted spine surgery

Rohan Vijayan, Tharindu De Silva, Runze Han, Ali Uneri, Sophia Doerr, Michael Ketcha, Alexander Perdomo-Pantoja, Nicholas Theodore, Jeffrey Siewerdsen

Proceedings Volume 10951, 1095102 (2019) https://doi.org/10.1117/12.2513722

KEYWORDS: Spine, Image registration, Surgery, Computed tomography, Navigation systems, Algorithm development, Principal component analysis, Statistical modeling, Image-guided intervention, Robotic surgery, Robotic systems

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Purpose. We report the initial implementation of an algorithm that automatically plans screw trajectories for spinal pedicle screw placement procedures to improve the workflow, accuracy, and reproducibility of screw placement in freehand navigated and robot-assisted spinal pedicle screw surgery. In this work, we evaluate the sensitivity of the algorithm to the settings of key parameters in simulation studies. Methods. Statistical shape models (SSMs) of the lumbar spine were constructed with segmentations of L1-L5 and bilateral screw trajectories of N=40 patients. Active-shape model (ASM) registration was devised to map the SSMs to the patient CT, initialized simply by alignment of (automatically annotated) single-point vertebral centroids. The atlas was augmented by definition of “ideal / reference” trajectories for each spinal pedicle, and the trajectories are deformably mapped to the patient CT. A parameter sensitivity analysis for the ASM method was performed on 3 parameters to determine robust operating points for ASM registration. The ASM method was evaluated by calculating the root-mean-square-error between the registered SSM and the ground-truth segmentation for the L1 vertebra, and the trajectory planning method was evaluated by performing a leave-one-out analysis and determining the entry point, end point, and angular differences between the automatically planned trajectories and the neurosurgeon-defined reference trajectories. Results. The parameter sensitivity analysis showed that the ASM registration algorithm was relatively insensitive to initial profile length (P_Linitial) less than ~4 mm, above which runtime and registration error increased. Similarly stable performance was observed for a maximum number of principal components (PC_max) of at least 8. Registration error ~2 mm was evident with diminishing return beyond a number of iterations, N_iter, ~2000. With these parameter settings, ASM registration of L1 achieved (2.0 ± 0.5) mm RMSE. Transpedicle trajectories for L1 agreed with reference definition by (2.6 ± 1.3) mm at the entry point, by (3.4 ± 1.8) mm at the end point, and within (4.9° ±2.8°) in angle. Conclusions. Initial results suggest that the algorithm yields accurate definition of pedicle trajectories in unsegmented CT images of the spine. The studies identified stable operating points for key algorithm parameters and support ongoing development and translation to clinical studies in free-hand navigated and robot-assisted spine surgery, where fast, accurate trajectory definition is essential to workflow.

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