Pediatric Spine Segmentation and Modeling Using Machine Learning

Mikulka, Jan; Chalupa, Daniel; Říha, Kamil; Filipovič, Milan; Dostál, Marek

doi:10.1109/icumt48472.2019.8970894

Cited by 3 publications

(1 citation statement)

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“…This capability is enabled by ML algorithms that automate the segmentation of the spine and generation of 3D models. 43 These models are trained on patient data to design patient-specific spinal cages in a manner that significantly reduces cage fitting time and improves correction quality for better outcomes. 44 Moreover, NNs were trained to autonomously place pedicle screws with the correct length, diameter, and angulation for FIGURE 2.…”

Section: Decision-making Support Surgical Planningmentioning

confidence: 99%

Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art

et al. 2022

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Recent developments in machine learning (ML) methods demonstrate unparalleled potential for application in the spine. The ability for ML to provide diagnostic faculty, produce novel insights from existing capabilities, and augment or accelerate elements of surgical planning and decision making at levels equivalent or superior to humans will tremendously benefit spine surgeons and patients alike. In this review, we aim to provide a clinically relevant outline of ML-based technology in the contexts of spinal deformity, degeneration, and trauma, as well as an overview of commercial-level and precommercial-level surgical assist systems and decisional support tools. Furthermore, we briefly discuss potential applications of generative networks before highlighting some of the limitations of ML applications. We conclude that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium—it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety—but is yet subject to challenges that must be addressed before widespread implementation.

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Section: Decision-making Support Surgical Planningmentioning

confidence: 99%