Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches.

Background: This ex vivo experimental study sought to compare screw planning accuracy of a self-derived deep-learning-based (DL) and a commercial atlas-based (ATL) tool and to assess robustness towards pathologic spinal anatomy.

Methods: From a consecutive registry, 50 cases (256 screws in L1-L5) were randomly selected for experimental planning. Reference screws were manually planned by two independent raters.

CT-Navigated Spinal Instrumentations-Three-Dimensional Evaluation of Screw Placement Accuracy in Relation to a Screw Trajectory Plan.

Background and Objectives: In the literature, spinal navigation and robot-assisted surgery improved screw placement accuracy, but the majority of studies only qualitatively report on screw positioning within the vertebra. We sought to evaluate screw placement accuracy in relation to a preoperative trajectory plan by three-dimensional quantification to elucidate technical benefits of navigation for lumbar pedicle screws. Materials and Methods: In 27 CT-navigated instrumentations for degenerative disease, a dedicated intraoperative 3D-trajectory plan was created for all screws.

C-arm positioning for standard projections during spinal implant placement.

Fluoroscopy-guided trauma and orthopedic surgeries involve the repeated acquisition of correct anatomy-specific standard projections for guidance, monitoring, and evaluating the surgical result. C-arm positioning is usually performed by hand, involving repeated or even continuous fluoroscopy at a cost of radiation exposure and time. We propose to automate this procedure and estimate the pose update for C-arm repositioning directly from a first X-ray without the need for a patient-specific computed tomography scan (CT) or additional technical equipment.

Development and validation of an automated planning tool for navigated lumbosacral pedicle screws using a convolutional neural network.

Background Context: Navigation and robotic systems have been increasingly applied to spinal instrumentation but dedicated screw planning is a time-consuming prerequisite to tap the full potential of these techniques.

Purpose: To develop and validate an automated planning tool for lumbosacral pedicle screw placement using a convolutional neural network (CNN) to facilitate the planning process.

Study Design/setting: Retrospective analysis and processing of CT and screw planning data randomly selected from a consecutive registry of CT-navigated instrumentations from a single academic institution.

Automatic plane adjustment of orthopedic intraoperative flat panel detector CT-volumes.

To assess the result in orthopedic trauma surgery, usually three-dimensional volume data of the treated region is acquired. With mobile C-arm systems, these acquisitions can be performed intraoperatively, reducing the number of required revision surgeries. However, the acquired volumes are typically not aligned to the anatomical regions.

Computer-assisted contralateral side comparison of the ankle joint using flat panel technology.

Purpose: Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance.

Toward automatic C-arm positioning for standard projections in orthopedic surgery.

Purpose: Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error prone and involves repeated manual acquisitions or even continuous fluoroscopy.