Computer-Assisted Screw Size and Insertion Trajectory Planning for Pedicle Screw Placement Surgery

Structural Optimization of 3D-Printed Porous Titanium Implants Promotes Bone Regeneration for Enhanced Biological Fixation

Structural defects and biological inertness significantly impair the integration of titanium alloy implants and bone tissues. In spinal internal fixation, the issue of pedicle screw loosening or fracture caused by poor integration urgently needs solving. In this study, we utilized 3D printing technology to custom fabricate a structurally optimized porous pedicle screw with the aim of enhancing bone regeneration and integration at the defect site, thereby enhancing the biological fixation of the implant in vivo. Results showed that the structurally optimized porous unit has superior mechanical properties and actively promotes cell adhesion and growth at the surface interface. The porous screw based on this optimized structure has immediate bonding strength and bending resistance comparable to clinical products and provides an optimal spatial structure for newly regenerated bone ingrowth and integration. Alkali-thermal activation constructed a bioactive sodium titanate coating on the screw surface, which promoted the proliferation, adhesion, and osteogenic differentiation of BMSCs. This further enhances the biological performance of the implant surface interface, highlighting the advantages of structurally optimization. In the beagle vertebrae, the structurally optimized bioactive screw promoted the regeneration of surrounding bone and the inward growth of newly regenerated bone, strengthening the osseointegration strength at the implant interface and inside, thus synergistically enhancing biological fixation. This study pioneers the introduction of porous structure into a pedicle screw through structural optimization, which provides an innovative strategy for the development of spinal internal fixation and improves the potential value for advancing the utilization of 3D-printed orthopedic implants.

SafeRPlan: Safe deep reinforcement learning for intraoperative planning of pedicle screw placement

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of the anatomy. Robotic surgery systems have been proposed to improve placement accuracy. Despite remarkable advances, current robotic systems still lack advanced mechanisms for continuous updating of surgical plans during procedures, which hinders attaining higher levels of robotic autonomy. These systems adhere to conventional rigid registration concepts, relying on the alignment of preoperative planning to the intraoperative anatomy. In this paper, we propose a safe deep reinforcement learning (DRL) planning approach (SafeRPlan) for robotic spine surgery that leverages intraoperative observation for continuous path planning of pedicle screw placement. The main contributions of our method are (1) the capability to ensure safe actions by introducing an uncertainty-aware distance-based safety filter; (2) the ability to compensate for incomplete intraoperative anatomical information, by encoding a-priori knowledge of anatomical structures with neural networks pre-trained on pre-operative images; and (3) the capability to generalize over unseen observation noise thanks to the novel domain randomization techniques. Planning quality was assessed by quantitative comparison with the baseline approaches, gold standard (GS) and qualitative evaluation by expert surgeons. In experiments with human model datasets, our approach was capable of achieving over 5% higher safety rates compared to baseline approaches, even under realistic observation noise. To the best of our knowledge, SafeRPlan is the first safety-aware DRL planning approach specifically designed for robotic spine surgery.

Experimental research based on robot-assisted surgery: Lower limb fracture reduction surgery planning navigation system

Background and Aims

Lower extremity fracture reduction surgery is a key step in the treatment of lower extremity fractures. How to ensure high precision of fracture reduction while reducing secondary trauma during reduction is a difficult problem in current surgery.

Methods

First, segmentation and three-dimensional reconstruction are performed based on fracture computed tomography images. A cross-sectional point cloud extraction algorithm based on the normal filtering of the long axis of the bone is designed to obtain the cross-sectional point clouds of the distal bone and the proximal bone, and the optimal reset target pose of the broken bone is obtained by using the iterative closest point algorithm. Then, the optimal reset sequence of reset parameters was determined, combined with the broken bone collision detection algorithm, a surgical planning algorithm for lower limb fracture reset was proposed, which can effectively reduce the reset force while ensuring the accuracy of the reset process without collision.

Results

The average error of the reduction of the model bone was within 1.0 mm. The reduction operation using the planning and navigation system of lower extremity fracture reduction surgery can effectively reduce the reduction force. At the same time, it can better ensure the smooth change of the reduction force.

Conclusion

Planning and navigation system of lower extremity fracture reduction surgery is feasible and effective.

Pedicle screw path planning for multi-level vertebral fixation

BACKGROUND

For the spinal internal fixation procedures, connecting rods to the pedicle screws are commonly used in all spinal segments from the cervical to sacral spine. So far, we have only seen single vertebral screw trajectory planning methods in literatures. Joint screw placements in multi-level vertebrae with the constraint of an ipsilateral connecting rod are not considered.

PURPOSE

In this paper, a screw trajectory planning method that considers screw-rod joint system with both multi-level vertebral constraints and individual vertebral safety tolerance are proposed.

METHODS

The proposed method addresses three challenging constraints jointly for multi-level vertebral fixation with pedicle screws. First, a cylindrical screw safe passage model is suggested instead of a unique mathematical optimal trajectory for a single pedicle. Second, the flexible screw cap accessibility model is also included. Third, the connecting rod is modeled to accommodate the spine contour and support the needed gripping capacity. The retrospective clinical data of relative normal shape spines from Beijing Jishuitan hospital were used in the testing. The screw trajectories from the existing methods based on single vertebra and the proposed method based on multi-level vertebrae optimization are calculated and compared.

RESULTS

The results showed that the calculated screw placements by the proposed method can achieve 88% success rate without breaking the pedicle cortex and 100% in clinical class A quality (allow less than 2 mm out of the pedicle cortex) compared to 86.1% and 99.1%, respectively, with the existing methods. Expert evaluation showed that the screw path trajectories and the connecting rod calculated by the new method satisfied the clinical implantation requirements.

CONCLUSIONS

The new screw planning approach that seeks an overall optimization for multi-level vertebral fixation is feasible and more advantageous for clinical use than the single vertebral approaches.

A user-friendly system for identifying the optimal insertion direction and to choose the best pedicle screws for patient-specific spine surgery

Background and objective

Many diseases of the spine require surgical treatments that are currently performed based on the experience of the surgeon. The basis of this study is to deliver an automatic and patient-specific algorithm able to come to the aid of the surgeons in pedicle arthrodesis operations, by finding the optimal direction of the screw insertion, the maximum screw diameter and the maximum screw length.

Results

The paper introduce an algorithm based on the reconstructed geometry of a vertebra by 3D-scan that is able to identify the best introduction direction for screw and to select, from commercial and/or personalised databases, the best screws in order to maximize the occupation of the bone while not intersecting each other and not going through the walls of the pedicle and the bounds of the vertebral body. In fact, for pedicle arthrodesis surgery, the incorrect positioning of the screws may cause operating failures, an increase in the overall duration of surgery and, therefore, more harmful, real-time X-ray checks. In case of not availability on market, the algorithm also suggests parameters for designing and manufacturing an 'ad hoc' solution. The algorithm has been tested on 6 vertebras extracted by a medical database. Furthermore, the algorithm is based on a procedure through which the surgeon can freely choose the entering point of the screw (based on his/her own experience and will). A real patient vertebra has been processed with almost 400 different entering point, always giving a feedback on the possibility to use the entering point (in case of unavailability of a good trajectory) and on the individuation of the right trajectory and the choose of the better screws.

Conclusions

In very recent bibliography, several papers deal with procedure to screw' trajectory planning in arthrodesis surgery by using Computer Aided surgery systems, and some of them used also modern methodologies (KBE, AI, Deep learning, etc.) methods for planning the surgery as better as possible. Nevertheless, no methodologies or algorithm have been still realized to plan the trajectory and choose the perfect fitting screws on the basis of the patient-specific vertebra. This paper represents a wind of novelty in this field and allow surgeons to use the proposed algorithm for planning their surgeries. Finally, it allows also the easy creation of a customized surgical template, characterized by two cylindrical guides that follow a correct trajectory previously calculated by means of that automatic algorithm generated on the basis of a vertebra CAD model for a specific patient. The surgeon will be able to set the template (drilling guides) on the patient's vertebra and safely apply the screws.

Improving pedicle screw path planning by vertebral posture estimation

Objective.Robot-assisted pedicle screw placement in spinal surgery can reduce the complications associated with the screw placement and reduce the hospital return counts due to malfunctions. However, it requires accurate planning for a high-quality procedure. The state-of-the-art technologies reported in the literature either ignore the anatomical variations across vertebrae or require substantial human interactions. We present an improved approach that achieves pedicle screw path planning through multiple projections of a numerically re-oriented vertebra with the estimated posture.Approach.We proposed an improved YOLO-type neural network model (YOLOPOSE3D) to estimate the posture of a vertebra before pedicle path planning. In YOLOPOSE3D, the vertebral posture is given as a rotation quaternion and 3D location coordinates by optimizing the intersection over union of the vertebra with the predicted posture and the actual posture. Then, a new local coordinate system is established for the vertebra based on the estimated posture. Finally, the optimal pedicle screw path trajectory is determined from the multiple projections of the vertebra in the local coordinates.Main results.The experimental results in difficult cases of scoliosis showed that the new YOLOPOSE3D network could accurately detect the location and posture of the vertebra with average translation and orientation errors as small as 1.55 mm and 2.55°. The screw path planning achieved 83.1% success rate without breaking the pedicle cortex for the lumbar vertebral L1-L5, which is better than that of a doctor's manual planning, 82.4%. With the clinical class A requirement to allow less than 2 mm out of the pedicle cortex, the success rate achieved nearly 100%.Significance.The proposed YOLOPOSED3D method can accurately determine the vertebral postures. With the improved posture prior, better clinical outcomes can be achieved for pedicle screw placement in spine internal fixation procedures.

Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle

Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications. While deep learning strategies have brought to this field significant improvements over recent years, transitional and pathological vertebrae are still plaguing most existing approaches as a consequence of their poor representation in training datasets. Alternatively, proposed non-learning based methods take benefit of prior knowledge to handle such particular cases. In this work we propose to combine both strategies. To this purpose we introduce an iterative cycle in which individual vertebrae are recurrently localized, segmented and identified using deep-networks, while anatomic consistency is enforced using statistical priors. In this strategy, the transitional vertebrae identification is handled by encoding their configurations in a graphical model that aggregates local deep-network predictions into an anatomically consistent final result. Our approach achieves the state-of-the-art results on the VerSe20 challenge benchmark, and outperforms all methods on transitional vertebrae as well as the generalization to the VerSe19 challenge benchmark. Furthermore, our method can detect and report inconsistent spine regions that do not satisfy the anatomic consistency priors. Our code and model are openly available for research purposes.¹.

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. Additional planning sets were created using the automatic DL and ATL tools. Using Python, automatic planning was compared to the reference in 3D space by calculating minimal absolute distances (MAD) for screw head and tip points (mm) and angular deviation (degree). Results were evaluated for interrater variability of reference screws. Robustness was evaluated in subgroups stratified for alteration of spinal anatomy. Results: Planning was successful in all 256 screws using DL and in 208/256 (81%) using ATL. MAD to the reference for head and tip points and angular deviation was 3.93 ± 2.08 mm, 3.49 ± 1.80 mm and 4.46 ± 2.86° for DL and 7.77 ± 3.65 mm, 7.81 ± 4.75 mm and 6.70 ± 3.53° for ATL, respectively. Corresponding interrater variance for reference screws was 4.89 ± 2.04 mm, 4.36 ± 2.25 mm and 5.27 ± 3.20°, respectively. Planning accuracy was comparable to the manual reference for DL, while ATL produced significantly inferior results (p < 0.0001). DL was robust to altered spinal anatomy while planning failure was pronounced for ATL in 28/82 screws (34%) in the subgroup with severely altered spinal anatomy and alignment (p < 0.0001). Conclusions: Deep learning appears to be a promising approach to reliable automated screw planning, coping well with anatomic variations of the spine that severely limit the accuracy of ATL systems.

Optimization of Transpedicular Electrode Insertion for Electroporation-Based Treatments of Vertebral Tumors

Electroporation-based treatments such as electrochemotherapy and irreversible electroporation ablation have sparked interest with respect to their use in medicine. Treatment planning involves determining the best possible electrode positions and voltage amplitudes to ensure treatment of the entire clinical target volume (CTV). This process is mainly performed manually or with computationally intensive genetic algorithms. In this study, an algorithm was developed to optimize electrode positions for the electrochemotherapy of vertebral tumors without using computationally intensive methods. The algorithm considers the electric field distribution in the CTV, identifies undertreated areas, and uses this information to iteratively shift the electrodes from their initial positions to cover the entire CTV. The algorithm performs successfully for different spinal segments, tumor sizes, and positions within the vertebra. The average optimization time was 71 s with an average of 4.9 iterations performed. The algorithm significantly reduces the time and expertise required to create a treatment plan for vertebral tumors. This study serves as a proof of concept that electrode positions can be determined (semi-)automatically based on the spatial information of the electric field distribution in the target tissue. The algorithm is currently designed for the electrochemotherapy of vertebral tumors via a transpedicular approach but could be adapted for other anatomic sites in the future.

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.

PATIENT SAMPLE

Data from 179 cases was processed for CNN training and validation (155 for training, 24 for validation) leveraging a total of 1182 screws (1052 for training, 130 for validation).

OUTCOME MEASURES

Quantitative and qualitative (Gertzbein-Robbins classification [GR]) validation via comparison of automatically and manually planned reference screws, inter-rater and intra-rater variability.

METHODS

Annotated data from CT-navigated instrumentation was used to train a CNN operating in a vertebra instance-based approach employing a state-of-the-art U-Net framework. Internal five-fold cross-validation and external validation on an independent cohort not previously involved in training was performed. Quantitative validation of automatically planned screws was performed in comparison to corresponding manually planned screws by calculating the minimal absolute difference (MAD) of screw head and tip points, length and diameter, screw direction and Dice coefficient. Results were evaluated in relation to inter-rater and intra-rater variability of manual screw planning.

RESULTS

Automated screw planning was successful in all targeted 130 screws. Compared with manually planned screws as a reference, mean MAD of automatically planned screws was 4.61±2.27 mm for screw head, 3.96±2.19 mm for tip points and 5.51±3.64° for screw direction. These differences were either statistically comparable or significantly smaller when compared with interrater variability of manual screw planning (p>.99 for head point and direction, p=.004 for tip point, respectively). Mean Dice coefficient of 0.61±0.16 indicated significantly greater agreement of automatic screws with the manual reference compared with interrater agreement (Dice 0.56±0.18, p<.001). Automatically planned screws were marginally shorter (MAD 3.4±3.2 mm) and thinner (MAD mean 0.3±0.6 mm) compared with the manual reference, but with statistical significance (p<.0001, respectively). Automatically planned screws were GR grade A in 96.2% in qualitative validation. Planning time was significantly shorter with the automatic approach (0:41 min vs. 6:41 min, p<.0001).

CONCLUSIONS

We derived and validated a fully automated planning tool for lumbosacral pedicle screws using a CNN. Our validation showed noninferiority to manual screw planning and provided sufficient accuracy to facilitate and expedite the screw planning process. These results offer a high potential to improve workflows in spine surgery when integrated into navigation or robotic assistance systems.

Vertebrae Labeling via End-to-End Integral Regression Localization and Multi-Label Classification Network

Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use heatmaps to locate the vertebrae's centroid. However, the process of obtaining vertebrae's centroid coordinates using heatmaps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebrae coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. First, a novel end-to-end integral regression localization and multi-label classification network is developed, which can capture multi-scale features and also utilize the residual module and skip connection to fuse the multi-level features. Second, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure of location being destroyed, an integral regression module is used in the localization network. It combines the advantages of heatmaps representation and direct regression coordinates to achieve end-to-end training and can be compatible with any key point detection methods of medical images based on heatmaps. Finally, multi-label classification of vertebrae is carried out to improve the identification rate, which uses bidirectional long short-term memory (Bi-LSTM) online to enhance the learning of long contextual information of vertebrae. The proposed method is evaluated on a challenging data set, and the results are significantly better than state-of-the-art methods (identification rate is 91.1% and the mean localization error is 2.2 mm). The method is evaluated on a new CT data set, and the results show that our method has good generalization.

Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database

Magnetic resonance imaging (MRI) is widely utilized for diagnosing and monitoring of spinal disorders. For a number of applications, particularly those related to quantitative MRI, an essential step towards achieving reliable and objective measurements is the segmentation of the examined structures. Performed manually, such process is time-consuming and prone to errors, posing a bottleneck to its clinical applicability. A more efficient analysis would be achieved by automating a segmentation process. However, routine spine MRI acquisitions pose several challenges for achieving robust and accurate segmentations, due to varying MRI acquisition characteristics occurring in data acquired from different sites. Moreover, heterogeneous annotated datasets, collected from multiple scanners with different pulse sequence protocols, are limited. Thus, we present a manually segmented lumbar spine MRI database containing a wide range of data obtained from multiple scanners and pulse sequences, with segmentations of lumbar vertebral bodies and intervertebral discs. The database is intended for the use in developing and testing of automated lumbar spine segmentation algorithms in multi-domain scenarios.

Measurement(s)	Vertebral Body • Intervertebral Disc
Technology Type(s)	Magnetic Resonance Imaging

The accuracy and effectiveness of automatic pedicle screw trajectory planning based on computer tomography values: an in vitro osteoporosis model study

Background

Pedicle screw placement in patients with osteoporosis is a serious clinical challenge. The bone mineral density (BMD) of the screw trajectory has been positively correlated with the screw pull-out force, while the computer tomography (CT) value has been linearly correlated with the BMD. The purpose of this study was to establish an in vitro osteoporosis model and verify the accuracy and effectiveness of automated pedicle screw planning software based on CT values in this model.

Methods

Ten vertebrae (L1-L5) of normal adult pigs were randomly divided into decalcification and control groups. In the decalcification group, the vertebral bodies were decalcified with Ethylenediaminetetraacetic acid (EDTA) to construct an in vitro osteoporosis model. In the decalcification group, automatic planning (AP) and conventional manual planning (MP) were used to plan the pedicle screw trajectory on the left and right sides of the pedicle, respectively, and MP was used on both sides of the control group. CT values of trajectories obtained by the two methods were measured and compared. Then, 3D-printed guide plates were designed to assist pedicle screw placement. Finally, the pull-out force of the trajectory obtained by the two methods was measured.

Results

After decalcification, the BMD of the vertebra decreased from − 0.03 ± 1.03 to − 3.03 ± 0.29 (P < 0.05). In the decalcification group, the MP trajectory CT value was 2167.28 ± 65.62 Hu, the AP trajectory CT value was 2723.96 ± 165.83 Hu, and the MP trajectory CT value in the control group was 2242.94 ± 25.80 Hu (P < 0.05). In the decalcified vertebrae, the screw pull-out force of the MP group was 48.6% lower than that of the control group (P < 0.05). The pull-out force of the AP trajectory was 44.7% higher than that of the MP trajectory (P < 0.05) and reached 97.4% of the MP trajectory in the control group (P > 0.05).

Conclusion

Automatic planning of the pedicle screw trajectory based on the CT value can obtain a higher screw pull-out force, which is a valuable new method of pedicle screw placement in osteoporotic vertebre.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05101-6.

Workflow and performance of intraoperative CT, cone-beam CT, and robotic cone-beam CT for spinal navigation in 503 consecutive patients

OBJECTIVE

A direct comparison of intraoperative CT (iCT), cone-beam CT (CBCT), and robotic cone-beam CT (rCBCT) has been necessary to identify the ideal imaging solution for each individual user's need. Herein, the authors sought to analyze workflow, handling, and performance of iCT, CBCT, and rCBCT imaging for navigated pedicle screw instrumentation across the entire spine performed within the same surgical environment by the same group of surgeons.

METHODS

Between 2014 and 2018, 503 consecutive patients received 2673 navigated pedicle screws using iCT (n = 1219), CBCT (n = 646), or rCBCT (n = 808) imaging during the first 24 months after the acquisition of each modality. Clinical and demographic data, workflow, handling, and screw assessment and accuracy were analyzed.

RESULTS

Intraoperative CT showed image quality and workflow advantages for cervicothoracic cases, obese patients, and long-segment instrumentation, whereas CBCT and rCBCT offered independent handling, around-the-clock availability, and the option of performing 2D fluoroscopy. All modalities permitted reliable intraoperative screw assessment. Navigated screw revision was possible with each modality and yielded final accuracy rates > 92% in all groups (iCT 96.2% vs CBCT 92.3%, p < 0.001) without a difference in the accuracy of cervical pedicle screw placement or the rate of secondary screw revision surgeries.

CONCLUSIONS

Continuous training and an individual setup of iCT, CBCT, and rCBCT has been shown to permit safe and precise navigated posterior instrumentation across the entire spine with reliable screw assessment and the option of immediate revision. The perceived higher image quality and larger scan area of iCT should be weighed against the around-the-clock availability of CBCT and rCBCT technology with the option of single-handed robotic image acquisition.

The lumbar region localization using bone anatomy feature graphs

The automatic localization of the lumbar region is essential for the diagnosis of lumbar diseases, the study of lumbar morphology, and the surgical planning. Although the existing researches have made great progress, it still faces several challenges. First, the various lumbar diseases and pathologies cause different abnormalities in the lumbar shape and appearance. Second, the numbers of lumbar vertebrae are irregular (some people have an additional vertebra L6). To tackle these challenges, we propose a novel lumbar region localization method based on bone anatomy feature graphs. Specifically, a feature graph (called LS) considering the anatomy of the sacrum and the lumbar vertebra is proposed to locate the inferior boundary of L5 or L6. A feature graph (called TL) considering the anatomy of the thoracic vertebra and the lumbar vertebra is proposed to locate the superior boundary of L1. Extensive experimental analysis is performed on a public available dataset xVertSeg and a private dataset which contains 197 CT scans. The localization results show that the proposed method is robust and can be applied to normal scans, scoliosis scans, deformity scans, hyperosteogeny scans, 6 lumbar vertebrae scans and lumbar implant scans. The Dice and Jaccard coefficients are 98.09 ± 0.84% and 96.27 ± 1.62% respectively. Graphical Abstract Lumbar Region Localization Framework.

Spinal alignment shift between supine and prone CT imaging occurs frequently and regardless of the anatomic region, risk factors, or pathology

Computer-assisted spine surgery based on preoperative CT imaging may be hampered by sagittal alignment shifts due to an intraoperative switch from supine to prone. In the present study, we systematically analyzed the occurrence and pattern of sagittal spinal alignment shift between corresponding preoperative (supine) and intraoperative (prone) CT imaging in patients that underwent navigated posterior instrumentation between 2014 and 2017. Sagittal alignment across the levels of instrumentation was determined according to the C2 fracture gap (C2-F) and C2 translation (C2-T) in odontoid type 2 fractures, next to the modified Cobb angle (CA), plumbline (PL), and translation (T) in subaxial pathologies. One-hundred and twenty-one patients (C1/C2: n = 17; C3-S1: n = 104) with degenerative (39/121; 32%), oncologic (35/121; 29%), traumatic (34/121; 28%), or infectious (13/121; 11%) pathologies were identified. In the subaxial spine, significant shift occurred in 104/104 (100%) cases (CA: *p = .044; T: *p = .021) compared to only 10/17 (59%) cases that exhibited shift at the C1/C2 level (C2-F: **p = .002; C2-T: *p < .016). The degree of shift was not affected by the anatomic region or pathology but significantly greater in cases with an instrumentation length > 5 segments (“∆PL > 5 segments”: 4.5 ± 1.8 mm; “∆PL ≤ 5 segments”: 2 ± 0.6 mm; *p = .013) or in revision surgery with pre-existing instrumentation (“∆PL presence”: 5 ± 2.6 mm; “∆PL absence”: 2.4 ± 0.7 mm; **p = .007). Interestingly, typical morphological instability risk factors did not influence the degree of shift. In conclusion, intraoperative spinal alignment shift due to a change in patient position should be considered as a cause for inaccuracy during computer-assisted spine surgery and when correcting spinal alignment according to parameters that were planned in other patient positions.

Heterotopic Ossification After Prestige-LP Cervical Disc Arthroplasty Is Related to Insufficient Sagittal Coverage of the Endplate By the Prosthesis

Background

Heterotopic ossification (HO) is a major complication after cervical disc arthroplasty (CDR) that has attracted the attention of spine surgeons. There remains a great deal of controversy regarding the surgical risk factors. The present study investigated the correlation between insufficient sagittal coverage of the prosthesis-endplate and HO after CDR and explored strategies to prevent it.

Material/Methods

We included 73 patients who underwent Prestige-LP arthroplasty. Patients were divided into HO and non-HO groups. Related data, including radiological, clinical information, were collected. HO was graded using the McAfee classification. Analysis was performed to correlate HO to the surgical segmental range of motion (ROM) at last follow-up. To evaluate the insufficient sagittal coverage of the prosthesis-endplate and other factors for developing HO, receiver operating characteristic (ROC) curves were analyzed for insufficient sagittal coverage.

Results

Among 73 patients, 24 patients had HO at the last follow-up (HO incidence: 32.9%). The ROM in the HO group was significantly lower (P<0.001). The insufficient sagittal coverage of the upper and lower prosthesis-endplate, the height of intervertebral space, and the preoperative and postoperative ROM were related to HO (P<0.05). Multivariate logistic regression analysis showed that only insufficient sagittal coverage of the upper prosthesis-endplate was related to HO (P=0.023), and ROC curve analysis revealed that HO was more likely to occur with insufficient sagittal coverage distance ≥2.5 mm.

Conclusions

HO after CDR causes a reduction in ROM, the occurrence of which is associated with insufficient sagittal coverage of the prosthesis-endplate. HO was more likely to occur with insufficient sagittal coverage distance ≥2.5 mm.

QUANTITATIVE ASSESSMENTS OF FIRMNESS AND AUTOMATIC OPTIMIZATION METHODS OF TRAJECTORY FOR PEDICLE SCREWS

Pedicle screw placement is a common internal fixation technology used in spine surgery, with preoperative planning and assessment being one of the most important steps. Preoperative planning mainly refers to determining the path and parameters of screws, and preoperative assessment mainly refers to effects during and after operations (i.e., firmness, etc.). Technologies available at present lack effective quantitative assessments on the firmness of screws. Bone mineral density (BMD) is one of the most important influencing factors for firmness. To address the aforementioned problems, this study aimed to put forward quantitative assessments for the firmness of pedicle screws taking bone mass as the basis. In other words, quantitative assessments of the firmness of screw trajectories were made by computing the total mineral content of the bone supporting screws. Meanwhile, the quantitative assessment results of the firmness were used as the optimized objective functions to put forward and realize an automatic planning optimization method for screw trajectories. The findings of this study might provide more complete and simplified planning schemes for doctors, to enhance the postoperative firmness of screws effectively, prevent from issues such as the loosening of screws due to the low value of a patient’s bone mass, and promote the effects of operations.

Use of the sagittal Cobb* angle to guide the rod bending in the treatment of thoracolumbar fractures: a retrospective clinical study

Background

Pedicle screw fixation is a well-established technique for thoracolumbar fracture. A large number of studies have shown that the bending angle of the connecting rod has a significant correlation with the postoperative spinal stability. However, no studies have confirmed an objective indicator to guide the bending angle of the connecting rod during the operation. Our study aims to define a sagittal Cobb* angle to guide the bending angle of the connecting rod during surgery.

Methods

The frontal and lateral X-ray films in 150 cases of normal thoracolumbar spine were included to measure the normal spinal sagittal Cobb* angle in each segment. The patients who underwent single segment thoracolumbar fractures and pedicle screw internal fixation surgery were included. The radiological parameters included lumbar lordosis (LL), thoracic kyphosis (TK), pelvic tilt (PT), pelvic incidence (PI), sagittal vertical axis (SVA), and sacral slope (SS) were measured. The incidence of adjacent segment degeneration (ASD) 2 years after surgery was measured.

Results

The average values of normal sagittal Cobb* angle in each segment were − 5.196 ± 3.318° (T12), 2.279 ± 3.324° (L1), 7.222 ± 2.798° (L2), and 12.417 ± 11.962° (L3), respectively. The LL in the three groups was 35.20 ± 9.12°, 46.26 ± 9.68°, and 54.24 ± 15.31°, respectively. Compared with the normal group, there were significant differences in group A and group C, respectively (p < 0.05). The results were similar in the parameters of TL, PT, and SS. The incidences of SVA > 50 mm in group A, group B, and group C were 23.33%, 12.50%, and 19.23%, respectively. The parameter of PI in three groups was 41.36 ± 12.69, 44.53 ± 15.27, and 43.38 ± 9.85°, respectively. The incidences of ASD in group A, group B, and group C 2 years after surgery were 21.67%, 13.75%, and 17.95%, respectively.

Conclusions

The study confirmed that the sagittal Cobb* angle can be used as a reference angle for bending rods. When the bending angle of the connecting rod is 4 to 8° greater than the corresponding segment sagittal Cobb* angle, the patient’s spinal sagittal stability is the best 2 years after the operation.

Surgical planning assistance in keyhole and percutaneous surgery: A systematic review

Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.

Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory

OBJECTIVE

Robotic spine surgery systems are increasingly used in the US market. As this technology gains traction, however, it is necessary to identify mechanisms that assess its effectiveness and allow for its continued improvement. One such mechanism is the development of a new 3D grading system that can serve as the foundation for error-based learning in robot systems. Herein the authors attempted 1) to define a system of providing accuracy data along all three pedicle screw placement axes, that is, cephalocaudal, mediolateral, and screw long axes; and 2) to use the grading system to evaluate the mean accuracy of thoracolumbar pedicle screws placed using a single commercially available robotic system.

METHODS

The authors retrospectively reviewed a prospectively maintained, IRB-approved database of patients at a single tertiary care center who had undergone instrumented fusion of the thoracic or lumbosacral spine using robotic assistance. Patients with preoperatively planned screw trajectories and postoperative CT studies were included in the final analysis. Screw accuracy was measured as the net deviation of the planned trajectory from the actual screw trajectory in the mediolateral, cephalocaudal, and screw long axes.

RESULTS

The authors identified 47 patients, 51% male, whose pedicles had been instrumented with a total of 254 screws (63 thoracic, 191 lumbosacral). The patients had a mean age of 61.1 years and a mean BMI of 30.0 kg/m2. The mean screw tip accuracies were 1.3 ± 1.3 mm, 1.2 ± 1.1 mm, and 2.6 ± 2.2 mm in the mediolateral, cephalocaudal, and screw long axes, respectively, for a net linear deviation of 3.6 ± 2.3 mm and net angular deviation of 3.6° ± 2.8°. According to the Gertzbein-Robbins grading system, 184 screws (72%) were classified as grade A and 70 screws (28%) as grade B. Placement of 100% of the screws was clinically acceptable.

CONCLUSIONS

The accuracy of the discussed robotic spine system is similar to that described for other surgical systems. Additionally, the authors outline a new method of grading screw placement accuracy that measures deviation in all three relevant axes. This grading system could provide the error signal necessary for unsupervised machine learning by robotic systems, which would in turn support continued improvement in instrumentation placement accuracy.

Automatic pedicle screw planning using atlas-based registration of anatomy and reference trajectories

An algorithm for automatic spinal pedicle screw planning is reported and evaluated in simulation and first clinical studies. A statistical atlas of the lumbar spine (N  =  40 members) was constructed for active shape model (ASM) registration of target vertebrae to an unsegmented patient CT. The atlas was augmented to include 'reference' trajectories through the pedicles as defined by a spinal neurosurgeon. Following ASM registration, the trajectories are transformed to the patient CT and accumulated to define a patient-specific screw trajectory, diameter, and length. The algorithm was evaluated in leave-one-out analysis (N  =  40 members) and for the first time in a clinical study (N  =  5 patients undergoing cone-beam CT (CBCT) guided spine surgery), and in simulated low-dose CBCT images. ASM registration achieved (2.0  ±  0.5) mm root-mean-square-error (RMSE) in surface registration in 96% of cases, with outliers owing to limitations in CT image quality (high noise/slice thickness). Trajectory centerlines were conformant to the pedicle in 95% of cases. For all non-breaching trajectories, automatically defined screw diameter and length were similarly conformant to the pedicle and vertebral body (98.7%, Grade A/B). The algorithm performed similarly in CBCT clinical studies (93% centerline and screw conformance) and was consistent at the lowest dose levels tested. Average runtime in planning five-level (lumbar) bilateral screws (ten trajectories) was (312.1  ±  104.0) s. The runtime per level for ASM registration was (41.2  ±  39.9) s, and the runtime per trajectory was (4.1  ±  0.8) s, suggesting a runtime of ~(45.3  ±  39.9) s with a more fully parallelized implementation. The algorithm demonstrated accurate, automatic definition of pedicle screw trajectories, diameter, and length in CT images of the spine without segmentation. The studies support translation to clinical studies in free-hand or robot-assisted spine surgery, quality assurance, and data analytics in which fast trajectory definition is a benefit to workflow.

Biomechanically driven intraoperative spine registration during navigated anterior vertebral body tethering

The integration of pre-operative biomechanical planning with intra-operative imaging for navigated corrective spine surgery may improve surgical outcomes, as well as the accuracy and safety of manoeuvres such as pedicle screw insertion and cable tethering, as these steps are performed empirically by the surgeon. However, registration of finite element models (FEMs) of the spine remains challenging due to changes in patient positioning and imaging modalities. The purpose of this study was to develop and validate a new method registering a preoperatively constructed patient-specific FEM aimed to plan and assist anterior vertebral body tethering (AVBT) of scoliotic patients, to intraoperative cone beam computed tomography (CBCT) during navigated AVBT procedures. Prior to surgery, the 3D reconstruction of the patient's spine was obtained using biplanar radiographs, from which a patient-specific FEM was derived. The surgical plan was generated by first simulating the standing to intraoperative decubitus position change, followed by the AVBT correction techniques. Intraoperatively, a CBCT was acquired and an automatic segmentation method generated the 3D model for a series of vertebrae. Following a rigid initialization, a multi-level registration simulation using the FEM and the targeted positions of the corresponding reconstructed vertebrae from the CBCT allows for the refinement of the alignment between modalities. The method was tested with 18 scoliotic cases with a mean thoracic Cobb angle of 47°  ±  7° having already undergone AVBT. The translation error of the registered FEM vertebrae to the segmented CBCT spine was 1.4  ±  1.2 mm, while the residual orientation error was 2.7°  ±  2.6°, 2.8°  ±  2.4° and 2.5°  ±  2.8° in the coronal, sagittal, and axial planes, respectively. The average surface-to-surface distance was 1.5  ±  1.2 mm. The proposed method is a first attempt to use a patient-specific biomechanical FEM for navigated AVBT, allowing to optimize surgical strategies and screw placement during surgery.

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebrae. This instance-by-instance segmentation is enabled by combining the network with a memory component that retains information about already segmented vertebrae. The network iteratively analyzes image patches, using information from both image and memory to search for the next vertebra. To efficiently traverse the image, we include the prior knowledge that the vertebrae are always located next to each other, which is used to follow the vertebral column. The network concurrently performs multiple tasks, which are segmentation of a vertebra, regression of its anatomical label and prediction whether the vertebra is completely visible in the image, which allows to exclude incompletely visible vertebrae from further analyses. The predicted anatomical labels of the individual vertebrae are additionally refined with a maximum likelihood approach, choosing the overall most likely labeling if all detected vertebrae are taken into account. This method was evaluated with five diverse datasets, including multiple modalities (CT and MR), various fields of view and coverages of different sections of the spine, and a particularly challenging set of low-dose chest CT scans. For vertebra segmentation, the average Dice score was 94.9 ± 2.1% with an average absolute symmetric surface distance of 0.2 ± 10.1mm. The anatomical identification had an accuracy of 93%, corresponding to a single case with mislabeled vertebrae. Vertebrae were classified as completely or incompletely visible with an accuracy of 97%. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible and generalizable.

Variability Analysis of Manual and Computer-Assisted Preoperative Thoracic Pedicle Screw Placement Planning

STUDY DESIGN

A comparison among preoperative pedicle screw placement plans, obtained from computed tomography (CT) images manually by two spine surgeons and automatically by a computer-assisted method.

OBJECTIVE

To analyze and compare the manual and computer-assisted approach to pedicle screw placement planning in terms of the inter- and intraobserver variability.

SUMMARY OF BACKGROUND DATA

Several methods for computer-assisted pedicle screw placement planning have been proposed; however, a systematic variability analysis against manual planning has not been performed yet.

METHODS

For 256 pedicle screws, preoperative placement plans were determined manually by two experienced spine surgeons, each independently performing two sets of measurements by using a dedicated software for surgery planning. For the same 256 pedicle screws, preoperative placement plans were also obtained automatically by a computer-assisted method that was based on modeling of the vertebral structures in 3D, which were used to determine the pedicle screw size and insertion trajectory by maximizing its fastening strength through the underlying bone mineral density.

RESULTS

A total of 1024 manually (2 observers × 2 sets × 256 screws) and 256 automatically (1 computer-assisted method × 256 screws) determined preoperative pedicle screw placement plans were obtained and compared in terms of the inter- and intraobserver variability. A large difference was observed for the pedicle screw sagittal inclination that was, in terms of the mean absolute difference and the corresponding standard deviation, equal to 18.3° ± 7.6° and 12.3° ± 6.5°, respectively for the intraobserver variability of the second observer and for the interobserver variability between the first observer and the computer-assisted method.

CONCLUSION

The interobserver variability among the observers and the computer-assisted method is within the intraobserver variability of each observer, which indicates on the potential use of the computer-assisted approach as a useful tool for spine surgery that can be adapted according to the preferences of the surgeon.

LEVEL OF EVIDENCE

3.

Robotic drill guide positioning using known-component 3D-2D image registration

A method for x-ray image-guided robotic instrument positioning is reported and evaluated in preclinical studies of spinal pedicle screw placement with the aim of improving delivery of transpedicle K-wires and screws. The known-component (KC) registration algorithm was used to register the three-dimensional patient CT and drill guide surface model to intraoperative two-dimensional radiographs. Resulting transformations, combined with offline hand-eye calibration, drive the robotically held drill guide to target trajectories defined in the preoperative CT. The method was assessed in comparison with a more conventional tracker-based approach, and robustness to clinically realistic errors was tested in phantom and cadaver. Deviations from planned trajectories were analyzed in terms of target registration error (TRE) at the tooltip (mm) and approach angle (deg). In phantom studies, the KC approach resulted in [Formula: see text] and [Formula: see text], comparable with accuracy in tracker-based approach. In cadaver studies with realistic anatomical deformation, the KC approach yielded [Formula: see text] and [Formula: see text], with statistically significant improvement versus tracker ([Formula: see text] and [Formula: see text]). Robustness to deformation is attributed to relatively local rigidity of anatomy in radiographic views. X-ray guidance offered accurate robotic positioning and could fit naturally within clinical workflow of fluoroscopically guided procedures.

Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration

Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). Atlas-based registration is used to calculate a patient-specific plan of screw trajectories in preoperative CT. 3D-2D registration aligns the patient to CT within the projective geometry of intraoperative fluoroscopy. Deformable known-component registration (dKC-Reg) localizes the surgical device, and the combination of plan and device location is used to provide guidance and QA. A leave-one-out analysis evaluated the accuracy of automatic planning, and a cadaver experiment compared the accuracy of dKC-Reg to rigid approaches (e.g. optical tracking). Surgical plans conformed within the bone cortex by 3-4 mm for the narrowest corridor (superior pubic ramus) and  >5 mm for the widest corridor (tear drop). The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4° and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial-and-error with extended fluoroscopy time.