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

Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography

Achieving precise pedicle screw placement in posterior lumbar interbody fusion (PLIF) is essential but difficult due to the intricacies of manual preoperative planning with CT scans. We analyzed CT data from 316 PLIF patients, using Mimics software for manual planning by two surgeons. A deep learning model was trained on 228 patients and validated on 88 patients, assessing planning efficiency and accuracy. Automatic planning successfully segmented and placed screws in all 316 cases, significantly outperforming manual planning in speed. The Dice coefficient for segmentation accuracy was 0.95. The difference in mean pedicle transverse angle (PTA) and pedicle sagittal angle (PSA) for automatic planning screws compared to manual planning screws was 1.63 ± 0.83° and 1.39 ± 1.03°, respectively, and these differences were either statistically comparable or not significantly different compared to the variability of manual planning screws. The average Dice coefficient of implanted screws was 0.63 ± 0.08, and the consistency between automatic screws and manual reference screws was higher than that of internal screws (Dice 0.62 ± 0.09). Compared with manual screws, automatic screws were shorter (46.58 ± 3.09 mm) and thinner (6.24 ± 0.35 mm), and the difference was statistically significant. In qualitative validation, 97.7% of the automatic planning screws were rated Gertzbein-Robbins (GR) Class A and 97.3% of the automatic planning screws were rated Badu Class 0. Deep learning software automates lumbosacral pedicle screw planning, enhancing surgical efficiency and accuracy.

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning

PURPOSE

Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.

METHODS

We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient's computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning. Six volunteers planned pedicle screws' position and orientation with the AR system and on a 2D desktop planner. We compared the placement accuracy of each screw with both methods. Finally, we administered a questionnaire to all participants to assess their experience with the AR system.

RESULTS

The latency in message exchange is sufficiently low to enable real-time communication between the platforms. The AR method was non-inferior to the 2D desktop planner, with a mean error of 2.1 ± 1.4 mm. Moreover, 98% of the screw placements performed with the AR system were successful, according to the Gertzbein-Robbins scale. The average questionnaire outcomes were 4.5/5.

CONCLUSIONS

Real-time communication between Microsoft HoloLens 2 and 3D Slicer is feasible and supports accurate planning for pedicle screw placement.

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.

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.

The Surgical Treatment of Osteoarthritis

Osteoarthritis is a degenerative condition affecting the whole joint with the underlying bone, representing a major source of pain, disability, and socioeconomic cost worldwide. Age is considered the strongest risk factor, albeit abnormal biomechanics, morphology, congenital abnormality, deformity, malalignment, limb-length discrepancy, lifestyle, and injury may further increase the risk of the development and progression of osteoarthritis as well. Pain and loss of function are the main clinical features that lead to treatment. Although early manifestations of osteoarthritis are amenable to lifestyle modification, adequate pain management, and physical therapy, disease advancement frequently requires surgical treatment. The symptomatic progression of osteoarthritis with radiographical confirmation can be addressed either with arthroscopic interventions, (joint) preservation techniques, or bone fusion procedures, whereas (joint) replacement is preferentially reserved for severe and end-stage disease. The surgical treatment aims at alleviating pain and disability while restoring native biomechanics. Miscellaneous surgical techniques for addressing osteoarthritis exist. Advanced computer-integrated surgical concepts allow for patient personalization and optimization of surgical treatment. The scope of this article is to present an overview of the fundamentals of conventional surgical treatment options for osteoarthritis of the human skeleton, with emphasis on arthroscopy, preservation, arthrodesis, and replacement. Contemporary computer-assisted orthopaedic surgery concepts are further elucidated.

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.

Under viral attack: An orthopaedic response to challenges faced by regional referral centres during a national cyber-attack

BACKGROUND

A national ransomware attack on the Irish Health Service Executive left the Healthcare system bereft of access to IT systems, electronic patient records, and the national imaging system. Widespread disruption to internal and external referral pathways, and both trauma and elective Orthopaedic services occurred as a result. The purpose of this paper to discuss the challenges faced by Regional trauma units and adjustments made to overcome these.

METHODS

Issues occurring as a result of the IT cybersecurity attack were discussed at regional level. Local and specialist centre adaptations were collated to identify effective modifications to established practice in the wake of the IT attack.

RESULTS

The main areas affecting Orthopaedic regional practice were identified, including internal referrals, interhospital referrals to both regional and specialist centres, outpatient clinics, and elective practice. Strategies to overcome these were collated and shared between regional centres, including the use of secure messaging systems to safely transmit relevant clinical information between services, use of radiological hard copies, and integration of imaging resources to the outpatient department to expedite clinical review.

CONCLUSION

The national cyberattack necessitated rapid adaptations to overcome the challenges faced as a result of reduced clinical and radiological access. While the recent cyberattack highlights the vulnerability of electronic systems, and the need for vigilance including staff training on cybersecurity; Changes implemented by regional centres also illustrate the potential for further development and expansion of current clinical practices.

Selective cement augmentation of cranial and caudal pedicle screws provides comparable stability to augmentation on all segments in the osteoporotic spine: a finite element analysis

Background

Cement-augmented pedicle screw instrumentation (CAPSI) has been proven to significantly increase the biomechanical stability in the osteoporotic lumbar spine. However, besides the merits, it is responsible for the inevitable cement leakage growing with more instrumented segments and volumes involved. This study aimed to compare the biomechanical performance of pedicle screws augmented on all segments with those augmented only on the cranial and caudal vertebrae selectively.

Methods

The finite element model of L3-S1 was modeled with the CT data of a healthy volunteer, the solid/fenestrated pedicle screws from micro-CT scans of physical screws, and bone cement from the CT scans of a postoperative patient with CAPSI. Three different augmented strategies for pedicle screws were taken into consideration: augmentation at each pedicle trajectory (Model A), selective augmentation at the cranial and caudal pedicle trajectories (Model B), and pedicle trajectories without augmentation (Model C). A total of six surgical models were constructed: Models A, B, and C were subdivided into double segmental fusion from L4 to S1 (Models A1, B1, and C1) and multi-segment fusion from L3 to S1 (Models A2, B2, and C2). The Range of motion (ROM), stress on the cage, and stress on the fixed segments were compared among the six models.

Results

The ROM at the fusion segments decreased in all instrumentation models. The ROMs of Model B and Model A are similar in each direction, while that of Model C is significantly larger. The differences in the ROMs between Model A and Model B were noted to be less than 0.1°. Compared with Models A1 and A2, the peak Von Mise stress on the cage-endplate interface and pedicle screws were slightly higher in Models B1 and B2. In contrast, the stress of Models C1 and C2 increased significantly. The compressive stress was concentrated in the screw head, the cranial and caudal screws, and rods.

Conclusions

The selective augmentation of pedicle screws is capable of providing reliable stability in short-segment posterior fixation (2- or 3-level). It could be a potential optimal procedure to minimize the associated complications of CAPSI.

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.

SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality

Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.