Overview of Methods to Quantify Invasiveness of Surgical Approaches in Orthopedic Surgery-A Scoping Review

BACKGROUND: There is a trend toward minimally invasive and more automated procedures in orthopedic surgery. An important aspect in the further development of these techniques is the quantitative assessment of the surgical approach. The aim of this scoping review is to deliver a structured overview on the currently used methods for quantitative analysis of a surgical approaches' invasiveness in orthopedic procedures. The compiled metrics presented in the herein study can serve as the basis for digitization of surgery and advanced computational methods that focus on optimizing surgical procedures. METHODS: We performed a blinded literature search in November 2020. In-vivo and ex-vivo studies that quantitatively assess the invasiveness of the surgical approach were included with a special focus on radiological methods. We excluded studies using exclusively one or multiple of the following parameters: risk of reoperation, risk of dislocation, risk of infection, risk of patient-reported nerve injury, rate of thromboembolic event, function, length of stay, blood loss, pain, operation time. RESULTS: The final selection included 51 articles. In the included papers, approaches to 8 different anatomical structures were investigated, the majority of which examined procedures of the hip (57%) and the spine (29%). The different modalities to measure the invasiveness were categorized into three major groups "biological" (23 papers), "radiological" (25), "measured in-situ" (14) and their use "in-vivo" or "ex-vivo" was analyzed. Additionally, we explain the basic principles of each modality and match it to the anatomical structures it has been used on. DISCUSSION: An ideal metric used to quantify the invasiveness of a surgical approach should be accurate, cost-effective, non-invasive, comprehensive and integratable into the clinical workflow. We find that the radiological methods best meet such criteria. However, radiological metrics can be more prone to confounders such as coexisting pathologies than in-situ measurements but are non-invasive and possible to perform in-vivo. Additionally, radiological metrics require substantial expertise and are not cost-effective. Owed to their high accuracy and low invasiveness, radiological methods are, in our opinion, the best suited for computational applications optimizing surgical procedures. The key to quantify a surgical approach's invasiveness lies in the integration of multiple metrics

X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients' lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow

The winking sign is an indicator for increased femorotibial rotation in patients with recurrent patellar instability

Purpose: Rotation of the tibia relative to the femur was recently identified as a contributing risk factor for patellar instability, and correlated with its severity. The hypothesis was that in patellofemoral dysplastic knees, an increase in femorotibial rotation can be reliably detected on anteroposterior (AP) radiographs by an overlap of the lateral femoral condyle over the lateral tibial eminence. Methods: Sixty patients (77 knees) received low-dose computed tomography (CT) of the lower extremity for assessment of torsional malalignment due to recurrent patellofemoral instability. Three-dimensional (3D) surface models were created to assess femorotibial rotation and its relationship to other morphologic risk factors of patellofemoral instability. On weight-bearing AP knee radiographs, a femoral condyle/lateral tibial eminence superimposition was defined as a positive winking sign. Using digitally reconstructed radiographs of the 3D models, susceptibility of the winking sign to vertical/horizontal AP knee radiograph malrotation was investigated. Results: A positive winking sign was present in 30/77 knees (39.0%) and indicated a 6.3 ± 1.4° increase in femorotibial rotation (p 15°) with 43% sensitivity and 90% specificity (AUC = 0.72; p = 0.002). A positive winking sign (with 2 mm overlap) disappeared in case of a 10° horizontally or 15° vertically malrotated radiograph, whereas a 4 mm overlap did not disappear at all, regardless of the quality of the radiograph. In absence of a winking sign, on the other hand, no superimposition resulted within 20° of vertical/horizontal image malrotation. Femorotibial rotation was positively correlated to TT-TG (R2 = 0.40, p = 0.001) and patellar tilt (R2 = 0.30, p = 0.001). Conclusions: The winking sign reliably indicates an increased femorotibial rotation on a weight-bearing AP knee radiograph and could prove useful for day-by-day clinical work. Future research needs to investigate whether femorotibial rotation is not only a prognostic factor but a potential surgical target in patients with patellofemoral disorders. Level of evidence: III. Keywords: Femorotibial rotation; Knee rotation; Patellar instability; Winking sig

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques

Photogrammetric Advances to C-arm Use in Surgery

C-arms are commonly used during orthopaedic surgeries to provide real-time static or dynamic fluoroscopic imaging. These devices are mostly utilized for qualitative assessment during operations; several advancements, such as C-arm tracking, must be accomplished to make them capable of providing quantitative measurements. This thesis presents in two major contributions to C-arm quantification: (1) development and testing of a monocular visual odometry method to track the C-arm base and (2) development and testing of a particular application, estimating the pose of an intramedullary (IM) nail for fracture surgery. The proposed base-tracking system can either be integrated with a C-arm joint tracking module or employed on its own, e.g. for steering. An IM-nail pose estimation method is proposed in this research that is capable of reporting the position and orientation of an inserted IM-nail. The offered IM-nail pose estimation method can help reduce both radiation exposure and time during surgery.2 year

An intraoperative position assessment system for pedicle screw insertion surgeries

Pedicle screw fixation is a common practice in spinal surgeries. Metal implants are inserted into the structure of interest (pedicles) to provide stabilization. The major surgical challenge is to ensure that the implants are within a narrow safe zone otherwise serious postoperative complications or potential revision surgeries can occur. Currently, implant malplacement rates are rather high resulting in a considerable rate of revision surgery associated with this operation. The most common technique for verifying the position of the inserted implants during the surgery is to acquire intraoperative X-rays and visually assess these two dimensional images to estimate the three-dimensional position of each implant. This is an erroneous process due to the lack of the third dimension and can result in misjudgment. In this thesis, we developed an accurate and automated system for intraoperative post-placement) pedicle screw position assessment. To this end, we first designed a semi-automatic pipeline that consisted of the main computational modules required for such a system and evaluated its performance in identifying screw breaches. After demonstrating satisfactory performance (average root mean square error better than 0.8 mm and 1.3 degrees), we focused on improving the autonomy of the system by incorporating more advanced processing modules with the aim of reducing interference with the existing surgical workflow and ultimately facilitating clinical adoption. For this purpose, we developed automatic and application-specific approaches for the following system modules: intraoperative X-ray calibration (average error 0.4 mm), spinal 2D-3D registration (capture range for a two view setup ∼ 12.5 mm and 31 degrees), implant segmentation and pose estimation (errors less than 2 degrees and 2 mm), X-ray inpainting (up to 85% increase in registration capture range) and preoperative pedicle region localization (on average 90% accuracy), which all helped us in achieving the end goal of system autonomy. The performance of individual modules were validated on clinical or clinically realistic data. This system can potentially improve the ability of a surgeon to assess pedicle breaches intraoperatively and decide whether or not a screw needs to be repositioned before closing the patient; therefore, potentially reducing the current rate of revision surgeries.Applied Science, Faculty ofBiomedical Engineering, School ofGraduat

X23D—Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients’ lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow

An Engineers-in-Scrubs Design Project

International Heat, a team of five graduate students, spent the past four months designing a thermal cautery system capable of being used for open abdominal surgery in low-resource settings. Throughout those four months, the team followed the design process outlined in Biodesign: The Process of Innovating Medical Technologies. Potential project ideas were presented to the entire Engineers-in-Scrubs class. International Heat formed from a desire to create a device with the potential to significantly impact the quality of medical care in third world countries. This report details the team’s progression starting with defining the team’s mission statement and strategic focus, to the needs finding process, to needs screening and creation of preliminary needs specifications, to concept generation and screening, to interviewing clinicians who provided feedback leading to the update of the needs statement and needs specifications, and then final concept selection. We describe the prototyping of and evaluation of two separate concepts in order to ultimately choose thermal cautery as well as extensive feasibility testing we performed to see if thermal cautery had the potential to be a viable alternative to an electrosurgical unit. Finally, we provide a potential development strategy which could be implemented to bring the thermal cautery unit to market.Applied Science, Faculty ofMechanical Engineering, Department ofUnreviewedGraduat

Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions

In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions.ISSN:2313-433

Introducing a brain-computer interface to facilitate intraoperative medical imaging control – a feasibility study

Abstract Background Safe and accurate execution of surgeries to date mainly rely on preoperative plans generated based on preoperative imaging. Frequent intraoperative interaction with such patient images during the intervention is needed, which is currently a cumbersome process given that such images are generally displayed on peripheral two-dimensional (2D) monitors and controlled through interface devices that are outside the sterile filed. This study proposes a new medical image control concept based on a Brain Computer Interface (BCI) that allows for hands-free and direct image manipulation without relying on gesture recognition methods or voice commands. Method A software environment was designed for displaying three-dimensional (3D) patient images onto external monitors, with the functionality of hands-free image manipulation based on the user’s brain signals detected by the BCI device (i.e., visually evoked signals). In a user study, ten orthopedic surgeons completed a series of standardized image manipulation tasks to navigate and locate predefined 3D points in a Computer Tomography (CT) image using the developed interface. Accuracy was assessed as the mean error between the predefined locations (ground truth) and the navigated locations by the surgeons. All surgeons rated the performance and potential intraoperative usability in a standardized survey using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Results When using the developed interface, the mean image control error was 15.51 mm (SD: 9.57). The user's acceptance was rated with a Likert score of 4.07 (SD: 0.96) while the overall impressions of the interface was rated as 3.77 (SD: 1.02) by the users. We observed a significant correlation between the users' overall impression and the calibration score they achieved. Conclusions The use of the developed BCI, that allowed for a purely brain-guided medical image control, yielded promising results, and showed its potential for future intraoperative applications. The major limitation to overcome was noted as the interaction delay