bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patients Specific Spine Simulators for Surgical Training and Rehearsal in Pedicle Screws Placement: A New Way for Surgical Education
Authors: Parchi Paolo Domenico , Condino Sara , Carbone Marina , Stagnari Sara , Rocchi David , Colangeli Simone , Ferrari Mauro , Scaglione Michelangelo , Ferrari Vincenzo 
Published: September 2020

In pedicle screws placement using a free-hand technique or a fluoroscopic guided technique the main difficulties are facing to the bone morphology (i.e in deformity cases) and it could be easily reproduced in a patient’s specific spine simulator (we can choose the case). The aim of this work is to evaluate the use of 3D printed patient- specific models (3D printing) not only as a surgical planning tool but also as a surgical training tool in spine surgery and in particular in pedicle screws placement. The manufacturing of patient-specific physical replica involves the elaboration of CT dataset and rapid prototyping techniques. . Five resident surgeons were involved in different training sessions on simulators. To evaluate the exact screws position weperformed a CT evaluation of each instrumented simulators. Statistical analysis was conducted using SPSS software. A total of 120 pedicle screws were positioned, 90 screws were well-positioned and 30 screws were bad-positioned. There were a significant difference (p = 0.000008) between the bad-positioning screw rate of the “senior” resident (13/72) and those of “young” participants (17/48). Timeline analysis of pedicle instrumentation training showed the presence of a learning effect, with a lower error rate in the latest session (p=000001). We believe that the use of patient- specific surgical simulators, especially for those surgical tasks in which the complexity is mainly linked to the spine morphology (i.e. deformity), may represent a valid alternative to the use of cadavers that generally present a standard or otherwise poorly predictable anatomy.
Outcome Validation of a Simulation Based Patient Specific TKA Planning Tool
Authors: Twiggs Joshua , Roe Justin , Fritsch Brett , Parker David , Miles Brad 
Published: September 2020

Dynamic knee computer simulations are a promising surgical planning option in TKA, allowing the impact of plan alterations on joint dynamics to be analysed prior to surgery. Previously, the dynamic results of our simulation have been shown to correlate with outcome; here we show validation of its use in pre-operative planning.A database of TKA Patients undergoing surgery from 1-Jan-2014 operated on by 9 surgeons, who received a pre-operative and post-operative CT were assessed. A musculoskeletal computational model with similar boundary conditions to the Oxford Knee Rig was used to simulate post-TKA knee dynamics using Adams MSC software (Newport, CA). In addition, a set of pre-operative simulations were generated covering positional variations. The Dynamic Knee Score (DKS), a predictive algorithm machine learned from KOOS scored postoperative cases to predict outcome in preoperative planning was applied to all simulations.Patients were split into groups depending on whether the ‘post-operative achieved position’ was the ‘best’ of the preoperative modelled options in terms of simulated DKS score or not. These results were compared with 12 month postoperative KOOS scores. Cases where the best plan was followed had better outcome results. A relationship was shown with the KOOS Pain subscore, with the portion of patients below a KOOS Pain score of 70 dropping to 11% from 16% (p=0.030) when the best plan was followed.This study shows significant relationships between selection of patient specific kinematically optimal surgical plan and outcome. Such tools will play an important role in future patient specific decision making.
Statistical Analysis of Sacral Bone Density using a CNN-based Atlas Creation on a Large-scale CT database
Authors: Kawasaki Akihiro , Otake Yoshito , Uemura Keisuke , Takao Masaki , Sugano Nobuhiko , Sato Yoshinobu 
Published: September 2020

Fragility fracture of the sacrum has been an issue for elderly people. Research has been limited for the sacrum partly because its shape is complex with large inter-subject variation. Also, large-scale statistical analysis of its shape and density distribution has been limited mainly due to the computational load in establishment of the voxel correspondences, i.e., deformable registration.In this study, we employed a convolutional neural network (CNN)-based deformable registration algorithm in the analysis of the sacral bone. The algorithm we employed, VoxelMorph (Dalca et al. Med Image Anal 2019), is characterized as an unsupervised algorithm where no ground truth deformation field is required. The algorithm also allows to create a conditional deformable template, which is a volume image exhibiting smallest deformation field from all samples with specific attributes (e.g., age, sex, etc.), which in short represents the “average” (or “centroid”) image among the specific age and sex group. We applied it on a database consisting of 837 CTs (149 males, 688 females, 58.14 ± 14.73 y.o.) of the pelvis region, where the sacral bone was segmented and masked. We computed the templates corresponding to ages of 20 to 80 for male and female. The templates visually illustrated reduction of the bone density with aging in both male and female. The quantitative analysis showed that the average CT value over the sacrum region was reduced from 135.73 HU to 39.88 HU for 20 y.o. template to 80 y.o. template.
Evaluation of Motion Compensation Dynamics of the Handheld Robot MINARO HD
Authors: Vossel Manuel , Radermacher Klaus , de La Fuente Matias 
Published: September 2020

Current surgical robotic systems consist either of a large serial arm, resulting in higher risks due to their high inertia and no inherent limitations of the working space, or they are bone-mounted, adding substantial additional task steps to the surgical workflow.The robot presented in this paper has a handy and lightweight design and can be easily held by the surgeon. No rigid fixation to the bone or a cart is necessary. A high-speed tracking camera together with a fast control system ensure the accurate positioning of the burring tool, while automatically compensating for movements of the surgeon or the patient’s bone.To evaluate the motion compensation capabilities of the developed robot, an experiment was conducted in which movements of the patient were simulated on the robot’s real time control system and the actual robot had to follow those virtual movements. The positioning error, measured with a tracking camera, was recorded for different velocities of the disturbing movement.A linear relation between the positioning error and the speed of the disturbing motion could be discovered, with a slope of 24 ms and an offset of 0.044 mm.The slope can be interpreted as the latency of the robotic system while following a moving target. Therefore, with a measured latency of 24 ms, the developed robotic system should easily be able to compensate for the patient’s breathing as well as the tremor of the operator, whereas the latter can reach frequencies of up to 12 Hz according to literature.
Towards Automated Tissue Classification for Markerless Orthopaedic Robotic Assistance
Authors: Laws Stephen , Souipas Spyridon , Davies Brian , Rodriguez Y Baena Ferdinando 
Published: September 2020

A markerless computer aided orthopaedic platform will require a complex computer vision system to isolate and track rigid bodies used to localize a robot to a patient. Isolating rigid bodies such as bone requires accurate segmentation and this study explores using diffuse laser reflectivity to accurately classify tissue.Lasers (Red, 650nm and IR, 850nm) intersected four material types; cartilage, ligament, muscle and metal surgical tools within a controlled cadaveric setup. Images were captured with an infrared CMOS sensor, pre-processed to isolate laser centres, and resized to test information requirements. Images for both laser types were scaled from 5x5 pixels to 30x30 pixels and trained on a convolutional neural network, GoogLeNet.At sizes above 15x15 pixels the IR laser had a higher classification accuracy reaching 97.8% at 30x30 pixels, whereas the red laser peaked at 94.1%. It was shown as not possible to qualitatively identify materials that were not trained in the network based on their probability outputs. Further work will be done to classify multiple points in a single scene as a step toward segmenting entire surgical views for markerless CAOS systems.
Preliminary Validation of a Patient Outcome Prediction Tool Relative to Surgeon Predictions of Patient Outcome
Authors: Twiggs Joshua , Liu David , Roe Justin , Parker David , Miles Brad 
Published: September 2020

Currently, patient selection for Total Knee Arthroplasties (TKA) is surgeon specific. A combination of patient reported symptoms, clinical examination findings, and radiological criteria are combined in an idiosyncratic fashion based on the surgeon’s individual clinical experiences. Predictive models offer an alternative by providing more detailed information on expected patient outcome. This study presents validation relative to surgeon predictions of such a predictive model, the Patient Expectation Management (PEM) tool.A cohort of patients undertook a survey covering the KOOS questionnaire and a number of other questions pertaining to comorbidities prior to their consultation. From this survey, a prediction of final state and assessment of current state was generated. Prior to seeing the prediction but after consulting the patient, surgeons were asked to score out of 100 a) their understanding of the patient’s current pain state and b) their prediction of the patient’s pain level following surgery. 35 of the patients were selected for TKA surgery and have gone on to have 12 month Knee Osteoarthritis and Outcome Scores (KOOS) captured.The predicted change in the PEM predicted score (preop to postop difference) had a relatively high correlation with the actual KOOS pain improvement achieved (r=0.71, p&lt;0.001), compared to no significant correlation for the surgeon prediction (r=0.24, p=0.20). Significant correlations also existed for changes in KOOS symptoms score (r=0.70, p&lt;0.001) and KOOS Activities of Daily Living (ADL) score (r=0.42, p=0.02).This study showed that, compared to a set of surgeon predictions of outcome following a consultation with patients, a predictive analytics tool was able to outperform in terms of predicting the improvement patients are likely to report following TKA.
Femoral Component Alignment Boundaries for Tibia First Gap Balancing using Digital Tensioning Tool
Authors: Wakelin Edgar , Shalhoub Sami , Lawrence Jeffrey , Keggi John , DeClaire Jeffrey , Randall Amber , Ponder Corey , Koenig Jan , Plaskos Christopher 
Published: September 2020

Achieving a balanced knee is a critical aspect of Total Knee Arthroplasty (TKA). Coronal and axial boundaries for femoral component placement to achieve balance however, are not well defined. Our aim is to investigate the effect of femoral component and long leg coronal and axial alignment on patient outcomes when using a tibia-first gap balancing technique.All surgeries were performed using the OMNIBotics robot-assisted TKA platform and BalanceBot device. A total of 197 patients were prospectively enrolled into this study and received TKA surgery using the OMNIBotics platform and completed 1-year KOOS outcome scores. Femoral component and tibiofemoral alignment were categorized as inliers or outliers in the coronal and axial planes. Knee Injury and Osteoarthritis Outcome Score (KOOS), and University of California at Los Angeles Activity Scale (UCLA) was collected at 1-year post-op.No significant differences were found between the KOOS subscores or UCLA outcome and femoral coronal or tibiofemoral coronal and axial alignment. Significant differences were found between the KOOS pain and sports sub-scores and femoral axial alignment (∆ = 5.4, p = 0.007, ∆ = 8.3, p = 0.03 respectively), in which outlier femoral rotation reported higher scores.Component alignment limits for improved survival and patient outcomes are a source of ongoing debate. The data presented here indicates that when utilizing a tibia-first gap balancing technique, small deviations outside of traditional ±3°alignment boundaries did not negatively affect KOOS or UCLA outcomes, indicating balance may have a stronger link to patient outcome than alignment.
Robotic-Assisted THA Can Achieve Precise Cup Positioning Even in Severe Developmental Dysplasia of Hip
Authors: Hayashi Shinya , Hashimoto Shingo , Takayama Koji , Matsumoto Tomoyuki , Shibanuma Nao , Ishida Kazunari , Kuroda Ryosuke 
Published: September 2020

IntroductionWe hypothesize robotic-assisted THA can achieve precise cup positioning. The aim of this study is to figure out the accuracy of cup placement of severe DDH cases by using robotic-assisted THAMethodsThis was a prospective cohort study. The study analyzed a consecutive series of 53 hips with robotic-assisted THA between Aug 2018 and Sep 2019. Fifteen patients were DDH cases, and classified Crowe type II- 7 patients, type III- 7 patients, and type IV- 1 patient. All patients underwent robotic assisted-THA (MAKO Rio Robot, Ft. Lauderdale, FL) for osteoarthritis via the Hardinge or posterior approach in the lateral position. TTo analyze the accuracy of intraoperative navigation records for cup inclination and anteversion, we compared the intraoperative cup angles using the navigation records with the postoperative angles using postoperative CT data.ResultsReproducibility of robotic-assisted THA for preoperative planIn non DDH cases, the average measurement absolute error (postoperative CT-preoperative target) was 1.9 ± 2.1° (inclination) and 2.0 ± 2.5° (anteversion) (Figure 1). In DDH cases, the average measurement absolute error (postoperative CT- preoperative target) was 2.2 ± 3.1° (inclination) and 2.6 ± 2.4° (anteversion) (Figure 1). There were no differences of the average measurement absolute error between DDH and non DDH cases.Accuracy of navigation record for cup inclination and anteversion anglesIn non DDH cases, the average measurement absolute error (postoperative CT-navigation record) was 2.3 ± 1.9° (inclination) and 2.2 ± 1.5° (anteversion) (Figure 1). In DDH cases, the average measurement absolute error (postoperative CT-navigation record) was 2.0 ± 2.5° (inclination) and 2.3 ± 2.7° (anteversion) (Figure 1). There were no differences of the average measurement absolute error between DDH and non DDH cases. Scatter plots demonstrated the measurement error of both inclination and anteversion within 5° was 89.5% in non DDH cases (Figure 2a) and 80% in DDH cases (Figure 2b).DiscussionIn this study, we demonstrated that no differences of the average measurement absolute navigation record error or the absolute difference between target angle and postoperative cup alignment were found between severe DDH and non-DDH cases. Our results indicate that this robotic-assisted system has high accuracy for cup placement even in severe DDH case.
Effect of GAN-based image standardization on MR knee bone-tissue classification performance
Authors: Jaouen Vincent , Dardenne Guillaume , Tixier Florent , Stindel Éric , Visvikis Dimitris 
Published: September 2020

Due to their sensitivity to acquisition parameters, medical images such as magnetic resonance images (MRI), Positron Emission tomography (PET) or Computed Tomography (CT) images often suffer from a kind of variability unrelated to diagnostic power, often known as the center effect (CE). This is especially true in MRI, where units are arbitrary and image values can strongly depend on subtle variations in the pulse sequences [1]. Due to the CE it is particularly difficult in various medical imaging applications to 1) pool data coming from several centers or 2) train machine learning algorithms requiring large homogeneous training sets. There is therefore a clear need for image standardization techniques aiming at reducing this effect.Considerable improvements in image synthesis have been achieved over the recent years using (deep) machine learning. Models based on generative adversarial neural networks (GANs) now enable the generation of high definition images capable of fooling the human eye [2]. These methods are being increasingly used in medical imaging for various cross-modality (image-to-image) applications such as MR to CT synthesis [3]. However, they have been seldom used for the purpose of image standardization, i.e. for reducing the CE [4].In this work, we explore the potential advantage of embedding a standardization step using GANs prior to knee bone tissue classification in MRI. We consider image standardization as a within-domain image synthesis problem, where our objective is to learn a mapping between a domain D constituted of heterogeneous images and a reference domain R showing one or several images of desired image characteristics.Preliminary results suggest a beneficial impact of such a standardization step on segmentation performance.
Predicting Cementless Stem Fixation in Total Hip Arthroplasty Using Radiodensity Mapping
Authors: Cherukuri Teja , Madurawe Chameka , Pierrepont Jim , Bare Jonathan , McMahon Stephen , Shimmin Andrew 
Published: September 2020

Femoral component loosening due to poor fixation is a common failure mode in cementless total hip arthroplasty (THA). We sought to develop a technique to predicted femoral stem fixation using pre-operative CT.Forty-nine patients requiring THA received pre-operative CTs as part of Corin OPSTM 3D dynamic planning. All patients received a taper wedged blade stem (TriFitTM TS, Corin UK) implanted through a posterior approach. Post-operatively, patients received an immediate CT and AP x-ray and a 1-year follow-up x-ray. The distance between the most superior point of the greater trochanter and the shoulder of the stem was measured and scaled using the known cup diameter on both the immediate and 1- year follow-up x-rays. The difference was recorded as stem subsidence. Subsidence greater than 4mm was deemed clinically relevant. The precise placement of the stem determined by registering the known 3D implant geometry to the post-op CT was virtually implanted back into the pre-operative OPSTM planning software. A colour map of the bone density at the stem/bone interface using the Hounsfield Units (HU) of each CT pixel was generated. Blue represents low density bone transitioning through to green and then red.Mean stem subsidence was 1.8mm (0 to 11.1mm). Five patients had clinically relevant subsidence. Significant areas of blue and green around the proximal portion of the stem was observed in high subsidence stems when compared to the stems with minimal subsidence.Using the HU of the CT scan as an indicator for bone density, we were able to predict poor implant fixation and subsequent subsidence in a taper wedge stem.
Frequency of soft-tissue releases and their effect on patient reported outcomes in robotic-assisted TKA.
Authors: Plaskos Christopher , Wakelin Edgar , Shalhoub Sami , Lawrence Jeffrey , Keggi John , Koenig Jan , Ponder Corey , DeClaire Jeffrey 
Published: September 2020

Soft tissue releases are often required to correct deformity and achieve balance in total knee arthroplasty (TKA). However, releasing soft tissues can be subjective, highly variable and is perceived as an ‘art’ in TKA. The objective of this study was to compare the rate of soft tissue release required to achieve a balanced knee in tibial-first gap- balancing versus conventional, measured resection TKA, and its effect on outcomes.Soft tissue releases were documented and reviewed in 1256 robotic-assisted gap- balancing and 85 robotic-assisted measured-resection TKAs. Knees were stratified by coronal deformity (varus: &gt;2° varus; valgus: &gt;2° valgus). Rates of releases were compared between the two groups and literature. A subset of these patients were also enrolled in a prospective study. KOOS outcomes were captured pre-operatively and at 6M post TKA.The frequency of soft tissue release was significantly lower in the robotic gap- balancing group, with 21% of knees requiring release versus 40% (p=0.001) in the robotic measured resection group and 67% (p&lt;0.001) for conventional measured resection. Pre-operative KOOS scores were similar between groups, however 6M scores showed a significant improvement in QOL, Sports and Symptoms scores in knees not released.Robotic assisted TKA with predictive gap balancing was found to reduce the number of releases across all coronal angles compared to conventional instruments. Furthermore, performing a soft tissue release rather than bone resection to achieve balance, correlated with worse outcomes. Further research is required to understand when imbalance should be corrected with bone resection adjustment versus soft tissue release.
The Effect of Coronal and Axial Femoral Component Rotation on Midflexion Laxity and Patient Reported Outcomes in Total Knee Arthroplasty
Authors: Wakelin Edgar , Shalhoub Sami , Lawrence Jeffrey , Keggi John , Randall Amber , Ponder Corey , DeClaire Jeffrey , Koenig Jan , Plakos Christopher 
Published: September 2020

Joint balance in total knee arthroplasty (TKA) has traditionally focused on achieving a tight symmetric extension gap and rectangular or trapezoidal gaps in flexion. This study sought to investigate the effect of femoral and tibial coronal rotation and femoral axial rotation on midflexion coronal joint balance and patient outcomes.A prospective multi-center study was performed with a mixture of tibia-first gap-balancing and femur-first approaches were performed using the Corin OMNIBotics robot-assisted TKA platform with APEX implant components. Coronal and axial femoral and tibial resections were recorded by the platform. Medial and lateral joint gaps were recorded while applying a computer-controlled load to the joint throughout flexion during trialing using the Corin BalanceBot device. In addition, 1-year Knee Injury and Osteoarthritis Outcome Score (KOOS) and PROMIS-10 global health scores were collected.231 surgeries were identified: 66.9±8.1 years, 31.4±4.8 kg/m2 and 57% female (121) with a mean pre-operative HKA angle of 4.5±5.2° varus. A significant correlation was found between the medio-lateral (ML) joint gap difference in midflexion and both extension and flexion joint line (p=0.003, r2=-0.20, p=0.001, r2=-0.22, respectively). A significant correlation was found between midflexion ML imbalance and KOOS stiffness questions at 3 M and 6 M post-op (r2=-0.15, p=0.036, r2=-0.18, p=0.013), in which a more balanced knee correlated with improved outcomes.Treating flexion and extension joint balance in isolation may not capture the effect on midflexion laxity. Component placement should take in to account the effect on joint gaps throughout flexion to target optimal joint balance.