Computer-assisted Surgery Systems Research Articles

One model to use them all: training a segmentation model with complementary datasets.

Understanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require large amounts of annotated training data, containing examples of all relevant object classes, which are rarely available. In this work, we propose a method to combine multiple partially annotated datasets, providing complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets. Our method aims to combine available data with complementary labels by leveraging mutual exclusive properties to maximize information. Specifically, we propose to use positive annotations of other classes as negative samples and to exclude background pixels of these binary annotations, as we cannot tell if a positive prediction by the model is correct. We evaluate our method by training a DeepLabV3 model on the publicly available Dresden Surgical Anatomy Dataset, which provides multiple subsets of binary segmented anatomical structures. Our approach successfully combines 6 classes into one model, significantly increasing the overall Dice Score by 4.4% compared to an ensemble of models trained on the classes individually. By including information on multiple classes, we were able to reduce the confusion between classes, e.g. a 24% drop for stomach and colon. By leveraging multiple datasets and applying mutual exclusion constraints, we developed a method that improves surgical scene segmentation performance without the need for fully annotated datasets. Our results demonstrate the feasibility of training a model on multiple complementary datasets. This paves the way for future work further alleviating the need for one specialized large, fully segmented dataset but instead the use of already existing datasets.

Can Hisense computer-assisted surgery system (Hisense CAS) improve anatomy teaching in pediatric liver surgery?

This study aimed to investigate the effectiveness of the Hisense computer-assisted surgery system (CAS) in teaching pediatric liver surgical anatomy. The research subjects were residents who underwent standardized training at the Department of Pediatric Surgery at Yijishan Hospital of Wannan Medical College from May 2022 to May 2023. The study recruited a total of 62 students, with 31 students assigned to the Hisense CAS group (12 males and 19 females) and the remaining 31 students serving as controls (Control group, 15 males and 16 females). There were no significant differences in baseline characteristics observed between the two groups. This study found that the average scores of the Hisense CAS teaching group in the liver surgery evaluations were higher than those of the control group. Specifically, the Hisense CAS group had an average score of 84.25 ± 5.70 points in the liver surgery knowledge test, 77.10 ± 8.12 points in the image reading test, and 70.58 ± 8.79 points in the surgical simulation test, while the traditional teaching group had average scores of 73.45 ± 6.12 points, 69.81 ± 6.05 points, and 66.42 ± 6.61 points, respectively; the differences between the two groups were statistically significant (P < 0.05). Furthermore, this study also found that the Hisense CAS teaching model resulted in significantly better teaching satisfaction on the part of the residents in terms of standardized teaching for physicians in pediatric liver surgical anatomy. In conclusion, this study demonstrated greater satisfaction of the residents with the use of 3D reconstruction added to traditional teaching sessions and better performance during the posttraining evaluation.

Instrument-tissue Interaction Detection Framework for Surgical Video Understanding.

Instrument-tissue interaction detection task, which helps understand surgical activities, is vital for constructing computer-assisted surgery systems but with many challenges. Firstly, most models represent instrument-tissue interaction in a coarse-grained way which only focuses on classification and lacks the ability to automatically detect instruments and tissues. Secondly, existing works do not fully consider relations between intra-and inter-frame of instruments and tissues. In the paper, we propose to represent instrument-tissue interaction as ⟨instrument class, instrument bounding box, tissue class, tissue bounding box, action class⟩ quintuple and present an Instrument-Tissue Interaction Detection Network (ITIDNet) to detect the quintuple for surgery videos understanding. Specifically, we propose a Snippet Consecutive Feature (SCF) Layer to enhance features by modeling relationships of proposals in the current frame using global context information in the video snippet. We also propose a Spatial Corresponding Attention (SCA) Layer to incorporate features of proposals between adjacent frames through spatial encoding. To reason relationships between instruments and tissues, a Temporal Graph (TG) Layer is proposed with intra-frame connections to exploit relationships between instruments and tissues in the same frame and inter-frame connections to model the temporal information for the same instance. For evaluation, we build a cataract surgery video (PhacoQ) dataset and a cholecystectomy surgery video (CholecQ) dataset. Experimental results demonstrate the promising performance of our model, which outperforms other state-of-the-art models on both datasets.

An Automatic and Robust Visual SLAM Method for Intra-Abdominal Environment Reconstruction

Three-dimensional (3D) surface reconstruction is used to solve the problem of the narrow field of view in laparoscopy. It can provide surgeons or computer-assisted surgery systems with real-time complete internal abdominal anatomy. However, rapid changes in image depth, less texture, and specular reflection pose a challenge for the reconstruction. It is difficult to stably complete the reconstruction process using feature-based simultaneous localization and mapping (SLAM) method. This paper proposes a robust laparoscopic 3D surface reconstruction method using SLAM, which can automatically select appropriate parameters for stereo matching and robustly find matching point pairs for laparoscope motion estimation. The changing trend of disparity maps is used to predict stereo matching parameters to improve the quality of the disparity map. Feature patch extraction and tracking are selected to replace feature point extraction and matching in motion estimation, which reduces its failure and interruption in feature-based SLAM. The proposed feature patch matching method is suitable for parallel computing, which can improve its computing speed. Evaluation results on public in vivo and ex vivo porcine abdominal video data show the efficiency and robustness of our 3D surface reconstruction approach.

Less is More: Surgical Phase Recognition from Timestamp Supervision.

Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotations, which require the surgeons to repeat watching videos to find the precise start and end time for a surgical phase. In this paper, we introduce timestamp supervision for surgical phase recognition to train the models with timestamp annotations, where the surgeons are asked to identify only a single timestamp within the temporal boundary of a phase. This annotation can significantly reduce the manual annotation cost compared to the full annotations. To make full use of such timestamp supervisions, we propose a novel method called uncertainty-aware temporal diffusion (UATD) to generate trustworthy pseudo labels for training. Our proposed UATD is motivated by the property of surgical videos, i.e., the phases are long events consisting of consecutive frames. To be specific, UATD diffuses the single labelled timestamp to its corresponding high confident ( i.e., low uncertainty) neighbour frames in an iterative way. Our study uncovers unique insights of surgical phase recognition with timestamp supervision: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation costs; 3) less is more in surgical phase recognition, i.e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug-and-play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods. Code and annotations obtained from surgeons are available at https://github.com/xmed-lab/ TimeStamp-Surgical.

Gap Balancing Throughout the Arc of Motion With Navigated TKA and a Novel Force-Controlled Distractor: A Review of the First 273 Cases

BackgroundThis study evaluated the ability to achieve the targeted soft-tissue balance in terms of medio-lateral (ML) laxity and gap values when using a computer-assisted orthopedic surgery (CAOS) system featuring an intra-articular force-controlled distractor and assessed learning curves associated with the adoption of this technology. MethodsThe first 273 cases using this technology were reported without exclusions comparing 1) final ML laxity and 2) final average gap to their predefined targets. For both parameters, the signed and unsigned differentials were reported. The linear mixed model was used to evaluate laxity curve differences between surgeons. A cumulative sum control chart (CUSUM) was applied to assess surgeon learning curves regarding surgical time. ResultsBoth the average signed ML laxity and gap differentials were neutral throughout the full arc of motion. Both the average unsigned ML laxity and gap differentials were linear. Signature of ML laxity and gap differential curves tended to be surgeon-specific. The CUSUM analyses of surgical times demonstrated either a short learning curve or the absence of a discernible learning pattern for surgeons. ConclusionData from all users involved with the pilot release of the balancing device were considered to capture variability in familiarity with the technique and learning curve cases were included. A high ability to achieve targeted gap balance throughout the arc of motion using the proposed method was observed.

Can Axis C be a navigation route – CT comparison study between actual and virtual C1 transpedicular screw insertion

BackgroundPrevious studies mainly reported perpendicular and medial inclination insertion methods for C1 transpedicular screw insertion (TSI). Our recent study showed the ideal C1 transpedicular screw trajectory (TST) can be achieved by medial inclination, perpendicular or even lateral inclination insertion, and Axis C can be a reliable trajectory. The purpose of this study is to confirm Axis C is an ideal C1 TST by comparing the cortical perforation differences between actual C1 TSI and virtual C1 transpedicular screw insertion along Axis C (Virtual C1 Axis C TSI). MethodsFirstly, the cortical perforations of the transverse foramen and vertebral canal caused by C1 TSIs in twelve randomly selected patients were evaluated based on their postoperative CT data. Secondly, Virtual C1 Axis C TSIs were performed based on same patients’ preoperative CT data. Thirdly, the cortical perforation differences between actual and virtual screws were compared. ResultsIn actual C1 TSI group, there were thirteen locations of cortical perforation in the axial plane, with five sides in transverse foramen and eight sides in vertebral canal, the cortical perforation rate was 54.2%; the degree of perforation was mild in twelve locations and medium in one location. In contrast, there was no cortical perforation in Virtual C1 Axis C TSI group. ConclusionsAxis C is an ideal trajectory for C1 TSI, it can be utilized as a navigation route for computer assisted surgery system.

Comparison between passive knee kinematics during surgery and active knee kinematics during walking: A preliminary study.

Recovery of function is among a patient's main expectations when undergoing total knee arthroplasty (TKA). However, normal gait knee function is not always completely restored, which can affect patient satisfaction and quality of life. Computer-assisted surgery (CAS) allows surgeons to evaluate passive knee kinematics intra-operatively. Understanding associations between knee kinematics measured during surgery and during daily activities, such as walking, could help define criteria for success based on knee function and not only on the correct alignment of the implant or the leg. This preliminary study compared passive knee kinematics measured during surgery with active kinematics measured during walking. Eight patients underwent a treadmill gait analysis using the KneeKG™ system both before surgery and three months afterwards. Knee kinematics were measured during CAS both before and after TKA implantation. The anatomical axes of the KneeKG™ and CAS systems were homogenised using a two-level, multi-body kinematics optimisation with a kinematic chain based on the calibration measured during CAS. A Bland-Altman analysis was performed before and after TKA for adduction-abduction angle, internal-external rotation, and anterior-posterior displacement over the whole gait cycle, at the single stance phase and at the swing phase. Homogenising the anatomical axes between CAS and treadmill gait led to limited median bias and limits of agreement (post-surgery -0.6 ± 3.6 deg, -2.7 ± 3.6 deg, and -0.2 ± 2.4 mm for adduction-abduction, internal-external rotation and anterior-posterior displacement, respectively). At the individual level, correlations between the two systems were mostly weak (R2 < 0.3) over the whole gait cycle, indicating low kinematic consistency between the two measurements. However, correlations were better at the phase level, especially the swing phase. The multiple sources of differences did not enable us to conclude whether they came from anatomical and biomechanical differences or from measurement system errors.

Application of computer-assisted surgery in pediatric mediastinal tumor surgery.

Computer-assisted Surgery system (CAS) is an effective medical imaging simulation tool, which is widely used in preoperative planning of surgery. The objective of this study is to investigate the clinical application of CAS in pediatric mediastinal tumor resection. This retrospective study investigated 74 children who underwent mediastinal tumor resection between June 2008 and June 2022 at the pediatric surgical center of the Affiliated Hospital of Qingdao University and Qingdao Women and Children's Hospital. Preoperative chest computed tomography imaging was performed on all children. A total of 44 children (the CAS-assisted group) underwent clinical image 3D reconstruction and preoperative simulation using Hisense CAS. The control group consisted of 30 children who underwent a conventional procedure without CAS. The demographic, preoperative, and complication data were analyzed and compared between the two groups. t-test, Mann-Whitney U test, X2 test, or Fisher's exact test were used accordingly in this study during analysis. The median operative duration was 119.00min in the CAS-assisted group and 140.50min in the control group. The median intraoperative blood loss of the CAS-assisted group and the control group was 14.00 and 31.00ml respectively. Relative to the control groups, the CAS-assisted group experienced shorter operative duration time (p=0.041), and less intraoperative blood loss (p<0.001). The difference in postoperative drain indwelling between the CAS-assisted group (median:4.00days) and the control group (median:7.00days) reached a statistical significance (p=0.001). And the duration of hospitalization after the operation for the CAS-assisted group (median:7.00days) was shorter than that for the control group (median:9.00days) (p=0.001). No significant difference could be found in the rate of blood transfusion (p=0.258) and the incidence of postoperative complications (p=0.719) between the two groups. Hisense CAS could effectively assist surgeons to clearly determine the anatomical site of tumors and provide accurate preoperative simulation for surgeons, so as to assist surgeons to specify effective surgical plans for patients.

Does acetabular robotic-assisted total hip arthroplasty with femoral navigation improve clinical outcomes at 1-year post-operative? A case-matched propensity score study comparing 98 robotic-assisted versus 98 manual implantation hip arthroplasties

IntroductionDespite the optimization of implant positioning, the clinical benefit of the use of robotic assistance during THA remains uncertain. In this case-control study (robotic versus manual technique) we made a retrospective short-term comparison of: (1) the functional results, (2) the complications, (3) and the influence of radiological symmetrization of the hips on the functional result. HypothesisThe use of a robotic arm improved the functional results of a THA. Materials and methodA retrospective case-control study was performed, including patients with unilateral hip osteoarthritis who underwent a robotic arm-assisted THA (THA-R). The controls (THA-C) were matched according to age, sex, body mass index and surgical approach. The Harris (HHS), Oxford (OHS) and Forgotten Joint (FJS) scores were collected preoperatively and 1 year postoperatively. At each review, complications were categorized into minor events, revision surgery with and without implant change. Radiographic analysis was performed on weight-bearing images of the pelvis 3 months postoperatively. The objective of the surgery was symmetrization of the THA in the contralateral healthy hip. For each measured parameter of the hip joint center of rotation (COR), global offset (GO) and articular leg length discrepancy (aLLD), the difference between the two sides corresponding to the delta symmetrization was compared. ResultsNinety-eight patients were included in the THA-R group and matched to 98 controls in the THA-C group. At 1 year postoperatively, the FJS and OHS scores were statistically higher in the THA-R group, respectively 82.1±22.3 [8.3; 100] and 40.8±8.8 [6; 48] vs. 71.2±27.8 [0; 100] and 38.1±9.7 [12; 48] in the THA-C group (p=0.004 and p=0.043). There was no difference in HHS (THA-R: 85.9±15.8 [31; 100] vs. THA-C: 85.8±13.3 [49–100] (p=0.962)). The implant revision rate at 1 year was significantly higher in the THA-C group (0% vs. 5.1% (p=0.025)). There was no difference in the reoperation without component exchange despite 3 dislocations (3.1%) in the THA-R group (5 (5.1%) vs. 9 (9.2%) (p=0.273)). The rate of abarticular pathologies (ilio-psoas irritation and greater trochanteric bursitis) was higher in the THA-C group (10 (10.8%) vs. 2 (2%) (p=0.016). Robotic acetabular assistance allowed a significantly better restitution of the horizontal position (THA-R: 1mm±4.8 [–11.7; 12.6] vs. THA-C: 4.1mm±7 [-29.6; 28] (p=0.0005)) and vertical COR (THA-R: 0.5mm±3.1 [–6; 8.3] vs. THA-C: 2mm±4.1 [–6; 14.6] (p=0.0068)). Navigated assistance of the femur did not significantly optimize the restitution of the Global Offset (THA-R: +2mm±6.4 [-16.4; 27.6] vs. THA-C: +0.5mm±7 .7 [–34; 30.2] (p=0.145)), or lower limb length (THA-R: +1.1mm±5 [–8.2; 13.5] vs. THA-C: +0.3mm±6 [-14.1; 22.5] (p=0.344). The FJS was statistically correlated with the restoration of the symmetry of the COR and the aLLD. A post-hoc power analysis confirmed sufficient potency (1-β=0.85). ConclusionRobotic acetabular assistance combined with femoral navigation improves clinical outcomes of THAs with fewer short-term complications. The precision of the positioning of the implants, optimized by the computer-assisted surgery system, is correlated with the missed joint score. Level of evidenceIII, retrospective case-control study.

Class-wise confidence-aware active learning for laparoscopic images segmentation.

Segmentation tasks are important for computer-assisted surgery systems as they provide the shapes of organs and the locations of instruments. What prevents the most powerful segmentation approaches from becoming practical applications is the requirement for annotated data. Active learning provides strategies to dynamically select the most informative samples to reduce the annotation workload. However, most previous active learning literature has failed to select the frames that containing low-appearing frequency classes, even though the existence of these classes is common in laparoscopic videos, resulting in poor performance in segmentation tasks. Furthermore, few previous works have explored the unselected data to improve active learning. Therefore, in this work, we focus on these classes to improve the segmentation performance. We propose a class-wise confidence bank that stores and updates the confidence scores for each class and a new acquisition function based on a confidence bank. We apply confidence scores to explore an unlabeled dataset by combining it with a class-wise data mixture method to exploit unlabeled datasets without any annotation. We validated our proposal on two open-source datasets, CholecSeg8k and RobSeg2017, and observed that its performance surpassed previous active learning studies with about [Formula: see text] improvement on CholecSeg8k, especially for classes with a low-appearing frequency. For robSeg2017, we conducted experiments with a small and large annotation budgets to validate situation that shows the effectiveness of our proposal. We presented a class-wise confidence score to improve the acquisition function for active learning and explored unlabeled data with our proposed class-wise confidence score, which results in a large improvement over the compared methods. The experiments also showed that our proposal improved the segmentation performance for classes with a low-appearing frequency.

Generic surgical process model for minimally invasive liver treatment methods

Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced. The generic surgical process model was established at three different granularity levels. The generic process model, encompassing thirteen phases, was verified against videos of MILT procedures and interviews with surgeons. The established model covers all the surgical and interventional activities and the connections between them and provides a foundation for extensive quantitative analysis and simulations of MILT procedures for improving computer-assisted surgery systems, surgeon training and evaluation, surgeon guidance and planning systems and evaluation of new technologies.

Can tibia-first total knee arthroplasty using computer-assisted system improve anterior and posterior knee stability?

Total knee arthroplasty (TKA) is a widely performed procedure to alleviate pain and restore function of patients with end-stage knee osteoarthritis. The study aim was to determine if tibia-first (TF) total knee arthroplasty (TKA) using a novel computer-assisted surgery (CAS) system can yield better anterior and posterior (AP) knee stability. Patients with knee osteoarthritis with obvious varus knee who met the indication for and underwent TKA from May 2019 to November 2020 were included. Forty-one measured resection (MR)-TKAs and 32 TF-TKAs were compared. The varus-valgus ligament balance and joint tension at a joint center-gap setting equal to the tibial-baseplate thickness were measured, and appropriate polyethylene inserts with 0∘, 30∘, 45∘, 60∘, 90∘, and 120∘ of knee flexion were placed. A Kneelax 3 arthrometer was used to measure knee AP laxity in the postoperative anesthetized patients with 30∘ and 90∘ of knee flexion. The horizontal gap balance was significantly closer in the TF-TKA group than the MR-TKA group for 0∘, 30∘, 45∘, and 60∘ of knee flexion. In contrast, no significant differences were observed for 90∘ and 120∘ of knee flexion. No significant differences in joint-gap tensions among all knee-flexion angles were observed. Translation was significantly smaller in the TF-TKA group than the MR-TKA group for AP laxity with 30∘ of knee flexion (8.8 ± 2.9 mm vs. 10.7 ± 3.1 mm, P= 0.0079). In contrast, no significant AP laxity was observed with 90∘ of knee flexion (7.2 ± 2.8 mm vs. 7.2 ± 3.5 mm). TF-TKA using a novel CAS system provided better AP knee stability with close to horizontal gap balances.

Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: Experimental research

BackgroundTo perform accurate laparoscopic hepatectomy (LH) without injury, novel intraoperative systems of computer-assisted surgery (CAS) for LH are expected. Automated surgical workflow identification is a key component for developing CAS systems. This study aimed to develop a deep-learning model for automated surgical step identification in LH. Materials and methodsWe constructed a dataset comprising 40 cases of pure LH videos; 30 and 10 cases were used for the training and testing datasets, respectively. Each video was divided into 30 frames per second as static images. LH was divided into nine surgical steps (Steps 0–8), and each frame was annotated as being within one of these steps in the training set. After extracorporeal actions (Step 0) were excluded from the video, two deep-learning models of automated surgical step identification for 8-step and 6-step models were developed using a convolutional neural network (Models 1 & 2). Each frame in the testing dataset was classified using the constructed model performed in real-time. ResultsAbove 8 million frames were annotated for surgical step identification from the pure LH videos. The overall accuracy of Model 1 was 0.891, which was increased to 0.947 in Model 2. Median and average accuracy for each case in Model 2 was 0.927 (range, 0.884–0.997) and 0.937 ± 0.04 (standardized difference), respectively. Real-time automated surgical step identification was performed at 21 frames per second. ConclusionsWe developed a highly accurate deep-learning model for surgical step identification in pure LH. Our model could be applied to intraoperative systems of CAS.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Application of a novel computer-assisted surgery system in percutaneous nephrolithotomy: A controlled study

BACKGROUNDMost complex renal stones are managed primarily with percutaneous nephrolithotomy (PCNL). However, PCNL is still a great challenge for surgeons because of poor comprehension on complex adjacent structures. Novel techniques are required to assist in planning and navigation.AIMTo apply and evaluate the Hisense computer-assisted surgery (CAS) system in PCNL.METHODSA total of 60 patients with complex renal stones were included. Thirty patients in the CAS group had three-dimensional (3D) virtual models constructed with the CAS system. The model assisted in planning and navigating in the CAS system. Thirty patients in the control group planned and navigated as standard PCNL, without the application of the CAS system. Success rate of one attempt, operation time, initial stone-free rate, decrease in hemoglobin, and complications were collected and analyzed.RESULTSThere were no statistically significant differences in the baseline characteristics or planning characteristics. The success rate of one puncturing attempt (90% vs 67%, P = 0.028) and the initial stone-free rate (87% vs 63%, P = 0.037) were significantly higher in the CAS group. However, there were no statistically significant differences in the operation time (89.20 ± 29.60 min vs 92.33 ± 33.08 min, P = 0.859) or in the decrease in hemoglobin (11.07 ± 8.32 g/L vs 9.03 ± 11.72 g/L, P = 0.300) between the CAS group and the control group. No statistically significant differences in the incidence of complications (Clavien-Dindo grade ≥ 2) were found.CONCLUSIONCompared with standard PCNL, CAS-assisted PCNL had advantages in terms of the puncturing success rate and stone-free rate. The Hisense CAS System was recommended to assist in preoperative planning and intraoperative navigation for an intuitive, precise and convenient PCNL.

Abnormal spinopelvic mobility as a risk factor for acetabular placement error in total hip arthroplasty using optical computer-assisted surgical navigation system.

AimsNavigation devices are designed to improve a surgeon’s accuracy in positioning the acetabular and femoral components in total hip arthroplasty (THA). The purpose of this study was to both evaluate the accuracy of an optical computer-assisted surgery (CAS) navigation system and determine whether preoperative spinopelvic mobility (categorized as hypermobile, normal, or stiff) increased the risk of acetabular component placement error.MethodsA total of 356 patients undergoing primary THA were prospectively enrolled from November 2016 to March 2018. Clinically relevant error using the CAS system was defined as a difference of > 5° between CAS and 3D radiological reconstruction measurements for acetabular component inclination and anteversion. Univariate and multiple logistic regression analyses were conducted to determine whether hypermobile (Δsacral slope(SS)stand-sit > 30°), or stiff (ΔSSstand-sit < 10°) spinopelvic mobility contributed to increased error rates.ResultsThe paired absolute difference between CAS and postoperative imaging measurements was 2.3° (standard deviation (SD) 2.6°) for inclination and 3.1° (SD 4.2°) for anteversion. Using a target zone of 40° (± 10°) (inclination) and 20° (± 10°) (anteversion), postoperative standing radiographs measured 96% of acetabular components within the target zone for both inclination and anteversion. Multiple logistic regression analysis controlling for BMI and sex revealed that hypermobile spinopelvic mobility significantly increased error rates for anteversion (odds ratio (OR) 2.48, p = 0.009) and inclination (OR 2.44, p = 0.016), whereas stiff spinopelvic mobility increased error rates for anteversion (OR 1.97, p = 0.028). There were no dislocations at a minimum three-year follow-up.ConclusionDespite high reliability in acetabular positioning for inclination in a large patient cohort using an optical CAS system, hypermobile and stiff spinopelvic mobility significantly increased the risk of clinically relevant errors. In patients with abnormal spinopelvic mobility, CAS systems should be adjusted for use to avoid acetabular component misalignment and subsequent risk for long-term dislocation.Cite this article: Bone Jt Open 2022;3(6):475–484.

Application of 3D Simulation Software in Chemotherapy and Hepatoblastoma Surgery in Children.

PurposeThis study aims to explore the clinical value of a computer-assisted surgery system (Hisense CAS) in hepatoblastoma (HB) surgery in children after neoadjuvant chemotherapy.Patients and MethodsThe clinical medical records of children with HB treated after neoadjuvant chemotherapy at the Affiliated Hospital of Qingdao University from January 2016 to January 2019 were analyzed retrospectively.ResultsA total of 21 children were enrolled in this study, including 13 boys and 8 girls. All cases successfully underwent three-dimensional (3D) reconstruction of the liver and tumor using Hisense CAS, simulated hepatectomy, and hepatectomy according to the preoperative operation plan. There were twelve cases of right hemihepatectomy, four cases of right trefoil hepatectomy, one case of left lobe hepatectomy, and three cases of middle lobe hepatectomy, and one case of V and VI segment hepatectomy. All children recovered well after the operation. The follow-up ranged from 5 months to 3 years. One child died of systemic metastasis 8 months after the operation. One child received one course of chemotherapy after the operation. Due to the serious reaction to the chemotherapy, the family refused further treatment and follow-up. The remaining 19 children had no complications or recurrence.ConclusionHisense CAS can clearly and intuitively display the position and shape of the HB before and after chemotherapy and its relationship with the intrahepatic pipeline system and accurately evaluate the changes in tumor volume and the distance between important blood vessels, which is conducive to the operator selecting the best operation opportunity, timely formulating the best operation plan and implementing individualized and accurate liver tumor resection.

Not strong enough? Movements generated during clinical examination of sagittal and rotational laxity in a cadaver knee

ABSTRACT BACKGROUND: Injury to the anterior cruciate ligament (ACL) is associated with sagittal and rotational laxity, which is exacerbated by damage to the anterolateral capsuloligamentous structures, also known as the anterolateral ligament (ALL). The amount of laxity reported in biomechanical studies might be clinically insignificant during a surgeon's examination, possibly influencing clinical judgement. We aimed to measure whether the motion generated by clinicians in a cadaver model after the ACL and ALL were transected is clinically significant METHODS: A group of orthopaedic surgeons and trainees examined a cadaver knee for sagittal and rotational laxity at 30° and 90° with intact ligaments, after the ACL was transected, and after the ACL and ALL were transected. The examiners were blinded to the dissection process. Rotational and sagittal movements during these examinations were recorded by a computer-assisted surgery (CAS) system RESULTS: Twenty-four orthopaedic surgeons took part in the study. The median sagittal plane motion captured by CAS at 30° flexion was 7 mm (IQR 2 mm, p-value 0.32) in the intact knee, 9 mm (IQR 1 mm, p-value 0.34) after the ACL was cut and 9 mm (IQR 3 mm, p-value 0.63) after ACL and ALL were cut. The median arc of rotational motion at 30° was 19° (IQR 7°, p-value 0.12) in the intact knee, 24° (IQR 5°, p-value 0.56) after the ACL was cut, and 22° (IQR 6°, p-value 0.8) after the ACL and ALL were cut. None of the differences in these movements was significant CONCLUSION: The surgeons could not generate significant differences in sagittal or rotational motion in a cadaver model, which could be objectively detected by CAS, when examining the intact knee, ACL deficient (only), or combined ACL and ALL deficient knee. This challenges the utility of known clinical tests and calls for improved objective laxity assessment tools to provide input in clinical decision-making and measure outcomes of these injuries Level of evidence: Level 5 Keywords: knee, anterolateral ligament, anterior drawer's test, pivot shift, rotatory instability, anterior cruciate ligament, iliotibial band, Kaplan fibres

Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net

Automatic vertebrae localization and segmentation in computed tomography (CT) are fundamental for spinal image analysis and spine surgery with computer-assisted surgery systems. But they remain challenging due to high variation in spinal anatomy among patients. In this paper, we proposed a deep-learning approach for automatic CT vertebrae localization and segmentation with a two-stage Dense-U-Net. The first stage used a 2D-Dense-U-Net to localize vertebrae by detecting the vertebrae centroids with dense labels and 2D slices. The second stage segmented the specific vertebra within a region-of-interest identified based on the centroid using 3D-Dense-U-Net. Finally, each segmented vertebra was merged into a complete spine and resampled to original resolution. We evaluated our method on the dataset from the CSI 2014 Workshop with 6 metrics: location error (1.69 ± 0.78 mm), detection rate (100%) for vertebrae localization; the dice coefficient (0.953 ± 0.014), intersection over union (0.911 ± 0.025), Hausdorff distance (4.013 ± 2.128 mm), pixel accuracy (0.998 ± 0.001) for vertebrae segmentation. The experimental results demonstrated the efficiency of the proposed method. Furthermore, evaluation on the dataset from the xVertSeg challenge with location error (4.12 ± 2.31), detection rate (100%), dice coefficient (0.877 ± 0.035) shows the generalizability of our method. In summary, our solution localized the vertebrae successfully by detecting the centroids of vertebrae and implemented instance segmentation of vertebrae in the whole spine.