Computer-assisted Surgery Systems Research Articles

Undifferentiated embryonal sarcoma of the liver in children: our experience in four difficult cases and three-dimensional practical exploration

AimsTo explore rare and difficult cases of undifferentiated embryonal sarcoma of the liver (UESL) in children in a single centre, summarize the diagnosis and treatment experience and analyse the role of a computer-assisted surgery system (Hisense CAS), thus providing a new global vision and three-dimensional perspective.MethodsWe retrospectively collected the clinical data including the diagnoses and treatment processes, of children with UESL confirmed by histopathological examination in our hospital from January 2009 to December 2020. The relationship between the tumour volume and important blood vessels and between the liver volume and tumour volume, as well as other three-dimensional characteristics in the reconstructed three-dimensional model were analysed using Hisense CAS. The findings from this analysis can be used to aid in surgical decision-making and preoperative planning.ResultsFour children—3 girls and 1 boy—with UESL were included in the study. The age at onset ranged from 6 to 8 years. All four children presented with symptoms of abdominal discomfort, and abdominal masses were detected during physical examination. Owing to the wishes of their parents and the possibility that the disease was benign, all four children underwent one-stage radical surgery. For patient 1, a three-dimensional reconstruction was created during the initial diagnosis, which made accurate evaluation and planning of the preoperative procedure challenging. In patient 2, the tumour was located in the middle lobe of the liver and involved the first and second hepatic hilum. For patient 3, the pathological diagnosis of the tumour after surgery was challenging, but eventually, the diagnosis was confirmed through histochemistry and consultation with higher-level hospitals. Patient 4 had a giant tumour, which had a preoperative simulated future liver remnant volume (FLV) that was 21.0% of the total volume of the liver and tumour (TLTV). According to the standard liver volume (SLV) for children, the FLV was 77.0% of the SLV, making surgery feasible. All four children underwent complete resection, and only patient 4 experienced recurrence below the diaphragm 19 months after surgery. Currently, the 3-year overall survival rate is 100%, and the 3-year event-free survival rate is 75%.ConclusionUESL in children is rare, and the key to diagnosis and treatment is complete surgical resection. Through individualized three-dimensional surgical planning, accurate and complete resection of difficult and complex UESL in children can be achieved, leading to a favourable prognosis.

Comparative study on three-dimensional versus two-dimensional imaging using a computer-assisted surgery system for preoperative planning in pediatric middle hepatic tumors

BackgroudThe study objective was to compare three-dimensional and two-dimensional imaging using computer-assisted systems (CASs) in clinical guidance for preoperative surgical planning for middle hepatic tumors in children.MethodsA retrospective analysis was performed on 23 children who underwent surgery for middle hepatic tumors in our hospital from January 2016 to June 2022. The surgical resection plan was formulated by the operator team using two-dimensional CT images before the operation. Then, the same qualified surgeons conducted an in-depth analysis and formulated the surgical resection scheme for the same pediatric patient using three-dimensional imaging of the middle hepatic tumor. The feasibility of the two schemes was compared and analyzed.ResultAll the tumors were successfully removed according to the preoperative method developed using three-dimensional imaging. The postoperative short-term follow-up revealed that all patients were doing well. Preoperative plans were revised in 9 cases after evaluating the three-dimensional images due to the disparity between the original plans and the three-dimensional relationship between the tumor and blood vessels, vascular variation, and the volume of remnant liver.ConclusionsThree-dimensional imaging with a computer-assisted surgery system is superior to two-dimensional imaging in the preoperative planning of pediatric hepatoblastoma.

The Impact of Navigation in Lumbar Spine Surgery: A Study of Historical Aspects, Current Techniques and Future Directions.

Background/Objectives: We sought to improve accuracy while minimizing radiation hazards, improving surgical outcomes, and preventing potential complications. Despite the increasing popularity of these systems, a limited number of papers have been published addressing the historical evolution, detailing the areas of use, and discussing the advantages and disadvantages, of this increasingly popular system in lumbar spine surgery. Our objective was to offer readers a concise overview of navigation system history in lumbar spine surgeries, the techniques involved, the advantages and disadvantages, and suggestions for future enhancements to the system. Methods: A comprehensive review of the literature was conducted, focusing on the development and implementation of navigation systems in lumbar spine surgeries. Our sources include PubMed-indexed peer-reviewed journals, clinical trial data, and case studies involving technologies such as computer-assisted surgery (CAS), image-guided surgery (IGS), and robotic-assisted systems. Results: To develop more practical, effective, and accurate navigation techniques for spine surgery, consistent advancements have been made over the past four decades. This technological progress began in the late 20th century and has since encompassed image-guided surgery, intraoperative imaging, advanced navigation combined with robotic assistance, and artificial intelligence. These technological advancements have significantly improved the accuracy of implant placement, reducing the risk of misplacement and related complications. Navigation has also been found to be particularly useful in tumor resection and minimally invasive surgery (MIS), where conventional anatomic landmarks are lacking or, in the case of MIS, not visible. Additionally, these innovations have led to shorter operative times, decreased radiation exposure for patients and surgical teams, and lower rates of reoperation. As navigation technology continues to evolve, future innovations are anticipated to further enhance the capabilities and accessibility of these systems, ultimately leading to improved patient outcomes in lumbar spine surgery. Conclusions: The initial limited utilization of navigation system in spine surgery has further expanded to encompass almost all fields of lumbar spine surgeries. As the cost-effectiveness and number of trained surgeons improve, a wider use of the system will be ensured so that the navigation system will be an indispensable tool in lumbar spine surgery. However, continued research and development, along with training programs for surgeons, are essential to fully realize the potential of these technologies in clinical practice.

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

PurposeUnderstanding 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.MethodsOur 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.ResultsWe 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.ConclusionBy 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.