Hasil untuk "Surgery"

F. Allman

S. Mathes, F. Nahai

A. Gérard, M. Buyse, B. Nordlinger et al.

Chen S, Gao PJ

Shuang Chen,1,2 Peng-ji Gao1,2 1Department of General Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, People’s Republic of China; 2Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, People’s Republic of ChinaCorrespondence: Peng-ji Gao, Department of General Surgery, Beijing Jishuitan Hospital, Capital Medical University, No. 31, Xinjiekou East Street, Xicheng District, Beijing, 100035, People’s Republic of China, Tel +8601058398275, Email gaopengji@mail.ccmu.edu.cnBackground: Colorectal cancer (CRC) is one of the most common and deadly malignancies worldwide. Sarcopenia, defined as a progressive loss of skeletal muscle mass and function, has recently been recognized as an important prognostic factor in CRC, influencing both postoperative complications and long-term survival.Methods: We conducted a descriptive review of 18 clinical studies investigating the association between sarcopenia and CRC across stages I–IV. Sarcopenia was primarily assessed using computed tomography-derived skeletal muscle index (SMI) or psoas index (PI) at the lumbar vertebrae (L3/L4), with some studies additionally incorporating muscle strength and performance.Results: The prevalence of sarcopenia among CRC patients ranged from 12% to 60%. Most studies reported higher risks of postoperative complications in sarcopenic patients. For instance, Peng et al demonstrated an increased risk of complications in stage IV CRC patients with sarcopenia (OR: 3.12, 95% CI: 1.14– 8.49). Regarding survival, sarcopenia was consistently associated with worse overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS). Brown et al (2018) showed that deterioration in muscle mass and radiodensity significantly predicted poorer OS in 1,924 stage I–III CRC patients (HR: 2.15, 95% CI: 1.59– 2.92). However, several studies reported no significant associations.Conclusion: Sarcopenia is prevalent in CRC patients and strongly correlates with both short-term surgical outcomes and long-term prognosis. However, current evidence is mainly derived from heterogeneous observational studies, and further prospective studies are needed before sarcopenia assessment can be translated into routine clinical practice.Keywords: colorectal cancer, sarcopenia, skeletal muscle index, prognosis, postoperative complications, survival

Lincoln Spencer, Song Wang, Chen Chen

Surgical phase segmentation is central to computer-assisted surgery, yet robust models remain difficult to develop when labeled surgical videos are scarce. We study data-efficient phase segmentation for manual small-incision cataract surgery (SICS) through a controlled comparison of visual representations. To isolate representation quality, we pair each visual encoder with the same temporal model (MS-TCN++) under identical training and evaluation settings on SICS-155 (19 phases). We compare supervised encoders (ResNet-50, I3D) against large self-supervised foundation models (DINOv3, V-JEPA2), and use a cached-feature pipeline that decouples expensive visual encoding from lightweight temporal learning. Foundation-model features improve segmentation performance in this setup, with DINOv3 ViT-7B achieving the best overall results (83.4% accuracy, 87.0 edit score). We further examine cataract-domain transfer using unlabeled videos and lightweight adaptation, and analyze when it helps or hurts. Overall, the study indicates strong transferability of modern vision foundation models to surgical workflow understanding and provides practical guidance for low-label medical video settings. The project website is available at: https://sl2005.github.io/DataEfficient-sics-phase-seg/

Shuo Zhang, Zihua Wang, Changgeng He et al.

This paper proposes LiNUS, a lightweight deep learning framework for the automatic segmentation of the Subthalamic Nucleus (STN) in Deep Brain Stimulation (DBS) surgery. Addressing the challenges of small target volume and class imbalance in MRI data, LiNUS improves upon the U-Net architecture by introducing spectral normalization constraints, bilinear interpolation upsampling, and a multi-scale feature fusion mechanism. Experimental results on the Tsinghua DBS dataset (TT14) demonstrate that LiNUS achieves a Dice coefficient of 0.679 with an inference time of only 0.05 seconds per subject, significantly outperforming traditional manual and registration-based methods. Further validation on high-resolution data confirms the model's robustness, achieving a Dice score of 0.89. A dedicated Graphical User Interface (GUI) was also developed to facilitate real-time clinical application.

Decheng Jiang, Ruiling Xiao, Jialu Bai et al.

Abstract The advent of the omics era has facilitated the identification of precise biomarkers for cancer progression, revealing a broader diversity of macrophage phenotypes beyond the traditional M1/M2 classification. Folate receptor 2 (FOLR2)-positive macrophages, co-expressing markers such as mannose receptor C-Type 1 (MRC1/CD206) and lymphatic vessel endothelial hyaluronan receptor 1(LYVE1), are an embryonically derived subset typically found around blood vessels in both tumor stroma and normal tissues. Despite FOLR2’s longstanding association with anti-inflammatory, immunosuppressive macrophages in tumors, its precise role in cancer progression remains unclear. Recent studies suggest that FOLR2+ macrophages can either promote or inhibit cancer progression, depending on their multifaceted roles in the tumor microenvironment. This review provides a comprehensive overview of the biological features, functional roles, molecular mechanisms, and therapeutic potential of FOLR2+ macrophages in cancer.

Ziming Cai, Gongpeng Xiong, Jintao Wu et al.

ABSTRACT Introduction Spinal cord injury (SCI), acknowledged as the most severe complication arising from spinal trauma, pertains to the dysfunction of the spinal cord due to traumatic events or other pathological conditions. Extensive research has elucidated a substantial correlation between SCI and inflammatory processes, highlighting the critical involvement of microglia in orchestrating neuroinflammatory responses. Moreover, a growing body of evidence has demonstrated a strong connection between microglial activation and both the pathogenesis and progression of SCI. Objective We chose bibliometric analysis to comprehensively summarize the research progress of microglia in SCI, aiming to provide researchers with current trends and future research directions. Methods All articles and reviews addressing microglia in SCI were systematically retrieved from the Web of Science Core Collection database, spanning publications from 2000 to 2024. Subsequent bibliometric analysis was conducted utilizing four analytical tools: VOSviewer (version 1.6.20), R software (package bibliometrix), the Biblioshiny web interface, and CiteSpace (version 6.2.R4), ensuring comprehensive examination of publication patterns and research trends. Results A total of 2428 publications were ultimately included in this bibliometric analysis. The annual publication count demonstrated a consistent upward trajectory. China is the country with the most published articles, and Ohio State University ranks first in institutional publications. Experimental Neurology is the journal with the most published articles, while Journal of Neuroscience is the journal with the most cited articles. Popovich Pg is the author with the highest productivity and co‐citation. Cluster analysis yielded a total of 15 different co‐citation clusters. Time analysis shows explosive citation outbreaks in 2006, 2009, and 2011. Keyword analysis revealed inflammation, expression, activation, and central nervous system as the most frequently occurring terms. Recent keyword trends feature emerging terms like exosomes, extracellular vesicles, and nanoparticles. Keyword bursts revealed promotes, extracellular vesicle, recovery, neuroinflammation, therapy, polarization, and pathway are the hotspots of research at the present stage and are likely to continue. These findings provide critical insights for developing microglia‐targeted therapeutic strategies and prioritizing research directions in neuroinflammatory modulation to improve functional recovery after SCI. Conclusion Emerging research frontiers prominently feature exosomes, gut microbiota, and nanoparticles. The interplay between microglia‐mediated neuroinflammation and SCI has emerged as a critical focal point in current scientific investigations and is anticipated to remain central to forthcoming scientific inquiries.

Tyler LeBlond, Ryan S. Bennink

Hamiltonian simulation is one of the most promising candidates for the demonstration of quantum advantage within the next ten years, and several studies have proposed end-to-end resource estimates for executing such algorithms on fault-tolerant quantum processors. Usually, these resource estimates are based upon the assumption that quantum error correction is implemented using the surface code, and that the best surface code compilation scheme involves serializing input circuits by eliminating all Clifford gates. This transformation is thought to make best use of the native multi-body measurement (lattice surgery) instruction set available to surface codes. Some work, however, has suggested that direct compilation from Clifford+T to lattice surgery operations may be beneficial for circuits that have high degrees of logical parallelism. In this study, we analyze the resource costs for implementing Hamiltonian simulation using example approaches from each of these leading surface code compilation families. The Hamiltonians whose dynamics we consider are those of the transverse-field Ising model in several geometries, the Kitaev honeycomb model, and the $\mathrm{α-RuCl_3}$ complex under a time-varying magnetic field. We show, among other things, that the optimal scheme depends on whether Hamiltonian simulation is implemented using the quantum signal processing or Trotter-Suzuki algorithms, with Trotterization benefiting by orders of magnitude from direct Clifford+T compilation for these applications. Our results suggest that surface code quantum computers should not have a one-size-fits-all compilation scheme, but that smart compilers should predict the optimal scheme based upon high-level quantities from logical circuits such as average circuit density, numbers of logical qubits, and T fraction.

Tinghe Hong, Shenlin Cai, Boyang Li et al.

Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operations in confined surgical spaces. Despite the advancements in developing vision- and force-based control methods for surgical robots, preoperative navigation remains heavily reliant on manual operation, limiting the consistency and increasing the uncertainty. Existing eye gaze estimation techniques in the surgery, whether traditional or deep learning-based, face challenges including dependence on additional sensors, occlusion issues in surgical environments, and the requirement for facial detection. To address these limitations, this study proposes an innovative eye localization and tracking method that combines machine learning with traditional algorithms, eliminating the requirements of landmarks and maintaining stable iris detection and gaze estimation under varying lighting and shadow conditions. Extensive real-world experiment results show that our proposed method has an average estimation error of 0.58 degrees for eye orientation estimation and 2.08-degree average control error for the robotic arm's movement based on the calculated orientation.

José Alberto Benítez-Andrades, Camino Prada-García, Nicolás Ordás-Reyes et al.

The study proposes an advanced machine learning approach to predict spine surgery outcomes by incorporating oversampling techniques and grid search optimization. A variety of models including GaussianNB, ComplementNB, KNN, Decision Tree, and optimized versions with RandomOverSampler and SMOTE were tested on a dataset of 244 patients, which included pre-surgical, psychometric, socioeconomic, and analytical variables. The enhanced KNN models achieved up to 76% accuracy and a 67% F1-score, while grid-search optimization further improved performance. The findings underscore the potential of these advanced techniques to aid healthcare professionals in decision-making, with future research needed to refine these models on larger and more diverse datasets.

Shu Yang, Fengtao Zhou, Leon Mayer et al.

Computer-Assisted Intervention (CAI) has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-making, improving procedural efficacy, and ensuring intraoperative safety. While existing AI-driven approaches alleviate annotation burdens via self-supervised spatial representation learning, their lack of explicit temporal modeling during pre-training fundamentally restricts the capture of dynamic surgical contexts, resulting in incomplete spatiotemporal understanding. In this work, we introduce the first video-level surgical pre-training framework that enables joint spatiotemporal representation learning from large-scale surgical video data. To achieve this, we constructed a large-scale surgical video dataset comprising 3,650 videos and approximately 3.55 million frames, spanning more than 20 surgical procedures and over 10 anatomical structures. Building upon this dataset, we propose SurgVISTA (Surgical Video-level Spatial-Temporal Architecture), a reconstruction-based pre-training method that captures intricate spatial structures and temporal dynamics through joint spatiotemporal modeling. Additionally, SurgVISTA incorporates image-level knowledge distillation guided by a surgery-specific expert to enhance the learning of fine-grained anatomical and semantic features. To validate its effectiveness, we established a comprehensive benchmark comprising 13 video-level datasets spanning six surgical procedures across four tasks. Extensive experiments demonstrate that SurgVISTA consistently outperforms both natural- and surgical-domain pre-trained models, demonstrating strong potential to advance intelligent surgical systems in clinically meaningful scenarios.

Lei Liu, XingGuang Zhang, LiWei Niu

Michael Boelstoft Holte, Alexandru Diaconu, Else Marie Pinholt

The purpose of the present study was to compare the precision and reliability of voxel- and surface-based registration for computer-assisted assessment of the surgical accuracy and postoperative stability of segmental bimaxillary surgery. Three-dimensional translational and rotational measurements were performed by two observers using voxel- and surface-based registration. The precision and reliability of the measurements were calculated by the mean absolute differences (MAD) and intraclass correlation coefficients (ICC) at 95 % confidence intervals. A paired t-test or the non-parametric equivalent, Wilcoxon signed-rank test, was applied to statistically evaluate whether the precision of voxel- and surface-based registration was statistically significantly different (p < 0.05). Voxel-based registration had high precision (MAD <0.44 mm/0.92°) and excellent reliability, ICC [0.82–1.00]. The precision of surface-based registration was lower (MAD <0.56 mm/1.45°) and the reliability ranged from poor to excellent for the different bone segments, ICC [0.33–1.00]. Both registration techniques had high precision and excellent reliability for the assessment of the surgical accuracy, and the error margin of both techniques was clinical irrelevant. However, the increased precision of voxel-based registration was statistically significant (p < 0.05) for the maxillary segments and the chin, and the stability measurement error (ranging up to 1.58 mm and 4.46°) introduced by surface-based registration may be considered clinical relevant for these bone segments. Within the limitations of the present comparative study, voxel-based registration generally exhibited higher precision and reliability than surface-based registration for the surgical accuracy and postoperative stability assessment of segmental bimaxillary surgery.

Merryn D. Constable, Hubert P. H. Shum, Stephen Clark

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new opportunities to advance cardiothoracic surgical performance with innovations from computer vision and artificial intelligence. These technological innovations are critically evaluated in terms of the benefits they could offer the cardiothoracic surgical community, and any barriers to the uptake of the technology are elaborated upon. Like some other specialities, cardiothoracic surgery has relatively few opportunities to benefit from tools with data capture technology embedded within them (as with robotic-assisted laparoscopic surgery, for example). In such cases, pose estimation techniques that allow for movement tracking across a conventional operating field without using specialist equipment or markers offer considerable potential. With video data from either simulated or real surgical procedures, these tools can (1) provide insight into the development of expertise and surgical performance over a surgeon's career, (2) provide feedback to trainee surgeons regarding areas for improvement, (3) provide the opportunity to investigate what aspects of skill may be linked to patient outcomes which can (4) inform the aspects of surgical skill which should be focused on within training or mentoring programmes. Classifier or assessment algorithms that use artificial intelligence to 'learn' what expertise is from expert surgical evaluators could further assist educators in determining if trainees meet competency thresholds.

Ruohua Shi, Zhaochen Liu, Lingyu Duan et al.

Segmentation of surgical instruments is crucial for enhancing surgeon performance and ensuring patient safety. Conventional techniques such as binary, semantic, and instance segmentation share a common drawback: they do not accommodate the parts of instruments obscured by tissues or other instruments. Precisely predicting the full extent of these occluded instruments can significantly improve laparoscopic surgeries by providing critical guidance during operations and assisting in the analysis of potential surgical errors, as well as serving educational purposes. In this paper, we introduce Amodal Segmentation to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments Segmentation (AIS) dataset, which was developed by reannotating each instrument with its complete mask, utilizing the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset.

Csaba Nagy

Kreck proved that two $2q$-manifolds are stably diffeomorphic if and only if they admit normally bordant normal $(q-1)$-smoothings over the same normal $(q-1)$-type $(B,ξ)$. We show that stable diffeomorphism can be replaced by diffeomorphism if the normal smoothings have isomorphic Q-forms (which consists of the intersection form of the manifold and the induced homomorphism on $H_q$), when the manifolds are simply-connected, $q=2k$ is even and $H_q(B)$ is free. This proves a special case of Crowley's Q-form conjecture. The basis of the proof is the construction of an extended surgery obstruction associated to a normal bordism. As an application, we identify the inertia group of a $(2k-1)$-connected $4k$-manifold with the kernel of a certain bordism map. By the calculations of Senger-Zhang and earlier results, these kernels are now known in all cases. For $k=2,4$, the combination of these results determines the inertia groups. We also obtain, for a simply-connected $4k$-manifold $M$ with normal $(q-1)$-type $(B,ξ)$ such that $H_q(B)$ is free, an algebraic description of the stable class of $M$, that is, the set of diffeomorphism classes of manifolds stably diffeomorphic to $M$. Using this description, we explicitly compute the stable class of manifolds $M$ with rank-$2$ hyperbolic intersection form.

R. Riley, N. Powell, C. Guilleminault

J. Agee, H. Mccarroll, Richard Tortosa et al.

Matthias Mehdorn