Hasil untuk "Orthopedic surgery"

B. Ritchie, G. Harrison, L. Harrison et al.

Suganthan Veerachamy, Tejasri Yarlagadda, Geetha Manivasagam et al.

Biofilms are a complex group of microbial cells that adhere to the exopolysaccharide matrix present on the surface of medical devices. Biofilm-associated infections in the medical devices pose a serious problem to the public health and adversely affect the function of the device. Medical implants used in oral and orthopedic surgery are fabricated using alloys such as stainless steel and titanium. The biological behavior, such as osseointegration and its antibacterial activity, essentially depends on both the chemical composition and the morphology of the surface of the device. Surface treatment of medical implants by various physical and chemical techniques are attempted in order to improve their surface properties so as to facilitate bio-integration and prevent bacterial adhesion. The potential source of infection of the surrounding tissue and antimicrobial strategies are from bacteria adherent to or in a biofilm on the implant which should prevent both biofilm formation and tissue colonization. This article provides an overview of bacterial biofilm formation and methods adopted for the inhibition of bacterial adhesion on medical implants

Yu Liu, B. Rath, M. Tingart et al.

Long-term and stable fixation of implants is one of the most important points for a successful orthopedic surgery in the field of endoprosthesis. Osseointegration, functional connection between bone and implants, is considered as a pivotal process of cementless implant fixation and integration, respectively. Osseointegration is affected by various factors of which the property of implants is of high significance. The modification of implants surface for better osseointegration has raised increasing attention in modern orthopedic medicine. Here, the process of osseointegration and the interactions between implants and ambient bone tissues were emblazed. The knowledge regarding the contemporary surface modification strategies was systematically analyzed and reviewed, including materials used for the fabrication of implants, advanced modification techniques, and key factors in the design of porous implants structure. We discussed the superiority of current surface modification programs and concluded the problems remain to be solved. The primary intention of this systematic review is to provide comprehensive reference information and an extensive overview for better fabrication and design of orthopedic implants. This article is protected by copyright. All rights reserved.

M. Cibelli, A. Fidalgo, N. Terrando et al.

Devinderpal Singh, M.S. Ortho., Prathmesh Jain, M.S. Ortho.

Arthroscopic subscapularis repair techniques are evolving and improving, with better preoperative and intraoperative diagnosis of tear patterns observed as well as improved capabilities of arthroscopic surgeons. Improved subscapularis repair helps to secure tough posterosuperior rotator cuff repair more easily, but subscapularis tear repair still often is neglected compared with posterosuperior rotator cuff as the result of different surgical challenges. We seek to share our surgical technique for subscapularis repair in which a posterior cruciate ligament femoral guide is used and a retrograde transosseous tunnel created, exiting the subscapularis footprint at lesser tuberosity and repair achieved using an all-suture anchor. Our surgical technique avoids a difficult trajectory and large-diameter bone punches during anchor insertion seen with other techniques, providing a reproducible and secure fixation.

Piotr Wodzinski, M.D., Ph.D., Tomasz Zoraw, M.D., Andrzej Wielgus, M.D., Ph.D. et al.

Despite constant development of surgical techniques, the outcomes of anterior cruciate ligament (ACL) reconstructions remain unpredictable, and the failure rate remains unacceptably high. Among many factors influencing results, the graft-healing process called ligamentization has recently been highly emphasized. Improvement of healing potential, connected with graft diameter optimization and the addition of lateral extra-articular tenodesis, might lead to superior treatment results. We describe a simple technique of combined anterior cruciate ligament reconstruction with a pedicled hamstring graft of case-specific diameter and lateral extra-articular tenodesis with limited hardware. “PARLET” stands for pedicled anterior cruciate ligament reconstruction and lateral extra-articular tenodesis without additional implants. Three options of graft preparation (3, 4, or 6 strands) enable diameter versatility and optimization during surgery. Preserving the hamstring pedicle and limiting its interference with tibial fixation due to the use of an ACL adjustable cortical button might improve graft healing potential. Fixation of lateral tenodesis with a femoral ACL cortical button solves the problem of tunnel or implant interference.

Cesar De Cesar Netto MD, PhD, Canon Cornelius MD, Anna Bryniarski MD et al.

Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: In the treatment of Progressive Collapsing Foot Deformity (PCFD), Lateral Column Lengthening (LCL) osteotomies are commonly performed. Two popular techniques for this procedure are the Evans (EO) and Hintermann osteotomy (HO). The EO involves a parallel cut 10-14mm proximal to the calcaneocuboid joint (CCJ), between the anterior facet (AF) and middle facet (MF). The HO starts at the angle of Gissane and runs obliquely between the MF and posterior facet (PF). However, due to anatomical variations in the calcaneus and articular facets, the entry point for LCL can vary, leading to frequent iatrogenic injury to the articular facets. This study aimed to identify a single ideal entry point for LCL that would allow for reliable cuts between the anterior/middle or middle/posterior facets. Methods: Retrospective-cohort study with a total 70 consecutive PCFD patients (70 feet, 35 females/35 males, mean age 47 years ±4) that underwent weightbearing CT (WBCT). The calcaneus was segmented semi-automatically. Only calcanei with independent AF and MF were utilized. AF, MF and PF were identified and marked. Two points were identified initially: entry-points for classic EO (12.5mm proximal to CCJ) and HO (Angle of Gissane). The distance between the CCJ and the Angle of Gissane, as well as the angles between HO entry-point and the edges of AF/MF and MF/PF were measured. We then determined the location of an ideal entry-point in the lateral wall of the calcaneus, that would optimize the angulation tolerance for performing either an EO (between AF/MF) or HO (between MF/PF) for each individual patient. To calculate that we utilized the optimum yields of the largest minimum tolerance statistics. Measurements were reported utilizing descriptive statistics. Results: By utilizing the traditional EO point and angulation (12.5mm proximal to CCJ), 91% of MF would be injured. HO entry-point (Angle of Gissane) was found to be positioned on average 20.1mm±2.7mm (range, 13.7-26.8mm) posterior to the CCJ. Utilizing the HO entry-point, cuts could be performed in between AF/MF and MF/PF, with an average angulation range of respectively 7.5o±1.6o (18o anterior margin/25.6o posterior margin) and -6.8o±2.6o (-3.8o anterior margin/-10.7o posterior margin). The ideal entry-point was found to be positioned on average 19.4mm±3mm posteriorly to the CCJ and just 0.7mm±2.5mm anteriorly to the angle of Gissane (HO point). The average angulation ranges from the ideal entry-point to AF/MF was 7.4o±1.4o (16.8o anterior margin/24.2o posterior margin) and to MF/PF -7.4o±1.4o (-4.6o anterior margin/-12.1o posterior margin). Conclusion: This study identifies an ideal entry-point for lateral column lengthening (LCL) osteotomies that maximizes angulation tolerance while minimizing the risk of iatrogenic injury to the articular facets. The proposed entry-point, located on average 19.4mm posterior to the CCJ and slightly anterior to the Angle of Gissane, offers a more reliable approach for positioning the osteotomy line in between the anterior and middle facets or between the middle and posterior facets. These findings provide valuable guidance for surgical planning in Progressive Collapsing Foot Deformity (PCFD) and may contribute to optimize accuracy for LCL osteotomies and to improve patient outcomes. Schematic drawings of a 3D rendering of calcaneus from weightbearing CT imaging, demonstrating the anterior, middle and posterior facets of the subtalar joint, the calcaneocuboid joint line, the point of the angle of gissane (hintermann osteotomy classic entry-point) and the identified ideal entry-point for a lateral column lengthening osteotomy, maximizing the range of angulations allowed to be utilized, avoiding iatrogenic injuries to the subtalar joint articular facets.

Ana Cristina Rodríguez-Pineda, Francisco Alexander Cevallos-Castro, Marco Xavier Montero-Uchuari

Las fracturas-avulsión de cabeza de fíbula son raras, generalmente son acompañadas por fracturas de tibia proximal, son muy pocos los casos reportados dentro de la bibliografía. Este tipo de fracturas son ocasionadas en su mayoría por traumas de alta energía, y en las que se puede acompañar de lesiones del ligamento colateral lateral (LCL) concomitantes. El caso que presentamos se trata de paciente femenina de 65 años de edad sin antecedentes patológicos, que sufre caída desde su propia altura provocando trauma directo en zona lateral de la rodilla derecha en flexión. El control radiológico confirmó fractura-avulsión de la cabeza de fíbula. Se encontró además lesión del LCL. La paciente fue tratada quirúrgicamente, se realizó reducción abierta más osteosíntesis con tornillo

Shimal Harichurn, Mrunmay Jagadale, Dmitry Noshchenko et al.

$\widehat{Z}$ invariants, rigorously defined for negative definite plumbed 3-manifolds, are expected--on physical grounds--to exist for every closed, oriented 3-manifold. Several prescriptions have been proposed to extend their definition to generic plumbings by reversing the orientation of a negative definite plumbing, thus turning it into a positive definite one. Two existing proposals are relevant for this paper: (i) the regularized $+1/r$-surgery conjecture combined with the false-mock modular conjecture, and (ii) a construction based on resurgence and a false theta function duality. In this note, we compare these proposals on the class of Brieskorn homology spheres $Σ\left(s,t,rst\pm1\right)$ and find that they are incompatible in general. Our diagnostic is the effective central charge, $c_{\text{eff}}$, which governs the asymptotic growth of coefficients of $\widehat{Z}$. First, we prove that the upper bound on $c_{\text{eff}}$ from prescription (i) is governed by the Ramanujan theta function, which regularizes the surgery formula. Second, we develop numerical and modular tools that deliver the lower bounds as well as exact values via mixed mock-modular analysis. Complementing this, we also study $c_{\text{eff}}$ for negative definite plumbed 3-manifolds which allow for a better comparison of pairs of 3-manifolds related by orientation reversal. As a result, we find that for some Brieskorn spheres the surgery and false-mock prescriptions violate the expected relation between $c_{\text{eff}}$, Chern-Simons invariants and non-abelian flat connections. These findings underscore $\widehat{Z}$ as a sensitive probe of the "positive side" of $\widehat{Z}$-theory.

Muhammad Hanif Lashari, Shakil Ahmed, Wafa Batayneh et al.

Accurate and real-time position estimation of the robotic arm on the patient's side is crucial for the success of remote robotic surgery in Tactile Internet environments. This paper proposes a predictive approach using the computationally efficient Transformer-based Informer model for position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The method effectively addresses network-induced delays, jitter, and packet loss, ensuring reliable performance in remote robotic surgery. The study evaluates the Informer model on the JIGSAWS dataset, demonstrating its capability to handle sequential data challenges caused by network uncertainties. Key features, including ProbSparse attention and a generative-style decoder, enhance prediction accuracy, computational speed, and memory efficiency. Results indicate that the proposed method achieves over 90 percent accuracy across varying network conditions. Furthermore, the Informer framework outperforms traditional models such as TCN, RNN, and LSTM, highlighting its suitability for real-time remote surgery applications.

Sahar Nasirihaghighi, Negin Ghamsarian, Leonie Peschek et al.

Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce GynSurg, the largest and most diverse multi-task dataset for gynecologic laparoscopic surgery to date. GynSurg provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the GynSurg dataset and its annotations

João Pedro Oliveira, Otília C. d'Almeida, Ricardo Sampaio et al.

Abstract Purpose To longitudinally evaluate sockets localization, tunnel morphological changes and graft maturation after the inside‐out tibial tunnel drilling technique for all‐inside Anterior Cruciate Ligament Reconstruction (ACLR). We hypothesized that due the necessary angle for the inside‐out reaming procedure, the described technique could input changes in the tibial socket. Methods Fourteen knees treated with the same all‐inside ACLR technique were randomly assigned for a magnetic resonance evaluation. All patients were operated by the same surgeon and performed the same follow‐up rehabilitation protocol. Socket's localization, shape and widening, as well as graft maturation and integration, were evaluated intraoperatively at 6 months and 4 years after surgery. Results Both femoral and tibial tunnels had an expected increase at 6 months follow‐up. The widening was larger in the tibial tunnel (12.6 ± 10.0% vs. 9.1 ± 8.5%), yet this difference was not statistically different. Tibial tunnel was well centred in the tibial plateau and the integration of the graft was higher in the tibial socket. Four years after surgery, there was a general reduction of diameter in both tunnels. The tunnel occlusion rate was 33.3% for tibia and 16.7% for femur. Conclusions Overall, our results show that within a 4‐year follow‐up period, the inside‐out tibial tunnel drilling technique for all‐inside ACLR represents a safe technique that did not influence the tibial socket position nor tunnel widening or graft maturation in the long term. Level of Evidence Level IV.

Mashu Futagawa, Tetsuya Okazaki, Eiji Nakata et al.

Abstract Neurofibromatosis type 1 (NF1) presents with a broad spectrum of clinical manifestations, including an increased risk of tumor development and hypertension. Comprehensive data on genotype‒phenotype correlations in patients with NF1 are limited. Therefore, in this study, we aimed to elucidate the detailed genetic and clinical characteristics of NF1 in a hereditary tumor cohort. We performed sequencing and copy number assays in a clinical laboratory and analyzed the clinical data of 44 patients with suspected NF1. Germline pathogenic variants were detected in 36 patients (81.8%), and 20.7% of the variants were novel. Notably, 40.0% of adult patients presented with malignancies; female breast cancer occurred in 20.0% of patients, which was a higher rate than that previously reported. Hypertension was observed in 30.6% of the adult patients, with one patient experiencing sudden death and another developing pheochromocytoma. Three patients with large deletions in NF1 exhibited prominent cutaneous, skeletal, and neurological manifestations. These results highlight the importance of regular surveillance, particularly for patients with malignancies and hypertension. Our findings provide valuable insights for genetic counseling and clinical management, highlighting the multiple health risks associated with NF1 and the need for comprehensive and multidisciplinary care.

Robert Spektor, Tom Friedman, Itay Or et al.

This work presents a framework for monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, specularity, occlusions, and synthetic-to-real domain adaptation. The proposed approach consists of three main components: $(1)$ synthetic data generation pipeline that incorporates 3D scanning of surgical tools with articulation rigging and physically-based rendering; $(2)$ a tailored pose estimation framework combining tool detection with pose and articulation estimation; and $(3)$ a training strategy on synthetic and real unannotated video data, employing domain adaptation with automatically generated pseudo-labels. Evaluations conducted on real data of open surgery demonstrate the good performance and real-world applicability of the proposed framework, highlighting its potential for integration into medical augmented reality and robotic systems. The approach eliminates the need for extensive manual annotation of real surgical data.

Yu-Guan Hsieh, James Thornton, Eugene Ndiaye et al.

Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. performance on another dataset, robustness, agreement with a prior). Although the simplest approach to incorporating an auxiliary loss is to sum it with the training loss as a regularizer, recent works have shown that one can improve performance by blending the gradients beyond a simple sum; this is known as gradient surgery. We cast the problem as a constrained minimization problem where the auxiliary objective is minimized among the set of minimizers of the training loss. To solve this bilevel problem, we follow a parameter update direction that combines the training loss gradient and the orthogonal projection of the auxiliary gradient to the training gradient. In a setting where gradients come from mini-batches, we explain how, using a moving average of the training loss gradients, we can carefully maintain this critical orthogonality property. We demonstrate that our method, Bloop, can lead to much better performances on NLP and vision experiments than other gradient surgery methods without EMA.

Samuel Schmidgall, Joseph Cho, Cyril Zakka et al.

Surgery requires comprehensive medical knowledge, visual assessment skills, and procedural expertise. While recent surgical AI models have focused on solving task-specific problems, there is a need for general-purpose systems that can understand surgical scenes and interact through natural language. This paper introduces GP-VLS, a general-purpose vision language model for surgery that integrates medical and surgical knowledge with visual scene understanding. For comprehensively evaluating general-purpose surgical models, we propose SurgiQual, which evaluates across medical and surgical knowledge benchmarks as well as surgical vision-language questions. To train GP-VLS, we develop six new datasets spanning medical knowledge, surgical textbooks, and vision-language pairs for tasks like phase recognition and tool identification. We show that GP-VLS significantly outperforms existing open- and closed-source models on surgical vision-language tasks, with 8-21% improvements in accuracy across SurgiQual benchmarks. GP-VLS also demonstrates strong performance on medical and surgical knowledge tests compared to open-source alternatives. Overall, GP-VLS provides an open-source foundation for developing AI assistants to support surgeons across a wide range of tasks and scenarios. The code and data for this work is publicly available at gpvls-surgery-vlm.github.io.

Sophia Fuhui Lin, Eric C. Peterson, Krishanu Sankar et al.

Running quantum algorithms protected by quantum error correction requires a real time, classical decoder. To prevent the accumulation of a backlog, this decoder must process syndromes from the quantum device at a faster rate than they are generated. Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code. However, for surface code, quantum programs of utility will require multi-qubit interactions performed via lattice surgery. A large merged patch can arise during lattice surgery -- possibly as large as the entire device. This puts a significant strain on a real time decoder, which must decode errors on this merged patch and maintain the level of fault-tolerance that it achieves on isolated logical qubits. These requirements are relaxed by using spatially parallel decoding, which can be accomplished by dividing the physical qubits on the device into multiple overlapping groups and assigning a decoder module to each. We refer to this approach as spatially parallel windows. While previous work has explored similar ideas, none have addressed system-specific considerations pertinent to the task or the constraints from using hardware accelerators. In this work, we demonstrate how to configure spatially parallel windows, so that the scheme (1) is compatible with hardware accelerators, (2) supports general lattice surgery operations, (3) maintains the fidelity of the logical qubits, and (4) meets the throughput requirement for real time decoding. Furthermore, our results reveal the importance of optimally choosing the buffer width to achieve a balance between accuracy and throughput -- a decision that should be influenced by the device's physical noise.

Jinlin Wu, Xusheng Liang, Xuexue Bai et al.

Surgical interventions, particularly in neurology, represent complex and high-stakes scenarios that impose substantial cognitive burdens on surgical teams. Although deliberate education and practice can enhance cognitive capabilities, surgical training opportunities remain limited due to patient safety concerns. To address these cognitive challenges in surgical training and operation, we propose SurgBox, an agent-driven sandbox framework to systematically enhance the cognitive capabilities of surgeons in immersive surgical simulations. Specifically, our SurgBox leverages large language models (LLMs) with tailored Retrieval-Augmented Generation (RAG) to authentically replicate various surgical roles, enabling realistic training environments for deliberate practice. In particular, we devise Surgery Copilot, an AI-driven assistant to actively coordinate the surgical information stream and support clinical decision-making, thereby diminishing the cognitive workload of surgical teams during surgery. By incorporating a novel Long-Short Memory mechanism, our Surgery Copilot can effectively balance immediate procedural assistance with comprehensive surgical knowledge. Extensive experiments using real neurosurgical procedure records validate our SurgBox framework in both enhancing surgical cognitive capabilities and supporting clinical decision-making. By providing an integrated solution for training and operational support to address cognitive challenges, our SurgBox framework advances surgical education and practice, potentially transforming surgical outcomes and healthcare quality. The code is available at https://github.com/franciszchen/SurgBox.

Samuel Schmidgall, Ji Woong Kim, Alan Kuntz et al.

The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a target position. However, recent work on high-capacity models in robotics has shown promise toward being trained on large collections of diverse and task-agnostic datasets of video demonstrations. These models have shown impressive levels of generalization to unseen circumstances, especially as the amount of data and the model complexity scale. Surgical robot systems that learn from data have struggled to advance as quickly as other fields of robot learning for a few reasons: (1) there is a lack of existing large-scale open-source data to train models, (2) it is challenging to model the soft-body deformations that these robots work with during surgery because simulation cannot match the physical and visual complexity of biological tissue, and (3) surgical robots risk harming patients when tested in clinical trials and require more extensive safety measures. This perspective article aims to provide a path toward increasing robot autonomy in robot-assisted surgery through the development of a multi-modal, multi-task, vision-language-action model for surgical robots. Ultimately, we argue that surgical robots are uniquely positioned to benefit from general-purpose models and provide three guiding actions toward increased autonomy in robot-assisted surgery.

BEATRIZ FOGAROLLI AFONSO, ARTHUR FELIPE LAUF MELOTTI, ITALO BARCELLOS DE SOUZA et al.

ABSTRACT Introduction: Low back pain is defined as pain, muscle spasm, or stiffness between the L1 and L5 vertebrae, below the lower margin of the twelfth rib and above the upper gluteal fold, and may or may not be associated with pain radiating to the lower limbs. Objective: To determine the prevalence of low back pain in spine surgeons. Method: A non-randomized quantitative cross-sectional clinical study was carried out in a sample of 95 spine surgeons in Brazil, with the application of the Oswestry and visual analog pain scales, in addition to a structured questionnaire for the characterization of the participants. Results: Among the studied population, 69.5% were orthopedists, 30.5% were neurosurgeons, and the mean age of the sample was 46 years (±10.6), with neurosurgeons being older than orthopedists. Regarding BMI, the majority (77.8%) were overweight or obese, and seventy-six percent performed physical activity. The prevalence of low back pain was 58.9%. No relevant differences were found in the time spent weekly in surgeries between those who had low back pain and those who did not (p = 0.364). Mean pain intensity was 2.0 (SD = 2.2), statistically (p = 0.025) higher in orthopedists (2.3) when compared to neurosurgeons (1.3). Regarding the ODI score, 98.2% of the surgeons had a minimal disability (0-20%) for daily activities. Conclusion: The prevalence of low back pain in spine surgeons is high and is associated with mild inability to perform daily activities. Level Of Evidence IV; Non-Randomized Quantitative Cross-Sectional Clinical Study.