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The effect of thickness variation on the rigidity of ankle foot orthoses provided to the NHS: A case for the need for quality control. 厚度变化对踝关节足矫形器刚性的影响提供给NHS:一个需要质量控制的案例。
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-10-01 Epub Date: 2025-07-28 DOI: 10.1016/j.medengphy.2025.104404
Panagiotis E Chatzistergos, Nicola Eddison, Ilias Theodorakos, Nachiappan Chockalingam

Background: Drape-forming is a cost-effective method used worldwide to manufacture bespoke ankle foot orthoses (AFOs). It involves draping a heated polymer material sheet over a positive cast of the user's limb. Previous research has shown that the manual nature of drape-forming can influence the thickness of the final AFO and even lead to structures that are inadequately rigid to be clinically effective. This study assesses the difference between the prescribed and the final thickness of AFOs meant for use by UK's National Health Service (NHS) and estimates its potential impact on AFO rigidity.

Methods: A clinically relevant method to measure AFO thickness as part of the manufacturing process was developed and validated. This method was used by three major UK manufacturers for all bespoke rigid AFOs they provided to the NHS within a predefined period. A validated finite element model was used to estimate the impact of the observed difference between prescribed and final thickness on AFO stiffness.

Results: 86 AFOs were assessed in total. Final thickness was between 4.0% and 35.5% lower than the prescribed one (median thickness reduction= 17.4%). This discrepancy in thickness led to a relative reduction in AFO stiffness ranging between 7.0% and 80.0% (median stiffness reduction= 30.7%).

Discussion: The adequacy of AFO thickness cannot be judged based on prescription thickness. Measurements of final thickness as part of standard practice should be considered to enhance the provision of bespoke AFOs. Further research is needed to establish thresholds of acceptable manufacturing-induced deviation from the prescribed AFO thickness.

背景:悬垂成形是全球范围内用于制造定制踝足矫形器(AFOs)的一种经济有效的方法。它包括将加热的聚合物材料片覆盖在使用者肢体的正模上。先前的研究表明,手工形成褶皱的性质会影响最终AFO的厚度,甚至导致结构硬度不足,无法达到临床效果。本研究评估了英国国家卫生服务(NHS)使用的AFO的规定厚度和最终厚度之间的差异,并估计了其对AFO刚度的潜在影响。方法:开发并验证了一种临床相关的测量AFO厚度的方法,作为制造过程的一部分。这种方法被三家主要的英国制造商用于所有定制的刚性afo,他们在预定义的时期内提供给NHS。通过验证的有限元模型,估计了观察到的规定厚度与最终厚度之间的差异对AFO刚度的影响。结果:共评估afo 86例。最终厚度比规定厚度低4.0% ~ 35.5%(中位厚度降低= 17.4%)。这种厚度差异导致AFO刚度的相对降低幅度在7.0%到80.0%之间(中位刚度降低= 30.7%)。讨论:不能根据处方厚度判断AFO厚度是否充足。应考虑将最终厚度测量作为标准操作的一部分,以增强定制afo的提供。需要进一步的研究来建立可接受的制造引起的偏离规定AFO厚度的阈值。
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引用次数: 0
Biomechanical evaluation of intelligent fluid-solid coupling vaginal dilatation system: Experimental and numerical analysis. 智能流固耦合阴道扩张系统的生物力学评价:实验与数值分析。
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-10-01 Epub Date: 2025-07-28 DOI: 10.1016/j.medengphy.2025.104408
Renling Zou, Hongwei Tan, Xuan Zhang, Qingbin Fang, Xuelian Gu, Rui Guan

Objectives: Cervical cancer is a serious threat to women's life and health and has a high mortality rate. Colposcopy is an important method for early clinical cervical cancer screening, but the traditional vaginal dilator has problems such as discomfort in use and cumbersome operation. For this reason, this study aims to design an intelligent vaginal dilatation system to automate colposcopy and enhance patient comfort.

Methods: An intelligent vaginal dilatation system combining flexible and rigid dilatation techniques is proposed. A fluid-solid coupled finite element method was used to simulate the dilation process of the system during colposcopy. The smart dilator was inserted into a simulated vaginal model in the simulation, and the fluid domain pressure inlet was set to be 1.5 kPa, and the solid material was a hyperelastic model of medical silicone rubber. Subsequently, the prototype machining was completed and in vitro dilatation displacement and pressure experiments were conducted.

Results: The simulation results showed that the maximum expansion of the system was 32.2 mm, and the average pressure on the simulated vaginal wall was 605.91 kPa. The average maximum expansion of the system in the in-vitro expansion displacement experiment was 30.49 ± 0.05 mm, which was basically the same as the simulation results. The results of the in vitro pressure experiment showed that the intelligent dilatation system had a larger force area on the vaginal wall at the same level of dilatation, and the pressure value was smaller and more uniformly distributed. Compared with the traditional duckbill dilator, it can effectively reduce the local pressure feeling and improve the uniformity of dilation.

Conclusion: The intelligent vaginal dilatation system proposed in this study is superior to traditional dilators in terms of dilatation performance, safety and comfort. The feasibility of its design and potential for clinical application were verified.

目的:宫颈癌是对妇女生命和健康的严重威胁,死亡率很高。阴道镜检查是临床早期宫颈癌筛查的重要方法,但传统阴道扩张器存在使用不舒服、操作繁琐等问题。因此,本研究旨在设计一种智能阴道扩张系统,实现阴道镜检查的自动化,提高患者的舒适度。方法:提出一种柔性与刚性相结合的智能阴道扩张系统。采用流固耦合有限元法模拟阴道镜检查过程中系统的扩张过程。仿真中将智能扩张器插入模拟阴道模型,流体域压力入口设置为1.5 kPa,固体材料为医用硅橡胶超弹性模型。随后,完成了原型加工,并进行了体外膨胀位移和压力实验。结果:仿真结果表明,系统的最大膨胀为32.2 mm,模拟阴道壁上的平均压力为605.91 kPa。在体外膨胀位移实验中,系统的平均最大膨胀为30.49±0.05 mm,与仿真结果基本一致。体外压力实验结果表明,智能扩张系统在同等扩张水平下对阴道壁的受力面积更大,压力值更小,分布更均匀。与传统的鸭嘴扩张器相比,能有效降低局部压迫感,提高扩张的均匀性。结论:本研究提出的智能阴道扩张系统在扩张性能、安全性和舒适性方面均优于传统的扩张器。验证了其设计的可行性和临床应用潜力。
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引用次数: 0
A knowledge graph-based post-stroke gait assessment system: A pilot study 基于知识图谱的卒中后步态评估系统:初步研究
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-30 DOI: 10.1016/j.medengphy.2025.104449
Yiran Jiao , Zengkun Liu , Stacey Reading , Marie-Claire Smith , Jianhua Lin , Yanxin Zhang
Instrumented gait analysis (IGA) has been widely used in research, but not typically in clinical practice, as it requires expertise in data analysis and interpretation. Post-stroke clinical gait assessment could be improved by integrating artificial intelligence into IGA, but previous gait assessment systems have relatively low clinical utility. This study aims to develop a clinically oriented automatic post-stroke gait assessment system based on knowledge graph (KG) to better support clinicians. A domain KG is first constructed in the field of gait analysis. This system can process IGA data to identify gait abnormalities and their potential causes based on kinematic analysis and KG. A preliminary evaluation with twenty post-stroke patients and four domain experts tested the system's performance in clinical settings, showing an average recall, precision, and F-score of 1, 0.78, and 0.89. Four clinical professionals showed high behavioural intention to use the system in clinical settings (4.33 ± 0.41 on a 5-point Likert scale based on the Technology Acceptance Model). The results depicted that this system has potential to be applied in clinical settings to provide useful supplementary insights for clinicians, which may promote the interpretation and clinical utility of IGA. The schema of this KG could be generalised or extended to gait analysis related to other diseases.
仪器步态分析(IGA)已广泛应用于研究,但通常不用于临床实践,因为它需要数据分析和解释方面的专业知识。将人工智能集成到IGA中可以改善脑卒中后临床步态评估,但以往的步态评估系统的临床实用性相对较低。本研究旨在开发一种基于知识图谱(KG)的面向临床的脑卒中后步态自动评估系统,以更好地支持临床医生。首先在步态分析领域建立了一个域KG。该系统可以处理IGA数据,基于运动学分析和KG识别步态异常及其潜在原因。对20名中风后患者和4名领域专家的初步评估测试了该系统在临床环境中的表现,显示平均召回率、准确率和f分数分别为1.78、0.89。4名临床专业人员表现出在临床环境中使用该系统的高行为意愿(基于技术接受模型的5点李克特量表为4.33±0.41)。结果表明,该系统有可能应用于临床环境,为临床医生提供有用的补充见解,这可能会促进对IGA的解释和临床应用。这种KG的模式可以推广或扩展到与其他疾病相关的步态分析。
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引用次数: 0
A comprehensive review on the printing efficiency, precision, and cell viability in 3D bioprinting 对3D生物打印的打印效率、精度和细胞活力进行了全面的综述
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-29 DOI: 10.1016/j.medengphy.2025.104448
Bowen Li , Zhen Wang , Chuanzhen Huang , Longhua Xu , Shuiquan Huang , Meina Qu , Zhengkai Xu , Dijia Zhang , Baosu Guo , Tianye Jin , Chunhui Ji
Three-dimensional (3D) bioprinting demonstrates significant potential for advancing regenerative medicine through precise fabrication of functional tissue constructs via controlled deposition of cells, biomaterials, and bioactive factors. However, balancing key parameters-printing efficiency, resolution, and cell viability-remains challenging for replicating native tissue complexity. This review comprehensively examines recent advancements in three prominent bioprinting modalities: inkjet, extrusion-based, and digital light processing (DLP). Analysis reveals inherent performance trade-offs among these technologies. Inkjet bioprinting achieves high resolution (10-80 μm) at moderate speeds but exhibits limited cell viability (74-85%). Extrusion-based methods enable higher fabrication rates (0.00785-62.83 mm³/s) with variable viability (40-90%) at reduced resolution (100-2000 μm). DLP offers superior efficiency (0.648-840 mm³/s) and ultra-high resolution (2-50 μm) with favorable viability (75-95%), although limitations persist regarding photoinitiator toxicity and light penetration depth. Critical examination identifies energy-induced cell damage as a significant factor, with shear stress and UV exposure representing key detrimental influences. Bioink properties also emerge as crucial determinants of printing outcomes. The review further integrates modeling approaches for extrusion-based bioprinting and discusses preliminary computational modeling attempts. Future directions should focus on developing low-viscosity cell-compatible bioinks, advancing hybrid printing strategies, and establishing predictive models to harmonize printing parameters with biological outcomes. Interdisciplinary collaboration remains essential to fully realize the clinical potential of bioprinted tissues and organoids.
三维(3D)生物打印通过控制细胞、生物材料和生物活性因子的沉积来精确制造功能组织结构,展示了推进再生医学的巨大潜力。然而,平衡关键参数——打印效率、分辨率和细胞活力——仍然是复制天然组织复杂性的挑战。本文综述了三种突出的生物打印方式的最新进展:喷墨、挤压和数字光处理(DLP)。分析揭示了这些技术之间固有的性能权衡。在中等速度下,喷墨生物打印可以获得高分辨率(10-80 μm),但细胞存活率有限(74-85%)。基于挤压的方法可以在降低分辨率(100-2000 μm)下实现更高的制造速率(0.00785-62.83 mm³/s)和可变生存能力(40-90%)。DLP具有卓越的效率(0.648-840 mm³/s)和超高分辨率(2-50 μm),具有良好的生存能力(75-95%),但在光引发剂毒性和光穿透深度方面仍存在局限性。关键检查确定能量诱导的细胞损伤是一个重要因素,剪切应力和紫外线暴露是主要的有害影响。生物墨水的特性也成为打印效果的关键决定因素。这篇综述进一步整合了基于挤压的生物打印的建模方法,并讨论了初步的计算建模尝试。未来的方向应该集中在开发低粘度细胞兼容的生物墨水,推进混合打印策略,建立预测模型以协调打印参数与生物结果。跨学科合作对于充分发挥生物打印组织和类器官的临床潜力至关重要。
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引用次数: 0
Methods for quantitative analyses of nerve fiber deformation in the myenteric plexus under loading of mouse distal colon and rectum 方法定量分析小鼠远端结肠和直肠负荷下肌肠丛神经纤维变形
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-29 DOI: 10.1016/j.medengphy.2025.104444
Amirhossein Shokrani , Atta Seck , Bin Feng , David M. Pierce
Visceral pain in the large bowel is a hallmark of irritable bowel syndrome (IBS) and the primary reason patients seek gastroenterological care. Notably, mechanical distension of the distal colon and rectum (colorectum) reliably evokes abdominal pain and thus understanding mechanotransduction of sensory nerve endings (nerve fibers) in the colorectum is crucial for understanding and treating IBS-related bowel pain. To facilitate such understanding we aimed to establish novel methods to mechanically test, image, and analyze large-strain deformations of networks of nerve fibers in the myenteric plexus of the colorectum, and thus enable quantitative analyses. We successfully delivered circumferential, displacement-driven deformations to intact segments of colorectum while maintaining the myenteric plexus in focus during fluorescent imaging to capture the deforming nerve fibers. We also established a semi-automated method to recapitulate the network morphology and a code to calculate the stretch ratios of individual nerve fibers deforming within the myenteric plexus of mouse colorectum. Our code allows plotting of stretch ratios for each fiber, stretch ratios vs. fiber angles, and stretch ratios vs. fiber lengths. Our methods not only facilitate analyses of deformations of networks of colorectal nerve fibers in the context of visceral nociception but are also applicable to analyzing the in-plane deformation of other two-dimensional fiber networks. We provide free, public access to our analysis code for MATLAB, including input files for a simple test case, at github.uconn.edu/imLab/Fiber-Network_Analyses.
大肠内脏疼痛是肠易激综合征(IBS)的标志,也是患者寻求胃肠病学治疗的主要原因。值得注意的是,远端结肠和直肠(结直肠)的机械膨胀可靠地引起腹痛,因此了解结直肠感觉神经末梢(神经纤维)的机械转导对于理解和治疗ibs相关的肠痛至关重要。为了促进这种理解,我们的目标是建立新的方法来机械测试,成像和分析大应变变形的神经纤维网络在结直肠的肌肠丛,从而实现定量分析。我们成功地将圆周位移驱动的变形转移到完整的结直肠部分,同时在荧光成像期间保持肌丛聚焦,以捕获变形的神经纤维。我们还建立了一种半自动化的网络形态再现方法和计算小鼠结直肠肌肠丛内变形的单个神经纤维拉伸比的代码。我们的代码允许绘制每根纤维的拉伸比,拉伸比与纤维角度,以及拉伸比与纤维长度。我们的方法不仅有助于分析内脏痛觉背景下结直肠神经纤维网络的变形,而且也适用于分析其他二维纤维网络的平面内变形。我们提供免费的、公开的MATLAB分析代码,包括一个简单测试用例的输入文件,网址为github.uconn.edu/imLab/Fiber-Network_Analyses。
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引用次数: 0
Effect of curvilinear insertion on mechanics of curved needles in tissue-mimicking material 曲线插入对仿组织材料中弯曲针力学的影响
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-29 DOI: 10.1016/j.medengphy.2025.104446
Doyoung Kim, Parsaoran Hutapea
Needle steering to achieve large curvatures and deflection has the potential to enhance percutaneous procedures, such as brachytherapy, radiofrequency ablation, and deep brain stimulation. However, the mechanics of needles with large curvatures still need to be explored. This study introduces a curvilinear insertion to comprehensively investigate needle-tissue interactions. Experiments using passive curved needles with various bent angles (7.5°, 15°, 22.5°, and 30°) are conducted in tissue-mimicking materials, comparing curvilinear and linear insertion techniques. The curvilinear insertion method significantly reduces the average transverse force by 55.76%, axial force by 45.87%, and bending moment by 57.36%. Additionally, the proposed method achieves comparable curvature with less needle tip displacement between the initial puncture and the final needle position. These reductions in force, moment, and displacement may result in minimizing tissue damage and tissue deformation. The findings suggest that curvilinear insertion offers mechanical advantages and provides valuable insights for developing advanced needles with large curvature, enabling more precise targeting in minimally invasive surgery.
针头转向以实现大曲率和偏转有可能增强经皮手术,如近距离治疗、射频消融和深部脑刺激。然而,具有大曲率的针的力学仍然需要探索。本研究引入曲线插入来全面研究针与组织的相互作用。实验采用不同弯曲角度(7.5°,15°,22.5°和30°)的被动弯曲针在模拟组织材料中进行,比较曲线和线性插入技术。曲线插入法显著降低横向平均力55.76%,轴向平均力45.87%,弯矩57.36%。此外,该方法在初始穿刺和最终穿刺位置之间具有较小的针尖位移,从而实现了相当的曲率。这些力、力矩和位移的减少可以使组织损伤和组织变形最小化。研究结果表明,曲线插入具有机械优势,并为开发先进的大曲率针头提供了有价值的见解,可以在微创手术中更精确地定位。
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引用次数: 0
Long-term performance of a low-modulus acrylic cement in a simulated physiological environment 低模量丙烯酸水泥在模拟生理环境下的长期性能研究
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-29 DOI: 10.1016/j.medengphy.2025.104447
C. Lardilleux , C. Lavergne , F. Marcq , M. Faruch , C. Lacabanne , E. Dantras
A vertebroplasty bone cement, designed to exhibit mechanical properties closer to those of the vertebral bone, was investigated. Indeed, adjacent fractures are attributed to an excessive difference in elastic modulus between the vertebrae and the bone cement implying another operation to repair the newly damaged vertebra. The proposed solution to lower the mechanical properties is the introduction of gelatine in the powder of the cement. This low modulus bone cement was studied by quasi-static compression, dynamic mechanical analysis and fatigue tests. Compression testing showed a significant decrease of the elastic modulus of 33 % after 6 months of immersion in a PBS solution at 37 °C, confirmed by the dynamic mechanical analysis. This analysis also showed that immersion and the introduction of gelatine in the cement influence the relaxations of the PMMA matrix. Fatigue performance was also studied with and without gelatine and showed that it presents a characteristic life of at least 24,600 cycles for a load of 7 MPa. While compression testing indicates a reduction in mechanical performance, all others tests demonstrated that gelatine does not significantly impair the overall properties of acrylic bone cement that still possess sufficient properties to achieve its main goal to stabilise damaged vertebrae.
研究了一种椎体成形术骨水泥,旨在展示更接近椎体骨的机械性能。事实上,相邻骨折是由于椎骨和骨水泥之间的弹性模量差异过大,这意味着需要再次手术修复新损伤的椎骨。提出的降低水泥力学性能的方法是在水泥粉中加入明胶。通过准静态压缩、动态力学分析和疲劳试验对该低模数骨水泥进行了研究。压缩测试表明,在37°C的PBS溶液中浸泡6个月后,弹性模量显著降低33%,这一点得到了动态力学分析的证实。该分析还表明,水泥中的浸没和明胶的引入都会影响PMMA基体的弛豫。还研究了加明胶和不加明胶的疲劳性能,结果表明,在7 MPa的载荷下,它的特征寿命至少为24,600次。虽然压缩测试表明机械性能下降,但所有其他测试都表明明胶不会显著损害丙烯酸骨水泥的整体性能,仍然具有足够的性能来实现其稳定受损椎骨的主要目标。
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引用次数: 0
Refining cell classification for cervical cancer detection using an improved high dimensional feature fusion approach 使用改进的高维特征融合方法改进宫颈癌检测的细胞分类
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-24 DOI: 10.1016/j.medengphy.2025.104445
Seema Singh , Chandrahas Sahu , Pushpendra Singh , Alka Mishra , Santosh Kumar Mishra , Pawan Kumar Patnaik

Introduction

Early and accurate classification of cervical cell images is essential for timely detection and prevention of cervical cancer. Traditional cytological analysis methods, such as manual interpretation of Pap smears, are often labor-intensive and susceptible to human error. Recent advances in deep learning offer promising solutions, yet many existing models lack generalization across datasets and struggle with multi-class classification challenges.

Method

To address these limitations, this work introduces a Modified High-Dimensional Feature Fusion (HDFF) framework. The proposed method integrates normalized feature vectors extracted from seven diverse pre-trained CNN architectures—VGG16, VGG19, ResNet50, XceptionNet, InceptionV3, DenseNet121, and a Lightweight Feature extractor. These features are concatenated to form a unified representation, which is then processed by a fully connected classifier with dropout and batch normalization to enhance generalization and reduce redundancy.

Results

The model is evaluated on four benchmark datasets: Herlev, SIPaKMeD, Mendeley LBC, and Malhari. It achieves an accuracy of up to 99.85 % in binary classification tasks and maintains high precision, recall, F1-score, specificity, and AUC in more complex multi-class settings. On the Herlev dataset, for example, it attains a precision of 0.995, recall of 0.987, and F1-score of 0.985. Compared to existing approaches, the Modified HDFF demonstrates lower misclassification rates and stable performance across class imbalances and dataset variations.

Conclusion

The results confirm the robustness and adaptability of the Modified HDFF framework, making it a reliable candidate for real-world cervical cancer screening. Its ability to generalize across datasets underscores its clinical relevance and diagnostic value.
宫颈细胞图像的早期准确分类对于及时发现和预防宫颈癌至关重要。传统的细胞学分析方法,如人工解释子宫颈抹片检查,往往是劳动密集型的,容易出现人为错误。深度学习的最新进展提供了有希望的解决方案,但许多现有模型缺乏跨数据集的泛化,并且在多类分类方面面临挑战。方法为了解决这些局限性,本研究引入了一种改进的高维特征融合(HDFF)框架。该方法集成了从七个不同的预训练CNN架构(vgg16、VGG19、ResNet50、XceptionNet、InceptionV3、DenseNet121)中提取的归一化特征向量,以及一个轻量级特征提取器。这些特征被连接起来形成一个统一的表示,然后由一个全连接的分类器进行处理,并使用dropout和批处理归一化来增强泛化和减少冗余。结果该模型在Herlev、SIPaKMeD、Mendeley LBC和Malhari四个基准数据集上进行了评估。它在二元分类任务中达到99.85%的准确率,并且在更复杂的多类别设置中保持较高的精度、召回率、f1评分、特异性和AUC。例如,在Herlev数据集上,它的精度为0.995,召回率为0.987,f1分数为0.985。与现有方法相比,改进的HDFF在类不平衡和数据集变化中表现出更低的误分类率和稳定的性能。结论改进HDFF框架具有较强的鲁棒性和适应性,可作为实际宫颈癌筛查的可靠候选框架。它在数据集上的泛化能力强调了它的临床相关性和诊断价值。
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引用次数: 0
Adverse events associated with single-use bronchoscopes: analysis from the MAUDE database 与一次性支气管镜相关的不良事件:来自MAUDE数据库的分析
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-23 DOI: 10.1016/j.medengphy.2025.104443
Xibin Su, Huitong Cheng, Wentao Rao, Chenghong Xue, Donghui Gan, Binjian Liu

Background

Bronchoscopy is an aerosol-generating procedure and associated with a high risk of viral transmission, particularly during the coronavirus disease (COVID-19) pandemic. This circumstance has expedited the adoption of single-use flexible bronchoscopes (SUFBs) due to their potential to mitigate healthcare personnel exposure to SARS-CoV-2 and reduce patient infection risks.

Objective

This study aims at analyzing reported adverse events and complications associated with SUFBs, while delineating recommendations for risk mitigation strategies and ensuring patient safety through an examination of data from the Manufacturer and User Facility Device Experience (MAUDE) database.

Methods

We conducted an evaluation of adverse events related to SUFBs as documented in the FDA MAUDE database from January 1, 2014, to June 30, 2025. All reports were manually reviewed and categorized based on the primary device problem and any associated patient outcomes.

Results

A total of 280 reports were issued regarding SUFBs. Manual review categorized these into 280 primary device-related problems and 286 patient-related outcomes. The primary category of device-related problems predominately pertained to breakage/fracture (85 cases; 30.4 %), and a loss of image/display (57 cases; 20.4 %). Among patient adverse events, the most frequently encountered were foreign bodies and airway obstruction.

Conclusion

Our study augments the existing clinical literature and body of knowledge by providing a comprehensive understanding of potential problems related to SUFBs. It underscores the imperative for continuous surveillance and vigilance to ensure the safety and effectiveness of SUFBs.
背景:支气管镜检查是一种产生气溶胶的过程,与病毒传播的高风险相关,特别是在冠状病毒病(COVID-19)大流行期间。这种情况加速了一次性柔性支气管镜(SUFBs)的采用,因为它们有可能减少医护人员接触SARS-CoV-2并降低患者感染风险。本研究旨在分析报道的与sufb相关的不良事件和并发症,同时通过检查制造商和用户设施设备体验(MAUDE)数据库的数据,描绘风险缓解策略的建议,并确保患者安全。方法我们对2014年1月1日至2025年6月30日FDA MAUDE数据库中记录的与sufb相关的不良事件进行了评估。所有的报告都是根据主要的器械问题和任何相关的患者结果进行人工审查和分类的。结果共发表了280份关于sufb的报告。人工审查将这些分类为280个与器械相关的主要问题和286个与患者相关的结果。器械相关问题的主要类别主要与断裂/骨折(85例,30.4%)和图像/显示丢失(57例,20.4%)有关。在患者不良事件中,最常见的是异物和气道阻塞。结论我们的研究增加了现有的临床文献和知识体系,提供了对与sufb相关的潜在问题的全面了解。它强调了持续监测和警惕的必要性,以确保sufb的安全性和有效性。
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引用次数: 0
PSCT-Net: A parallel symmetric CNN-transformer hybrid network for medical image segmentation PSCT-Net:用于医学图像分割的并行对称CNN-transformer混合网络
IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-09-19 DOI: 10.1016/j.medengphy.2025.104442
Bing Wang , Hao Shi , Zutong Zhao , Shiyin Zhang
The precision of medical image segmentation is important in clinical analysis and diagnosis. CNN-Transformer based hybrid approaches show great potential in medical image segmentation due to their complementarity in modeling local and global contextual dependencies. However, local representations and global representations possess their own distinct structures and semantic characteristics, simplistic or inappropriate fusion strategies are insufficient to leverage their complementary strengths, Impeding the model to achieve optimal segmentation performance. For resolving this dilemma, we proposed A Parallel Symmetric CNN-Transformer Hybrid Network for Medical Image Segmentation (PSCT-Net)that implements a three-phase fusion mechanism to sufficiently and efficiently fuse heterogeneous and complementary features: 1) During the encoding stage, we design a layer-wise feature fusion (LWFF) module efficiently merges both CNN and Transformer learned local and global feature, enabling the network to learn more distinctive multi-scale feature. 2) For skip connections, we introduce a multi-scale feature fusion (MSFF) module to capture spatial and channel dependencies among features from different encoding layers while filtering redundant information through multi-scale feature spatial fusion (MFSF) and multi-scale feature channel fusion (MFCF). 3) In the decode stage, We also adopt a dual-branch architecture and through the LWFF module integrates upsampled features from the same decode layer enables the network to more accurately restore the image resolution information. Additionally, we through the CrossTransformer block further enhance the network's capability in processing boundary details. Comprehensive experiments on four medical datasets demonstrate the superiority, effectiveness, and robustness of our PSCT-Net.
医学图像分割的精度在临床分析和诊断中具有重要意义。基于CNN-Transformer的混合方法在医学图像分割中显示出巨大的潜力,因为它们在建模局部和全局上下文依赖方面具有互补性。然而,局部表示和全局表示具有各自不同的结构和语义特征,过于简单或不恰当的融合策略不足以充分发挥两者的互补优势,阻碍了模型实现最佳的分割性能。为了解决这一难题,我们提出了一种用于医学图像分割的并行对称CNN-Transformer混合网络(PSCT-Net),该网络实现了三阶段融合机制,充分有效地融合异构和互补特征:1)在编码阶段,我们设计了分层特征融合(LWFF)模块,有效地融合CNN和Transformer学习到的局部和全局特征,使网络能够学习到更鲜明的多尺度特征。2)引入多尺度特征融合(MSFF)模块,捕获不同编码层特征之间的空间和信道依赖关系,同时通过多尺度特征空间融合(MFSF)和多尺度特征信道融合(MFCF)过滤冗余信息。3)在解码阶段,我们同样采用双分支架构,通过LWFF模块集成来自同一解码层的上采样特征,使网络能够更准确地还原图像分辨率信息。此外,我们通过crosstrtransformer模块进一步增强了网络处理边界细节的能力。在四个医学数据集上的综合实验证明了我们的PSCT-Net的优越性、有效性和鲁棒性。
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Medical Engineering & Physics
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