Biomedical engineering advances最新文献_第2页

Lubrication to reduce tissue shear loading - effects on comfort perception and tissue damage when wearing a facemask 润滑以减少组织剪切负荷-对舒适度的影响和戴口罩时的组织损伤

Biomedical engineering advances Pub Date : 2026-06-01 Epub Date: 2026-01-15 DOI: 10.1016/j.bea.2026.100208

Urvi Sonawane , Jack Hayes , Mary J. Morrell , Marc A. Masen

{"title":"Lubrication to reduce tissue shear loading - effects on comfort perception and tissue damage when wearing a facemask","authors":"Urvi Sonawane , Jack Hayes , Mary J. Morrell , Marc A. Masen","doi":"10.1016/j.bea.2026.100208","DOIUrl":"10.1016/j.bea.2026.100208","url":null,"abstract":"<div><div>Patients with respiratory disease often require breathing support delivered via a nasal or facemask. The use of such a mask causes continued loading of the skin which can result in discomfort or skin injury, which may lead to low adherence to treatment. It is hypothesised that lubricating the mask-skin interface will reduce shear stress, thus reducing the load on the skin. The aim of this study was to determine whether the application of a novel lubricant to the mask improved discomfort and/or skin injury measured using three outcomes: subjective comfort, erythema, and the presence of epidermal interleukins.</div><div>Ten healthy participants were randomised to wear a lubricated or unlubricated facemask for one hour. Each participant switched over after a one-hour washout period. Subjective comfort was measured using visual analogue scales. Erythema was quantified from facial photographs and interleukins were obtained using tape stripping and ELISA assay analysis. Results show that the subjective comfort significantly improved after one hour with the application of a novel lubricant, compared to no lubrication (p=0.015), however erythema and interleukins were not significantly different. As comfort perception may affect adherence, this novel lubricant may be beneficial in the clinical care of those needing to wear facemasks.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"11 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Progress, challenges and opportunities of using artificial intelligence in additive manufacturing 人工智能在增材制造中的应用进展、挑战和机遇

Biomedical engineering advances Pub Date : 2026-06-01 Epub Date: 2026-05-22 DOI: 10.1016/j.bea.2026.100223

Thywill Cephas Dzogbewu , Rengith Baby Kuriakose , Deon Johan de Beer

{"title":"Progress, challenges and opportunities of using artificial intelligence in additive manufacturing","authors":"Thywill Cephas Dzogbewu , Rengith Baby Kuriakose , Deon Johan de Beer","doi":"10.1016/j.bea.2026.100223","DOIUrl":"10.1016/j.bea.2026.100223","url":null,"abstract":"<div><div>Additive Manufacturing (AM) has transformed modern manufacturing by enabling the creation of complex, customized structures with specific geometrical configurations. Despite its revolutionary impact, AM faces significant challenges such as optimizing process parameters, material customization, quality assurance, and reproducibility. Each AM process involves over 250 parameters that must be tailored for different materials, making the process labor-intensive and reliant on highly skilled professionals. To address these issues, Artificial Intelligence (AI) and Machine Learning (ML) are being explored as transformative tools. AI, particularly through deep neural networks (DNN) and artificial neural networks (ANN), has shown potential to simplify AM processes, automate decision-making, and enhance manufacturing efficiency. These models can learn from large datasets to predict optimal process parameters, thus reducing human dependency and improving product quality. The synergy between AI and AM is promising in areas such as process optimization, defect detection, material development, quality control, topology optimization, and predictive maintenance. By integrating AI-driven systems, AM can evolve into a more intelligent, data-driven production environment where parameters are optimized in real-time, improving reproducibility and reducing defects. Combining AI with AM paves the way for more efficient, innovative, and scalable manufacturing, particularly in the context of Industry 4.0.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"11 ","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pulse signal-based sleep classification using multiple signal processing methods 基于脉冲信号的睡眠分类采用多种信号处理方法

Biomedical engineering advances Pub Date : 2026-06-01 Epub Date: 2026-06-03 DOI: 10.1016/j.bea.2026.100224

Yu Zhan, Hongliang Lv, Bingpeng Zhan, Renpeng Dong, Yirun Liu, Weiting Liu

{"title":"Pulse signal-based sleep classification using multiple signal processing methods","authors":"Yu Zhan, Hongliang Lv, Bingpeng Zhan, Renpeng Dong, Yirun Liu, Weiting Liu","doi":"10.1016/j.bea.2026.100224","DOIUrl":"10.1016/j.bea.2026.100224","url":null,"abstract":"<div><div>Sleep plays a crucial role in physiological recovery, with different sleep stages exhibiting varying physiological characteristics. However, the complexity and diversity of physiological signals during sleep pose challenges to sleep scoring. Current methods for sleep stage classification are often costly and cumbersome. To address this, we propose a novel pulse signal-based approach that leverages multiple signal processing technology for accurate and portable sleep staging. We obtain pulse signals from both MIT-BIH PSG database and our self-developed pressure sensor. In our approach, we introduce a combination of feature extraction using singular spectrum analysis (SSA) and dimensionality reduction through principal component analysis (PCA). We then evaluate the effectiveness of four clustering algorithms, ultimately selecting subspace clustering, which demonstrated the best performance. Validation on 18 subjects from the MIT-BIH database and 8 volunteers yielded an accuracy above 80%, while maintaining lower hardware complexity. This demonstrates the success of our pulse signal-based method using SSA-PCA signal processing algorithms.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"11 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OMG-RL: Offline Model-based Guided Reward Learning for heparin treatment OMG-RL：基于离线模型的肝素治疗引导奖励学习

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-10-21 DOI: 10.1016/j.bea.2025.100198

Yooseok Lim , Sujee Lee

{"title":"OMG-RL: Offline Model-based Guided Reward Learning for heparin treatment","authors":"Yooseok Lim , Sujee Lee","doi":"10.1016/j.bea.2025.100198","DOIUrl":"10.1016/j.bea.2025.100198","url":null,"abstract":"<div><div>Accurate medication dosing plays a critical role in the therapeutic process. Recent studies have investigated the application of Reinforcement Learning (RL) to optimize administration strategies. In RL, the definition of a reward function is indispensable; however, reliance on a limited set of explicitly defined rewards fails to capture the heterogeneity of clinical characteristics across patients. Moreover, the wide range of medications used in practice makes it impractical to design a dedicated reward function for each drug. To address this challenge, we propose learning a reward network that reflects clinicians’ therapeutic intentions, moving beyond predefined reward functions. In this study, we introduce Offline Model-based Guided Reward Learning (OMG-RL), an offline Inverse Reinforcement Learning (IRL) method. By incorporating a dynamic model into sample-based Maximum Entropy IRL, the method learns a parametrized reward network from offline data, enhancing the agent’s policy. We validate OMG-RL on the clinically significant heparin dosing task. Our results demonstrate that the agent is positively reinforced not only in terms of the recovered reward but also in activated partial thromboplastin time (aPTT), a key laboratory test for monitoring heparin effects. These findings suggest that the OMG-RL effectively captures clinicians’ therapeutic intentions. More broadly, our approach provides a general framework for RL-based medication dosing.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning based radiomics analysis of SPECT images in predicting neuroendocrine tumors 基于机器学习的SPECT图像放射组学分析预测神经内分泌肿瘤

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-10-20 DOI: 10.1016/j.bea.2025.100197

Mehdi Ghazizadeh , Mahasti Amoui , Mohammad Reza Deevband , Kamran Aryana , Farivash Karamian , Abolhasan Divband , Zahra Mahboubi-Fooladi , Alireza Montazerabadi , Esmail Jafari , Alireza Zirak , Meysam Tavakoli

{"title":"Machine learning based radiomics analysis of SPECT images in predicting neuroendocrine tumors","authors":"Mehdi Ghazizadeh , Mahasti Amoui , Mohammad Reza Deevband , Kamran Aryana , Farivash Karamian , Abolhasan Divband , Zahra Mahboubi-Fooladi , Alireza Montazerabadi , Esmail Jafari , Alireza Zirak , Meysam Tavakoli","doi":"10.1016/j.bea.2025.100197","DOIUrl":"10.1016/j.bea.2025.100197","url":null,"abstract":"<div><h3>Purpose</h3><div>Over the past few decades, the prevalence of neuroendocrine tumors (NETs) has significantly increased. Current pathological assessment methods often rely on invasive procedures. This study aims to develop machine learning (ML)-based radiomics models using SPECT images to predict grade 1 and 2 NETs and assess the Ki-67 index, particularly in cases with grade discrepancy.</div></div><div><h3>Materials and Methods</h3><div>We developed a retrospective research involving 144 patients with pathologically confirmed NETs collected from five nuclear medicine centers. The dataset included patients with available Ki-67 index values and those exhibiting discrepancies between their Ki-67 index and tumor grade (i.e., low Ki-67 index and intermediate grade). Radiomic features were extracted from SPECT images, and feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Radiomic signature models were then constructed using a decision tree classifier.</div></div><div><h3>Results</h3><div>The prediction of tumor grade in absence of Ki-67 index resulted in area under the curve (AUC) of 0.83, in the presence of Ki-67 index with discrepancy an AUC of 0.86, and in presence of Ki-67 index without discrepancy an AUC of 0.99.</div></div><div><h3>Conclusion</h3><div>The proposed radiomics model based on SPECT images, and machine learning can effectively predict and assess classification of NET grade. Although Ki-67 has been highly recommended as a strong predictive feature in previous studies, our findings suggest that its predictive value diminishes in cases with grade discrepancies, such as low Ki-67 index paired with intermediate grade.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Architected Materials for External Breast Prostheses 乳房外部假体结构材料的研究进展

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-04-27 DOI: 10.1016/j.bea.2025.100168

Sibo Zhang , Jennifer Xue , Xiao Yu , Yunlan Zhang

{"title":"Development of Architected Materials for External Breast Prostheses","authors":"Sibo Zhang , Jennifer Xue , Xiao Yu , Yunlan Zhang","doi":"10.1016/j.bea.2025.100168","DOIUrl":"10.1016/j.bea.2025.100168","url":null,"abstract":"<div><div>Breast cancer remains the most prevalent cancer among women, with mastectomy often performed as a standard treatment or preventive measure. Post-surgery, breast prostheses are essential for restoring appearance, balance, and confidence. However, conventional silicone external prostheses often fall short in addressing key challenges such as comfort, weight, heat management, and personalization. This study presents a new design framework for External Breast Prostheses (EBPs) utilizing gyroid-based architected materials fabricated through additive manufacturing. By mimicking the density, thermal conductivity, and mechanical properties of natural breast tissue, the proposed design achieves advancements in realism and functionality. Tailored gyroid unit cell geometries enable precise control over weight distribution, thermal regulation, and stiffness, aligning with the properties of natural tissue. Guided by numerical simulations and validated through experimental testing, this approach produces a lightweight, breathable, and realistic prosthesis that can enhance comfort and functionality. This approach highlights the transformative potential of advanced imaging and 3D printing technologies in creating customizable, high-performance solutions to improve the quality of life for breast cancer patients.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced multiclass blood cell classification using contourlet transform and metaheuristic-optimized deep features with clustering-based decision making 使用contourlet变换和基于聚类决策的元启发式优化深度特征增强多类血细胞分类

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-11-19 DOI: 10.1016/j.bea.2025.100202

Omid Eslamifar , Mohammadreza Soltani , Seyed Mohammad Jalal Rastegr Fatemi

{"title":"Enhanced multiclass blood cell classification using contourlet transform and metaheuristic-optimized deep features with clustering-based decision making","authors":"Omid Eslamifar , Mohammadreza Soltani , Seyed Mohammad Jalal Rastegr Fatemi","doi":"10.1016/j.bea.2025.100202","DOIUrl":"10.1016/j.bea.2025.100202","url":null,"abstract":"<div><div>Accurate classification of white blood cells (WBCs) is critical for the diagnosis of hematological disorders. This paper presents a novel hybrid framework that integrates multi-scale feature extraction, deep learning, metaheuristic optimization, and clustering-based decision making for robust multiclass WBC classification. The proposed method first employs the contourlet transform to decompose microscopic blood smear images, effectively capturing intricate contour and directional edge information in the frequency domain. These features are then sequentially processed by a recurrent neural network (RNN) to model hierarchical dependencies. To enhance discriminative power and reduce computational complexity, the African Vulture Optimization Algorithm (AVOA) is leveraged for optimal feature selection. Finally, a fuzzy clustering-based decision strategy is introduced to refine the classification of five WBC subtypes: lymphocytes, monocytes, eosinophils, basophils, and neutrophils. The framework emphasizes not only high accuracy but also operational efficiency, addressing key requirements for clinical deployment. Experimental evaluation on the Jiangxi Tecom dataset demonstrates superior performance over baseline models, with significant improvements in precision, recall, and F1-score across most cell types. Despite the inherent class imbalance for basophils, the model maintains viable performance, with augmentation techniques identified for future enhancement. The study's primary contribution lies in the unified integration of Contourlet-based feature extraction with deep sequential learning and metaheuristic-driven feature selection, offering a promising computer-aided diagnostic tool for automated hematological analysis.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transverse impacts on long bones: A systematic study 对长骨的横向影响：一项系统研究

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-10-30 DOI: 10.1016/j.bea.2025.100200

Omid Ghafari , Reza Hedayati , Mojtaba Sadighi , Taha Goudarzi

{"title":"Transverse impacts on long bones: A systematic study","authors":"Omid Ghafari , Reza Hedayati , Mojtaba Sadighi , Taha Goudarzi","doi":"10.1016/j.bea.2025.100200","DOIUrl":"10.1016/j.bea.2025.100200","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Despite advancements in modeling bone impact behavior, gaps remain in integrating soft tissue effects and developing comprehensive material models for low-velocity impacts. This study addresses these gaps by developing and validating a finite element model in LS-DYNA to predict bone response under low-velocity transverse impacts—common in daily injuries, sports accidents, and falls.</div></div><div><h3>Methods</h3><div>The model incorporates key parameters such as soft tissue presence, impact velocity, impactor geometry and size, impact direction and angle, impact location, and boundary conditions, and extends its applicability to human model and shin-to-shin impacts trauma.</div></div><div><h3>Results</h3><div>Results closely matched experimental data, confirming model accuracy. Soft tissue prolonged impact duration (250 %) and reduced peak acceleration (61 %), making posterior impacts less damaging, while lateral impacts posed the highest fracture risk. Velocity influenced injury severity more than mass, with higher speeds increasing acceleration and damage. Smaller-diameter impactors reduced acceleration by 38 %. Moreover, conical impactors caused the most severe fractures, absorbing 90 % of impact energy. Boundary conditions played a crucial role, as impact near the constrained points absorbed 80 % of impact energy, leading to localized fractures, while central impacts absorbed only 30-50 %. Among all impact angles, the 90-degree impact maximized energy absorption (66 %).</div></div><div><h3>Conclusions</h3><div>The results of this research highlight the model’s relevance for protective gear design, injury prevention, and rehabilitation research. These findings advance finite element modeling and improve fracture risk assessment in various impact conditions.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biomedical applications of advanced textiles: From wound healing to wearable health monitoring systems 先进纺织品的生物医学应用：从伤口愈合到可穿戴健康监测系统

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-10-31 DOI: 10.1016/j.bea.2025.100199

Md. Riad Hossen , Mohammad Samiul Alam , Sabbir Hasan , Md. Iqbal Hossain

{"title":"Biomedical applications of advanced textiles: From wound healing to wearable health monitoring systems","authors":"Md. Riad Hossen , Mohammad Samiul Alam , Sabbir Hasan , Md. Iqbal Hossain","doi":"10.1016/j.bea.2025.100199","DOIUrl":"10.1016/j.bea.2025.100199","url":null,"abstract":"<div><div>The blending of textile engineering with biomedical science has led to some pretty amazing textiles that are changing the game in healthcare. These innovative materials combine flexibility, breathability, and the ability to adapt structurally, along with biological and electronic features, which opens up new possibilities for things like wound care and health monitoring devices we can wear. Some recent breakthroughs include scaffolds made from silk fibroin, nanofiber dressings made through electrospinning, and multifunctional hydrogel systems that not only help speed up tissue healing but also offer antimicrobial protection. At the same time, there’s been progress with textile-based biosensors and self-powered wearables that continually track vital signs and biochemical indicators, which is a big step forward for personalized medicine and looking after patients remotely. Still, it’s not all smooth sailing; issues like scaling up production, making sure these products last, complying with regulations, and securing data are significant obstacles that need to be tackled before we can fully integrate these biomedical textiles into everyday healthcare practices.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of automated temporal bone segmentation methods 自动颞骨分割方法的系统综述

Biomedical engineering advances Pub Date : 2025-11-01 Epub Date: 2025-09-20 DOI: 10.1016/j.bea.2025.100195

Weijing Li , Sudanthi Wijewickrema , Jan Margeta , Reda Kamraoui , Raabid Hussain , Jean-Marc Gerard

{"title":"A systematic review of automated temporal bone segmentation methods","authors":"Weijing Li , Sudanthi Wijewickrema , Jan Margeta , Reda Kamraoui , Raabid Hussain , Jean-Marc Gerard","doi":"10.1016/j.bea.2025.100195","DOIUrl":"10.1016/j.bea.2025.100195","url":null,"abstract":"<div><div>The temporal bone is a complex anatomical structure crucial for otologic and neurotologic procedures. Accurate segmentation of the temporal bone from computed tomography (CT) and magnetic resonance imaging (MRI) is essential for surgical planning, pathology assessment, and computational modeling. Manual segmentation is time-consuming and subject to inter-observer variability, necessitating the development of automated methods. This systematic review aims to analyze the current state of automated temporal bone segmentation techniques and their performance. A comprehensive search was conducted across PubMed, IEEE Xplore for articles published from 2004 to 2024. A total of 419 articles were reviewed, from which 34 were selected for this study. Among the identified studies, deep learning, particularly convolutional neural networks (CNNs) and U-Net variants, emerged as the dominant approach, consistently outperforming SSM and atlas-based methods. Deep learning models achieved the highest Dice Similarity Coefficient (DSC) and the lowest Hausdorff Distance (HD). Deep learning-based approaches improved automated temporal bone segmentation, with strong performance in segmenting larger structures such as the labyrinth, with Dice score over 0.86. However, the segmentation of smaller anatomical structures, such as stapes and chorda tympani, remains a challenge.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0