Pub Date : 2025-11-01DOI: 10.1016/j.bea.2025.100199
Md. Riad Hossen , Mohammad Samiul Alam , Sabbir Hasan , Md. Iqbal Hossain
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.
{"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}
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.
Methods
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.
Results
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 %).
Conclusions
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.
{"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}
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.
{"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}
Pub Date : 2025-11-01DOI: 10.1016/j.bea.2025.100194
Lukasz Surazynski , Jyri Järvinen , Martti Ilvesmäki , Markus Mäkinen , Heikki J. Nieminen , Miika T. Nieminen , Teemu Myllylä
{"title":"Corrigendum to “A core needle biopsy combined with novel spectroscopic probe for in vivo tissue classification – a pilot study on piglets” [Biomedical Engineering Advances, Available online 2 September 2025, 100191]","authors":"Lukasz Surazynski , Jyri Järvinen , Martti Ilvesmäki , Markus Mäkinen , Heikki J. Nieminen , Miika T. Nieminen , Teemu Myllylä","doi":"10.1016/j.bea.2025.100194","DOIUrl":"10.1016/j.bea.2025.100194","url":null,"abstract":"","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736282","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}
Pub Date : 2025-10-04DOI: 10.1016/j.bea.2025.100196
Mahamodul Hasan Mahadi, Md. Nasif Safwan, Sadia Islam Niha, Souhardo Rahman, M.F. Mridha
Eye diseases such as cataracts, glaucoma, macular degeneration, and diabetic retinopathy significantly impair vision and quality of life, particularly in aging populations, and pose substantial socio-economic challenges. Accurate and timely diagnosis is crucial for mitigating their impact. Deep learning presents a promising solution by leveraging unlabeled data to extract meaningful features and reduce dependence on extensively labeled datasets. However, conventional deep learning models rely on centralized data collection, raising serious concerns about data security and patient privacy. Federated Learning addresses these challenges by enabling collaborative model training across multiple entities without requiring data sharing or ensuring privacy preservation. Our approach integrates EfficientNetB3 as the backbone with Residual Channel Attention and a custom classification head, achieving 94.79% accuracy. Explainable Artificial Intelligence enhances interpretability and transparency. The integration of the model into real-time diagnostic systems holds the potential for advancing clinical applications while maintaining data security and scalability.
{"title":"KbFL-XAI: Explainable knowledge-based federated learning for eye disease diagnosis","authors":"Mahamodul Hasan Mahadi, Md. Nasif Safwan, Sadia Islam Niha, Souhardo Rahman, M.F. Mridha","doi":"10.1016/j.bea.2025.100196","DOIUrl":"10.1016/j.bea.2025.100196","url":null,"abstract":"<div><div>Eye diseases such as cataracts, glaucoma, macular degeneration, and diabetic retinopathy significantly impair vision and quality of life, particularly in aging populations, and pose substantial socio-economic challenges. Accurate and timely diagnosis is crucial for mitigating their impact. Deep learning presents a promising solution by leveraging unlabeled data to extract meaningful features and reduce dependence on extensively labeled datasets. However, conventional deep learning models rely on centralized data collection, raising serious concerns about data security and patient privacy. Federated Learning addresses these challenges by enabling collaborative model training across multiple entities without requiring data sharing or ensuring privacy preservation. Our approach integrates EfficientNetB3 as the backbone with Residual Channel Attention and a custom classification head, achieving 94.79% accuracy. Explainable Artificial Intelligence enhances interpretability and transparency. The integration of the model into real-time diagnostic systems holds the potential for advancing clinical applications while maintaining data security and scalability.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264954","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}
Pub Date : 2025-09-20DOI: 10.1016/j.bea.2025.100195
Weijing Li , Sudanthi Wijewickrema , Jan Margeta , Reda Kamraoui , Raabid Hussain , Jean-Marc Gerard
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.
{"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":"2025-09-20","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}
Pub Date : 2025-09-05DOI: 10.1016/j.bea.2025.100192
Nazifa Zaman Khan , S. Manjura Hoque , Harinarayan Das , Arup Kumar , Rafiqul Islam , Mozammal Hossain
Teeth enamel, composed of calcium and phosphorus, becomes demineralized in contact with beverages and food. The essential component of teeth, enamel, can be remineralized with the use of nano-hydroxyapatite (nHAp) alone or in a solution consisting of nHAp, sodium fluoride (NaF), and polyethylene oxide (PEO) nanocomposite. We divided ten sound-extracted teeth into two groups: Group A consisted of three teeth treated with nHAp colloids, while Group B consisted of seven teeth treated with nHAp-NaF-PEO nanocomposite in solution. We demineralized the teeth of both groups by soaking them in various pH-adjusted demineralizing agents for different periods. We analyzed the morphology and composition of the demineralized teeth by the scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDAX). The teeth specimens were brushed two times/day for about 2 minutes each, with a 12-hours interval between brushing sessions, to remineralize them over four weeks. Periodically, the enamel specimens were placed in distilled water and maintained at 37° C in the CO2 incubator. We analyzed the morphology and composition of the remineralized teeth by SEM and EDAX. The results show that the surface morphology produced by the nHAp-NaF-PEO nanocomposite solution was quite similar to the baseline enamel morphology. We observed an increase in mineral content, namely the Ca/P ratio, in the nHAp-NaF- PEO nanocomposite solution. The nHAp-NaF-PEO nanocomposite solution aids the remineralization of the decayed teeth more effectively than nHAp singly and heals carious lesions. Both nHAp and nHAp-NaF-PEO heals the morphology of carious teeth.
{"title":"Remineralization of demineralized teeth enamel with nHAp and nHAp-NaF-PEO nanocomposite","authors":"Nazifa Zaman Khan , S. Manjura Hoque , Harinarayan Das , Arup Kumar , Rafiqul Islam , Mozammal Hossain","doi":"10.1016/j.bea.2025.100192","DOIUrl":"10.1016/j.bea.2025.100192","url":null,"abstract":"<div><div>Teeth enamel, composed of calcium and phosphorus, becomes demineralized in contact with beverages and food. The essential component of teeth, enamel, can be remineralized with the use of nano-hydroxyapatite (nHAp) alone or in a solution consisting of nHAp, sodium fluoride (NaF), and polyethylene oxide (PEO) nanocomposite. We divided ten sound-extracted teeth into two groups: Group A consisted of three teeth treated with nHAp colloids, while Group B consisted of seven teeth treated with nHAp-NaF-PEO nanocomposite in solution. We demineralized the teeth of both groups by soaking them in various pH-adjusted demineralizing agents for different periods. We analyzed the morphology and composition of the demineralized teeth by the scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDAX). The teeth specimens were brushed two times/day for about 2 minutes each, with a 12-hours interval between brushing sessions, to remineralize them over four weeks. Periodically, the enamel specimens were placed in distilled water and maintained at 37° C in the CO<sub>2</sub> incubator. We analyzed the morphology and composition of the remineralized teeth by SEM and EDAX. The results show that the surface morphology produced by the nHAp-NaF-PEO nanocomposite solution was quite similar to the baseline enamel morphology. We observed an increase in mineral content, namely the Ca/P ratio, in the nHAp-NaF- PEO nanocomposite solution. The nHAp-NaF-PEO nanocomposite solution aids the remineralization of the decayed teeth more effectively than nHAp singly and heals carious lesions. Both nHAp and nHAp-NaF-PEO heals the morphology of carious teeth.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095285","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}
Pub Date : 2025-09-05DOI: 10.1016/j.bea.2025.100193
Joseph Amitrano , Milad Zarrinfar , Marco Giuliani , Kevin Cahill , Mark A. Seeley , Dhruv R. Seshadri
The anterior cruciate ligament (ACL) is critical for stabilizing the knee during high-performance activities. Anterior cruciate ligament reconstruction (ACLR) surgery, combined with rehabilitation, is the standard treatment for tears; however, determining readiness to return to sport (RTS) remains challenging. Traditional RTS assessments often fail to capture physiological recovery, emphasizing the need for precise, objective biomarkers. Near-infrared spectroscopy (NIRS) offers real-time, non-invasive insights into muscle oxygen saturation, providing an objective means to quantify recovery. This study investigated the utility of a wearable NIRS sensor to monitor muscle oxygen saturation levels in two Division 1 football athletes recovering from a torn ACL, with a focus on assessing inter-athlete recovery variability and its implications for RTS decisions. This longitudinal case study monitored muscle oxygen saturation using the Moxy Muscle Oxygen Monitor in the surgical and contralateral legs of two athletes at 1, 3-, 5-, 6-, and 7-months post-surgery during functional exercises (leg raises and quad sets). The study highlights the capacity of NIRS based wearable sensors to capture inter-individual variability over the rehabilitation continuum towards providing real-time physiological insights beyond traditional subjective or qualitative-based assessments. These findings support the integration of wearable technology into lower extremity rehabilitation protocols to enhance recovery evaluations and improve athlete RTS.
{"title":"Wearable near-infrared spectroscopy device to quantify rehabilitation following anterior cruciate ligament reconstruction: A case study on division I collegiate football athletes","authors":"Joseph Amitrano , Milad Zarrinfar , Marco Giuliani , Kevin Cahill , Mark A. Seeley , Dhruv R. Seshadri","doi":"10.1016/j.bea.2025.100193","DOIUrl":"10.1016/j.bea.2025.100193","url":null,"abstract":"<div><div>The anterior cruciate ligament (ACL) is critical for stabilizing the knee during high-performance activities. Anterior cruciate ligament reconstruction (ACLR) surgery, combined with rehabilitation, is the standard treatment for tears; however, determining readiness to return to sport (RTS) remains challenging. Traditional RTS assessments often fail to capture physiological recovery, emphasizing the need for precise, objective biomarkers. Near-infrared spectroscopy (NIRS) offers real-time, non-invasive insights into muscle oxygen saturation, providing an objective means to quantify recovery. This study investigated the utility of a wearable NIRS sensor to monitor muscle oxygen saturation levels in two Division 1 football athletes recovering from a torn ACL, with a focus on assessing inter-athlete recovery variability and its implications for RTS decisions. This longitudinal case study monitored muscle oxygen saturation using the Moxy Muscle Oxygen Monitor in the surgical and contralateral legs of two athletes at 1, 3-, 5-, 6-, and 7-months post-surgery during functional exercises (leg raises and quad sets). The study highlights the capacity of NIRS based wearable sensors to capture inter-individual variability over the rehabilitation continuum towards providing real-time physiological insights beyond traditional subjective or qualitative-based assessments. These findings support the integration of wearable technology into lower extremity rehabilitation protocols to enhance recovery evaluations and improve athlete RTS.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049341","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}
Pub Date : 2025-09-02DOI: 10.1016/j.bea.2025.100191
Lukasz Surazynski , Jyri Järvinen , Martti Ilvesmäki , Markus Mäkinen , Heikki J. Nieminen , Miika T. Nieminen , Teemu Myllylä
Tissue sampling is a primary goal of core needle biopsies (CNB), cancer therapy evaluation, and autoimmune disease assessment. Conventional guidance methods such as ultrasound and MRI suffer from periprocedural tissue‐type insensitivity in complex biopsy targets, motion sensitivity, imaging artifacts and high costs, which may limit their usefulness. Accurate tissue classification and needle guidance during CNB are equally important. Mistakes may lead to sample inadequacies, obscured results, incorrect sampling spots, and ultimately repeated biopsies. To address these challenges, this study investigates the feasibility of a smart CNB probe integrating real-time optical spectroscopy for enhanced tissue characterization during in vivo biopsy utilizing machine learning methods. Ten fabricated probes were tested in vivo on porcine fat, liver, and kidney tissues, demonstrating potential for improving biopsy accuracy. Acquired spectral data enabled effective tissue differentiation, as indicated by the best-performing classification models. LDA classifier with MRMR feature selection reached sensitivity of 87.3 % in classification between liver and fat tissues, where SVM with linear kernel and PCA reached 86.4 % sensitivity in kidney vs fat. These findings suggest that integrating optical spectroscopy into CNB procedures may enhance diagnostic accuracy while mitigating procedural risks.
{"title":"A core needle biopsy combined with novel spectroscopic probe for in vivo tissue classification – A pilot study on piglets","authors":"Lukasz Surazynski , Jyri Järvinen , Martti Ilvesmäki , Markus Mäkinen , Heikki J. Nieminen , Miika T. Nieminen , Teemu Myllylä","doi":"10.1016/j.bea.2025.100191","DOIUrl":"10.1016/j.bea.2025.100191","url":null,"abstract":"<div><div>Tissue sampling is a primary goal of core needle biopsies (CNB), cancer therapy evaluation, and autoimmune disease assessment. Conventional guidance methods such as ultrasound and MRI suffer from periprocedural tissue‐type insensitivity in complex biopsy targets, motion sensitivity, imaging artifacts and high costs, which may limit their usefulness. Accurate tissue classification and needle guidance during CNB are equally important. Mistakes may lead to sample inadequacies, obscured results, incorrect sampling spots, and ultimately repeated biopsies. To address these challenges, this study investigates the feasibility of a smart CNB probe integrating real-time optical spectroscopy for enhanced tissue characterization during in vivo biopsy utilizing machine learning methods. Ten fabricated probes were tested in vivo on porcine fat, liver, and kidney tissues, demonstrating potential for improving biopsy accuracy. Acquired spectral data enabled effective tissue differentiation, as indicated by the best-performing classification models. LDA classifier with MRMR feature selection reached sensitivity of 87.3 % in classification between liver and fat tissues, where SVM with linear kernel and PCA reached 86.4 % sensitivity in kidney vs fat. These findings suggest that integrating optical spectroscopy into CNB procedures may enhance diagnostic accuracy while mitigating procedural risks.</div></div>","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":"10 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018625","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}
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