Pub Date : 2025-07-24DOI: 10.1016/j.medengphy.2025.104402
Jacob M. Thomas , Jamie B. Hall , Rebecca Bliss , Emily Leary , Stephen P. Sayers , Praveen Rao , Trent M. Guess
Measurable neuromotor control deficits during functional task performance could provide objective criteria to aid in concussion diagnosis. However, many tools which measure these constructs are unidimensional and not clinically feasible. The purpose of this study was to assess the classification accuracy of a machine learning model using features measured by a clinically feasible movement-based assessment system (Mizzou Point-of-care Assessment System (MPASS) between athletes with and without concussion. Forty collegiate athletes participated. Twenty (19.40 ± 1.04 yrs., 11 females) suffered concussion within two weeks of data collection (5.40 ± 3.68 days). Twenty (19.85 ± 1.20 yrs.) sex, sport, and position-matched athletes had no concussions in the past year. All participants completed three 30-second static balance trials with eyes closed on foam surface under both single task and cognitive dual task conditions, four trials of gait under normal, head shaking, and dual task conditions, and reaction time tasks. Kinematics, kinetics, and reaction times were recorded by MPASS. Measures were used as features for a XGBoost machine learning model. Five-fold cross-validation yielded mean (across 5-folds): 82.5 % accuracy, 75 % sensitivity, 90 % specificity, 88.2 % positive predictive value, and 78.3 % negative predictive value. Results indicate promise for using movement-based features from a low-cost movement-based assessment system to improve the objectivity of concussion diagnosis decision-making.
{"title":"A machine learning approach to concussive group classification using discrete outcome measures from a low-cost movement-based assessment system","authors":"Jacob M. Thomas , Jamie B. Hall , Rebecca Bliss , Emily Leary , Stephen P. Sayers , Praveen Rao , Trent M. Guess","doi":"10.1016/j.medengphy.2025.104402","DOIUrl":"10.1016/j.medengphy.2025.104402","url":null,"abstract":"<div><div>Measurable neuromotor control deficits during functional task performance could provide objective criteria to aid in concussion diagnosis. However, many tools which measure these constructs are unidimensional and not clinically feasible. The purpose of this study was to assess the classification accuracy of a machine learning model using features measured by a clinically feasible movement-based assessment system (Mizzou Point-of-care Assessment System (MPASS) between athletes with and without concussion. Forty collegiate athletes participated. Twenty (19.40 ± 1.04 yrs., 11 females) suffered concussion within two weeks of data collection (5.40 ± 3.68 days). Twenty (19.85 ± 1.20 yrs.) sex, sport, and position-matched athletes had no concussions in the past year. All participants completed three 30-second static balance trials with eyes closed on foam surface under both single task and cognitive dual task conditions, four trials of gait under normal, head shaking, and dual task conditions, and reaction time tasks. Kinematics, kinetics, and reaction times were recorded by MPASS. Measures were used as features for a XGBoost machine learning model. Five-fold cross-validation yielded mean (across 5-folds): 82.5 % accuracy, 75 % sensitivity, 90 % specificity, 88.2 % positive predictive value, and 78.3 % negative predictive value. Results indicate promise for using movement-based features from a low-cost movement-based assessment system to improve the objectivity of concussion diagnosis decision-making.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104402"},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.medengphy.2025.104401
Hui Li , Yuetang Li , Zhidong Zhang , Chenyang Xue , Zhenhua Li , Xiaobo Li , Jiuzhang Men
Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction. Graph data structures with varying configurations are subsequently constructed to facilitate more profound insights into the intrinsic information within the data. Additionally, a multi-channel lightweight graph convolutional network (GCN) is devised. This network's core strategy lies in extracting diverse layers of information through parallel branches, integrating local structural information with adaptive weights, and employing attention-weighted fusion to improve classification accuracy and model robustness. The proposed network model achieved 91.68% accuracy, a mean F1 score of 92%, a mean recall rate of 92%, and a mean precision rate of 92% on the pulse dataset. The results demonstrate a marked improvement in pulse classification accuracy, validating the efficacy of this approach while offering new perspectives and methodologies for pulse signal classification research.
{"title":"TPC-GCN: Deep learning for pulse pattern classification in traditional Chinese medicine","authors":"Hui Li , Yuetang Li , Zhidong Zhang , Chenyang Xue , Zhenhua Li , Xiaobo Li , Jiuzhang Men","doi":"10.1016/j.medengphy.2025.104401","DOIUrl":"10.1016/j.medengphy.2025.104401","url":null,"abstract":"<div><div>Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction. Graph data structures with varying configurations are subsequently constructed to facilitate more profound insights into the intrinsic information within the data. Additionally, a multi-channel lightweight graph convolutional network (GCN) is devised. This network's core strategy lies in extracting diverse layers of information through parallel branches, integrating local structural information with adaptive weights, and employing attention-weighted fusion to improve classification accuracy and model robustness. The proposed network model achieved 91.68% accuracy, a mean F1 score of 92%, a mean recall rate of 92%, and a mean precision rate of 92% on the pulse dataset. The results demonstrate a marked improvement in pulse classification accuracy, validating the efficacy of this approach while offering new perspectives and methodologies for pulse signal classification research.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104401"},"PeriodicalIF":1.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed a cascade framework for automated MI diagnosis and localization in echocardiograms. Our method combines deep learning for left ventricle wall segmentation with machine learning classification using clinically relevant features. Specifically, we employ a U-Net architecture for segmentation, followed by a two-stage Random Forest classifier for MI detection and localization. We trained and evaluated our approach on two public datasets – CAMUS and HMC-QU. The proposed method achieved 100 % sensitivity and 89.8 % specificity for segment identification, outperforming single-stage classification methods. To the best of our knowledge, this is the first study to apply a multi-step artificial intelligence system combining segmentation and classification for MI diagnosis from echocardiography. This interpretable cascade framework exhibits high performance for early detection and localization of myocardial infarction, demonstrating potential as a clinical decision support tool.
{"title":"A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography","authors":"Carolina Gomez , Annalisa Letizia , Vincenza Tufano , Filippo Molinari , Massimo Salvi","doi":"10.1016/j.medengphy.2025.104400","DOIUrl":"10.1016/j.medengphy.2025.104400","url":null,"abstract":"<div><div>Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed a cascade framework for automated MI diagnosis and localization in echocardiograms. Our method combines deep learning for left ventricle wall segmentation with machine learning classification using clinically relevant features. Specifically, we employ a U-Net architecture for segmentation, followed by a two-stage Random Forest classifier for MI detection and localization. We trained and evaluated our approach on two public datasets – CAMUS and HMC-QU. The proposed method achieved 100 % sensitivity and 89.8 % specificity for segment identification, outperforming single-stage classification methods. To the best of our knowledge, this is the first study to apply a multi-step artificial intelligence system combining segmentation and classification for MI diagnosis from echocardiography. This interpretable cascade framework exhibits high performance for early detection and localization of myocardial infarction, demonstrating potential as a clinical decision support tool.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104400"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-16DOI: 10.1016/j.medengphy.2025.104399
Geovane José Pereira , Sheila Canevese Rahal , Wendell Monteiro Barboza , Ivan Moroz , Alcides Lopes Leão , Matheus Mesquita Alves , Edson Antonio Capello Sousa , Bruno Agostinho Hernandez
This study aimed to evaluate the near-cortical over-drilling technique on the mechanical behaviour of bone-plate constructs in a rabbit transverse femoral fracture. In vitro biomechanical testing and finite element (FE) models were used for analyses. Rabbits' bones (n = 14) were divided into two groups: G1 - without near-cortical over-drilling, and G2 - with near-cortical over-drilling. Locking stainless-steel plates composed of five holes with titanium bushings were used. A compression test was carried out with load applied eccentrically to the femoral head at a rate of 5 mm/min with load cell capacity of 500 kgf. FE model was created to evaluate differences in stress distributions between G1 and G2. In the vitro tests, the maximum load supported by G2 was statistically higher than G1 (p-value = 0.01 < 0.05), whilst there was no significant difference between the groups in bending stiffness (p-value = 0.12 > 0.05). FE models demonstrated similar behaviour to experimental data in terms of stiffness and biomechanical behaviour for either G1 or G2 (p-value = 0.09 > 0.05). Stress levels were higher for G1, and stress concentration areas were at the experimentally fractured sites. No evident pattern of fracture or stress distribution was observed in the bone for G2. In conclusion, over-drilling increased the maximum load-bearing capacity with a slight decrease in overall stiffness, which could potentially improve bone healing.
{"title":"Study of a near-cortical over-drilling technique on plate constructs with a conical locking system in a rabbit femoral fracture using a finite element model","authors":"Geovane José Pereira , Sheila Canevese Rahal , Wendell Monteiro Barboza , Ivan Moroz , Alcides Lopes Leão , Matheus Mesquita Alves , Edson Antonio Capello Sousa , Bruno Agostinho Hernandez","doi":"10.1016/j.medengphy.2025.104399","DOIUrl":"10.1016/j.medengphy.2025.104399","url":null,"abstract":"<div><div>This study aimed to evaluate the near-cortical over-drilling technique on the mechanical behaviour of bone-plate constructs in a rabbit transverse femoral fracture. In vitro biomechanical testing and finite element (FE) models were used for analyses. Rabbits' bones (<em>n</em> = 14) were divided into two groups: G1 - without near-cortical over-drilling, and G2 - with near-cortical over-drilling. Locking stainless-steel plates composed of five holes with titanium bushings were used. A compression test was carried out with load applied eccentrically to the femoral head at a rate of 5 mm/min with load cell capacity of 500 kgf. FE model was created to evaluate differences in stress distributions between G1 and G2. In the vitro tests, the maximum load supported by G2 was statistically higher than G1 (p-value = 0.01 < 0.05), whilst there was no significant difference between the groups in bending stiffness (p-value = 0.12 > 0.05). FE models demonstrated similar behaviour to experimental data in terms of stiffness and biomechanical behaviour for either G1 or G2 (p-value = 0.09 > 0.05). Stress levels were higher for G1, and stress concentration areas were at the experimentally fractured sites. No evident pattern of fracture or stress distribution was observed in the bone for G2. In conclusion, over-drilling increased the maximum load-bearing capacity with a slight decrease in overall stiffness, which could potentially improve bone healing.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104399"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-13DOI: 10.1016/j.medengphy.2025.104398
E. Velarde-Reyes , J.C. Santos-Ceballos , A. Torres-Fortuny , R. Cabal-Rodríguez , Y. Pantoja-Gómez , E. Martínez-Montes , A. Regueiro-Gómez
Congenital hearing loss is a significant health problem, with a worldwide incidence of >6 per 1000 live births. Late diagnosis will delay appropriate treatment, leading to potential neurodevelopment problems. Early diagnosis requires neonatal hearing screening, where one of the most used techniques is automated Auditory Brainstem Responses (aABR). Most aABR methods utilize statistical approaches to analyze the signal's temporal or spectral parameters. While both approaches are widely used, the former is susceptible to noise/artifacts, and the latter lack of analysis of the latencies of the different waves. This work aims to develop, by combining existing methods, an aABR detection algorithm that analyzes the signal in the time domain and improves the performance of the single methods, even in the presence of long latencies of wave V. The development of the algorithm involved evaluating three methods and their combinations in a pilot study. Finally, the best variant was validated in a clinical trial with 300 neonates. The validation results confirmed a specificity of 94.11 % and a sensitivity of 100 %, similar to other studies reported in the literature. These results demonstrated that the proposed algorithm is an effective tool for detecting hearing loss in neonates.
{"title":"Combining algorithms for the automated detection of auditory brainstem responses in newborns","authors":"E. Velarde-Reyes , J.C. Santos-Ceballos , A. Torres-Fortuny , R. Cabal-Rodríguez , Y. Pantoja-Gómez , E. Martínez-Montes , A. Regueiro-Gómez","doi":"10.1016/j.medengphy.2025.104398","DOIUrl":"10.1016/j.medengphy.2025.104398","url":null,"abstract":"<div><div>Congenital hearing loss is a significant health problem, with a worldwide incidence of >6 per 1000 live births. Late diagnosis will delay appropriate treatment, leading to potential neurodevelopment problems. Early diagnosis requires neonatal hearing screening, where one of the most used techniques is automated Auditory Brainstem Responses (aABR). Most aABR methods utilize statistical approaches to analyze the signal's temporal or spectral parameters. While both approaches are widely used, the former is susceptible to noise/artifacts, and the latter lack of analysis of the latencies of the different waves. This work aims to develop, by combining existing methods, an aABR detection algorithm that analyzes the signal in the time domain and improves the performance of the single methods, even in the presence of long latencies of wave V. The development of the algorithm involved evaluating three methods and their combinations in a pilot study. Finally, the best variant was validated in a clinical trial with 300 neonates. The validation results confirmed a specificity of 94.11 % and a sensitivity of 100 %, similar to other studies reported in the literature. These results demonstrated that the proposed algorithm is an effective tool for detecting hearing loss in neonates.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104398"},"PeriodicalIF":1.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Triply periodic minimal surfaces have garnered significant interest in the field of biomaterial scaffolds due to their unique structural properties, including a high surface-to-volume (S/V) ratio, tunable permeability, and the potential for enhanced biocompatibility. Bone scaffolds necessitate specific features to effectively support tissue regeneration. This study examines the permeability and active cell proliferation area of advanced Triply Periodic Minimal Surface (TPMS) lattice structures, focusing on a novel lattice design. The novel design integrates characteristics of the Gyroid and Schwarz-D TPMS, aiming to enhance permeability and increase the active cell proliferation area by leveraging a higher S/V ratio. A comparative analysis is performed at 70 % porosity, evaluating lattice configurations with angular variations ranging from 0° to 90° Computational fluid dynamics simulations are employed to calculate the pressure drop across the lattice structures at a flow rate of 5 ml/min, with permeability determined using Darcy's law. The proposed lattice design at a 45° angle demonstrates superior performance by achieving an optimal balance between permeability (2.97631E-08) and active cell proliferation area (1351.89), enabled by its higher surface-to-volume ratio (value). The internal curvatures of the proposed lattice design promote a substantial active cell proliferation area. This geometric customization highlights the potential of advanced lattice designs in enhancing bio-implant functionality and supporting tissue regeneration.
{"title":"Customization of existing TPMS lattices to enhance biocompatibility and active cell proliferation area","authors":"Richa Thakur , Pankaj Agarwal , Ashish Manoria , Chandra Pal Singh , Naresh","doi":"10.1016/j.medengphy.2025.104397","DOIUrl":"10.1016/j.medengphy.2025.104397","url":null,"abstract":"<div><div>Triply periodic minimal surfaces have garnered significant interest in the field of biomaterial scaffolds due to their unique structural properties, including a high surface-to-volume (S/V) ratio, tunable permeability, and the potential for enhanced biocompatibility. Bone scaffolds necessitate specific features to effectively support tissue regeneration. This study examines the permeability and active cell proliferation area of advanced Triply Periodic Minimal Surface (TPMS) lattice structures, focusing on a novel lattice design. The novel design integrates characteristics of the Gyroid and Schwarz-D TPMS, aiming to enhance permeability and increase the active cell proliferation area by leveraging a higher S/V ratio. A comparative analysis is performed at 70 % porosity, evaluating lattice configurations with angular variations ranging from 0° to 90° Computational fluid dynamics simulations are employed to calculate the pressure drop across the lattice structures at a flow rate of 5 ml/min, with permeability determined using Darcy's law. The proposed lattice design at a 45° angle demonstrates superior performance by achieving an optimal balance between permeability (2.97631E-08) and active cell proliferation area (1351.89), enabled by its higher surface-to-volume ratio (value). The internal curvatures of the proposed lattice design promote a substantial active cell proliferation area. This geometric customization highlights the potential of advanced lattice designs in enhancing bio-implant functionality and supporting tissue regeneration.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104397"},"PeriodicalIF":1.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-12DOI: 10.1016/j.medengphy.2025.104395
Yuhao Gao , Tingwei Liu , Jinyu Yang , Ya Wang , Bowen Peng , Shihao Tang , Xiaodong Yang , Yajie Xu
Benchtop Nuclear Magnetic Resonance (BNMR) technology has gained increasing attention in chemical and pharmaceutical applications due to its compact configuration and operational flexibility. However, the implementation of conventional commercial gradient amplifiers in BNMR systems remains challenging because of their excessive power consumption and bulky dimensions. To address these limitations, this study presents a novel gradient amplifier design optimized for BNMR applications, characterized by a satisfactory linearity in 0–2.5A output current range (slope: 0.9205, offset: 0.0359 and determination coefficient: 0.9963) and achieving a rise time of 1.41μs across dynamic output conditions (60 %, 80 %, 100 % amplitude range). Experimental validation confirms that these technical improvements satisfy the critical requirements for practical BNMR implementations.
{"title":"Design of a low-power gradient amplifier for benchtop nuclear magnetic resonance spectrometer","authors":"Yuhao Gao , Tingwei Liu , Jinyu Yang , Ya Wang , Bowen Peng , Shihao Tang , Xiaodong Yang , Yajie Xu","doi":"10.1016/j.medengphy.2025.104395","DOIUrl":"10.1016/j.medengphy.2025.104395","url":null,"abstract":"<div><div>Benchtop Nuclear Magnetic Resonance (BNMR) technology has gained increasing attention in chemical and pharmaceutical applications due to its compact configuration and operational flexibility. However, the implementation of conventional commercial gradient amplifiers in BNMR systems remains challenging because of their excessive power consumption and bulky dimensions. To address these limitations, this study presents a novel gradient amplifier design optimized for BNMR applications, characterized by a satisfactory linearity in 0–2.5A output current range (slope: 0.9205, offset: 0.0359 and determination coefficient: 0.9963) and achieving a rise time of 1.41μs across dynamic output conditions (60 %, 80 %, 100 % amplitude range). Experimental validation confirms that these technical improvements satisfy the critical requirements for practical BNMR implementations.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104395"},"PeriodicalIF":1.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1016/j.medengphy.2025.104394
Ghislain Sema , Shaun Zamani , Thanasis Touris , Frederique Norpetlian , Lauren Whitney , Annie Zhao , Celina Zhou , Santosh Konangi , Muhammad Sami
Introduction
Benchtop and animal models have traditionally been used to study the propagation of Onyx Liquid Embolic Systems (Onyx) used in the treatment of brain arteriovenous malformations (AVM). However, such models are costly, do not provide sufficient detail to elucidate how variations in Onyx viscosity alter flow dynamics, and rely on some trial-and-error, resulting in elongated timelines for product development.
Objectives
The goal of this study was to leverage Computational Fluid Dynamics (CFD) simulations to predict the behavior of different Onyx formulations. The key objectives were to: 1) validate the distal penetration results from CFD simulations with existing data from bench experiments, 2) compare the flow characteristics of Onyx formulations with differing viscosities in a blood vessel, 3) elucidate the impact of viscosity on distal penetration, and 4) understand how injection location affects distal penetration.
Methods
Using two-dimensional (2D) CFD simulations, we evaluated the propagation of two Onyx formulations (Onyx 18 and Onyx 34) inside a virtual neurovasculature filled with flowing water to mimic the presence of blood in blood vessels. Onyx was assumed to be a mixture of DMSO and EVOH. A physics-based model was developed to account for the change in viscosity of Onyx resulting from migration of DMSO in Onyx to the surrounding fluid (water). Navier–Stokes equations were solved using the commercially-available, finite-volume CFD code, Ansys Fluent. The mixture multiphase model in Fluent was used to track the evolution of the two fluids (Onyx and water), and a species transport equation was solved to account for mass transfer of DMSO from Onyx to water.
Results
The multiphase, multispecies flow simulations were validated by comparing the distal penetration after reflux with available experimental results from bench tests. The predictions from the simulation capture the lava-like flow behavior of Onyx and closely match the experimental data of distal penetration. As expected, lower viscosity Onyx 18 penetrated more distally than Onyx 34 when evaluated with the same degree of reflux. Next, from the simulation results, the impact of viscosity change and the impact of injection location were analyzed.
Key conclusions
Computational modeling and simulation can be used to create and analyze in-silico models representing physical systems and rapidly perform large numbers of tests to evaluate the different resulting outcomes without the need to build analogous physical prototypes. To the best of our knowledge, this is the first study to provide validation of multiphase CFD simulations against benchtop experimental data for Onyx embolization.
{"title":"Computational fluid dynamics (CFD) modelling of liquid embolic agents (Onyx) used in brain arteriovenous malformation (AVM) treatment to predict the distal penetration behavior","authors":"Ghislain Sema , Shaun Zamani , Thanasis Touris , Frederique Norpetlian , Lauren Whitney , Annie Zhao , Celina Zhou , Santosh Konangi , Muhammad Sami","doi":"10.1016/j.medengphy.2025.104394","DOIUrl":"10.1016/j.medengphy.2025.104394","url":null,"abstract":"<div><h3>Introduction</h3><div>Benchtop and animal models have traditionally been used to study the propagation of Onyx Liquid Embolic Systems (Onyx) used in the treatment of brain arteriovenous malformations (AVM). However, such models are costly, do not provide sufficient detail to elucidate how variations in Onyx viscosity alter flow dynamics, and rely on some trial-and-error, resulting in elongated timelines for product development.</div></div><div><h3>Objectives</h3><div>The goal of this study was to leverage Computational Fluid Dynamics (CFD) simulations to predict the behavior of different Onyx formulations. The key objectives were to: 1) validate the distal penetration results from CFD simulations with existing data from bench experiments, 2) compare the flow characteristics of Onyx formulations with differing viscosities in a blood vessel, 3) elucidate the impact of viscosity on distal penetration, and 4) understand how injection location affects distal penetration.</div></div><div><h3>Methods</h3><div>Using two-dimensional (2D) CFD simulations, we evaluated the propagation of two Onyx formulations (Onyx 18 and Onyx 34) inside a virtual neurovasculature filled with flowing water to mimic the presence of blood in blood vessels. Onyx was assumed to be a mixture of DMSO and EVOH. A physics-based model was developed to account for the change in viscosity of Onyx resulting from migration of DMSO in Onyx to the surrounding fluid (water). Navier–Stokes equations were solved using the commercially-available, finite-volume CFD code, Ansys Fluent. The mixture multiphase model in Fluent was used to track the evolution of the two fluids (Onyx and water), and a species transport equation was solved to account for mass transfer of DMSO from Onyx to water.</div></div><div><h3>Results</h3><div>The multiphase, multispecies flow simulations were validated by comparing the distal penetration after reflux with available experimental results from bench tests. The predictions from the simulation capture the lava-like flow behavior of Onyx and closely match the experimental data of distal penetration. As expected, lower viscosity Onyx 18 penetrated more distally than Onyx 34 when evaluated with the same degree of reflux. Next, from the simulation results, the impact of viscosity change and the impact of injection location were analyzed.</div></div><div><h3>Key conclusions</h3><div>Computational modeling and simulation can be used to create and analyze <em>in-silico</em> models representing physical systems and rapidly perform large numbers of tests to evaluate the different resulting outcomes without the need to build analogous physical prototypes. To the best of our knowledge, this is the first study to provide validation of multiphase CFD simulations against benchtop experimental data for Onyx embolization.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104394"},"PeriodicalIF":1.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-09DOI: 10.1016/j.medengphy.2025.104386
Omer Sahin Simsek , Mustafa Kutlu
Balance is a critical component of daily activities and overall quality of life. This study aims to develop a cost-effective exercise system for the rehabilitation of balance disorders by combining a sensor module with target-oriented video games. The system, designed using a microcontroller-controlled sensor module and Unity game engine, features a game component that provides visual feedback and is synchronized with the platform movements. The system detects the patient's movements on the platform and translates them into simultaneous movements of the game character, enhancing both therapy participation and patient motivation. A clinical trial was conducted with 36 patients suffering from orthopedic and neurological balance disorders. The Intrinsic Motivation Inventory (IMI) was used to analyze the scores obtained during the clinical evaluation. The results indicate that 91.96% of participants found the system engaging, and 97.70% believed it would contribute positively to their treatment process. The results indicate that game-based balance exercises contribute to rehabilitation by increasing patient motivation. This study highlights the potential of target-oriented games in balance therapy and suggests that future improvements, such as incorporating patient monitoring features and advanced hardware, along with 3D game designs, could further enhance the system's effectiveness.
{"title":"Goal-oriented balance rehabilitation system for balance disorder","authors":"Omer Sahin Simsek , Mustafa Kutlu","doi":"10.1016/j.medengphy.2025.104386","DOIUrl":"10.1016/j.medengphy.2025.104386","url":null,"abstract":"<div><div>Balance is a critical component of daily activities and overall quality of life. This study aims to develop a cost-effective exercise system for the rehabilitation of balance disorders by combining a sensor module with target-oriented video games. The system, designed using a microcontroller-controlled sensor module and Unity game engine, features a game component that provides visual feedback and is synchronized with the platform movements. The system detects the patient's movements on the platform and translates them into simultaneous movements of the game character, enhancing both therapy participation and patient motivation. A clinical trial was conducted with 36 patients suffering from orthopedic and neurological balance disorders. The Intrinsic Motivation Inventory (IMI) was used to analyze the scores obtained during the clinical evaluation. The results indicate that 91.96% of participants found the system engaging, and 97.70% believed it would contribute positively to their treatment process. The results indicate that game-based balance exercises contribute to rehabilitation by increasing patient motivation. This study highlights the potential of target-oriented games in balance therapy and suggests that future improvements, such as incorporating patient monitoring features and advanced hardware, along with 3D game designs, could further enhance the system's effectiveness.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104386"},"PeriodicalIF":1.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-08DOI: 10.1016/j.medengphy.2025.104396
Muwei Jian , Nan Yang , Chengzhan Zhu
Currently, colonoscopy stands as the most efficient approach for detecting colorectal polyps. In clinical diagnosis, colorectal cancer is closely related to colorectal polyps. Therefore, precise segmentation of polyps holds paramount importance for the early detection and clinical diagnosis of colorectal cancer. Among conventional segmentation methods, multi-layer feature extraction is prone to ignore shallow features, while the segmentation of diminutive polyps perpetually depends on shallow features. Meanwhile, some polyps are frequently hide confusingly in the background due to their own characteristics, resulting in high similarity and low contrast in the anterior and posterior views, which causes an aggravation of distinguishing colorectal polyps during segmentation. In this work, we depict a multi-attention built upon polyp automatic segmentation network, which is called multi-attention network (MANet). In detail, we first implement the shallow feature extraction module (SFEM) to augment the representation ability of diminutive polyps. Then, to conquer the visual confusion of background similarity in the polyp region, a camouflage identification module (CIM) is devised to better identify the polyp region and assisted in segmentation of polyps. Finally, CIM is combined with the extracted shallow features to ameliorate the accuracy of polyp segmentation. Qualitative evaluation on five challenging datasets shows that our proposed multi-attention-based network model shows promising segmentation accuracy, especially in detecting small polyps with low contrast.
{"title":"MANet: multi-attention network for polyp segmentation","authors":"Muwei Jian , Nan Yang , Chengzhan Zhu","doi":"10.1016/j.medengphy.2025.104396","DOIUrl":"10.1016/j.medengphy.2025.104396","url":null,"abstract":"<div><div>Currently, colonoscopy stands as the most efficient approach for detecting colorectal polyps. In clinical diagnosis, colorectal cancer is closely related to colorectal polyps. Therefore, precise segmentation of polyps holds paramount importance for the early detection and clinical diagnosis of colorectal cancer. Among conventional segmentation methods, multi-layer feature extraction is prone to ignore shallow features, while the segmentation of diminutive polyps perpetually depends on shallow features. Meanwhile, some polyps are frequently hide confusingly in the background due to their own characteristics, resulting in high similarity and low contrast in the anterior and posterior views, which causes an aggravation of distinguishing colorectal polyps during segmentation. In this work, we depict a multi-attention built upon polyp automatic segmentation network, which is called multi-attention network (MANet). In detail, we first implement the shallow feature extraction module (SFEM) to augment the representation ability of diminutive polyps. Then, to conquer the visual confusion of background similarity in the polyp region, a camouflage identification module (CIM) is devised to better identify the polyp region and assisted in segmentation of polyps. Finally, CIM is combined with the extracted shallow features to ameliorate the accuracy of polyp segmentation. Qualitative evaluation on five challenging datasets shows that our proposed multi-attention-based network model shows promising segmentation accuracy, especially in detecting small polyps with low contrast.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104396"},"PeriodicalIF":1.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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