Annals of Biomedical Engineering最新文献

{"title":"FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement.","authors":"Devin W Laurence, Patricia M Sabin, Analise M Sulentic, Matthew Daemer, Steve A Maas, Jeffrey A Weiss, Matthew A Jolley","doi":"10.1007/s10439-024-03637-3","DOIUrl":"10.1007/s10439-024-03637-3","url":null,"abstract":"<p><strong>Purpose: </strong>Finite element simulations are an enticing tool to evaluate heart valve function; however, patient-specific simulations derived from 3D echocardiography are hampered by several technical challenges. The objective of this work is to develop an open-source method to enforce matching between finite element simulations and in vivo image-derived heart valve geometry in the absence of patient-specific material properties, leaflet thickness, and chordae tendineae structures.</p><p><strong>Methods: </strong>We evaluate FEBio Finite Element Simulations with Shape Enforcement (FINESSE) using three synthetic test cases considering a range of model complexity. FINESSE is then used to estimate the in vivo valve behavior and leaflet strains for three pediatric patients.</p><p><strong>Results: </strong>Our results suggest that FINESSE can be used to enforce finite element simulations to match an image-derived surface and estimate the first principal leaflet strains within <math><mrow><mo>±</mo> <mspace></mspace> <mn>0.03</mn></mrow> </math> strain. Key considerations include: (i) defining the user-defined penalty, (ii) omitting the leaflet commissures to improve simulation convergence, and (iii) emulating the chordae tendineae behavior via prescribed leaflet free edge motion or a chordae emulating force. In all patient-specific cases, FINESSE matched the target surface with median errors of approximately the smallest voxel dimension. Further analysis revealed valve-specific findings, such as the tricuspid valve leaflet strains of a 2-day old patient with HLHS being larger than those of two 13-year old patients.</p><p><strong>Conclusions: </strong>FEBio FINESSE can be used to estimate patient-specific in vivo heart valve leaflet strains. The development of this open-source pipeline will enable future studies to begin linking in vivo leaflet mechanics with patient outcomes.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"241-259"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.","authors":"Ophelie M Herve, Will Flanagan, Jake Kanetis, Bailey Mooney, Thomas J Kremen, David R McAllister, Tyler R Clites","doi":"10.1007/s10439-024-03624-8","DOIUrl":"10.1007/s10439-024-03624-8","url":null,"abstract":"<p><p>Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunately, the mechanics of ligament injury are difficult to study in vivo, and computational studies provide limited insight. The objective of this study was to implement and validate a robotic system capable of reproducing natural six degree-of-freedom clamped-kinematic trajectories on human cadaver knees (meaning that positions and orientations are rigidly controlled and resultant loads are measured). To accomplish this, we leveraged the field's recent access to high-fidelity bone kinematics from dynamic biplanar radiography (DBR), and implemented these kinematics in a coordinate frame built around the knee's natural flexion-extension axis. We assessed our system's capabilities in the context of ACL injury, by moving seven cadaveric knee specimens through kinematics derived from walking, running, drop jump, and ACL injury. We then used robotically simulated clinical stability tests to evaluate the hypothesis that knee stability would be only reduced by the motions intended to injure the knee. Our results show that the structural integrity of the knee was not compromised by non-injurious motions, while the injury motion produced a clinically relevant ACL injury with characteristic anterior and valgus instability. We also demonstrated that our robotic system can provide direct measurements of reaction loads during a variety of motions, and facilitate gross evaluation of ligament failure mechanisms. Clamped-kinematic robotic evaluation of cadaver knees has the potential to deepen understanding of the mechanics of knee ligament injury.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"193-206"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Ureteral Stent Placeable via Standard Procedure for Kidney Pressure Telemetry: An Ex-Vivo Study.","authors":"Mohammad Reza Yousefi Darestani, Dirk Lange, Ben H Chew, Kenichi Takahata","doi":"10.1007/s10439-024-03610-0","DOIUrl":"10.1007/s10439-024-03610-0","url":null,"abstract":"<p><p>This paper reports the first telemetric ureteral stent compatible with common placement procedure, enabling wireless sensing and detection of ureteral obstruction and resultant kidney swelling known as hydronephrosis at an early stage. This sensor-integrated \"intelligent\" ureteral stent is prototyped via the design and fabrication approaches that raise the practicality of the device and tested in a harvested swine kidney-ureter model ex vivo. Leveraging a polymeric double-J stent and micro-electro-mechanical systems technology, the intelligent stent is built by embedding micro pressure sensors and a radiofrequency antenna, forming a resonant circuit that enables wireless kidney pressure monitoring in an operating frequency of 40-50 MHz. The stent device is entirely packaged with Parylene-C for both biocompatibility and electrical insulation of the device in order to function in the real environment including urine, an electrically conductive liquid. A comparison between the results measured in in-vitro and ex-vivo settings show a good match in the sensitivity to applied pressure. In particular, the ex-vivo test in the kidney-ureter model pressurized with artificial urine in a cycled manner demonstrates wireless pressure tracking with a response of 1.3 kHz/mmHg, over pressures up to 37 mmHg that well covers a range of pressure increase known for chronic obstruction. This testing is enabled by the prototype placement into the ex-vivo model using the standard stenting technique and tools without noticeable functional degradation or failures, showing potential compatibility of the device with today's clinical need as a ureteral stent.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"180-192"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Publish or Perish\" Paradigm and Medical Research: Replication Crisis in the Context of Artificial Intelligence Trend.","authors":"Obada Al-Leimon, Malik Eid Juweid","doi":"10.1007/s10439-024-03625-7","DOIUrl":"10.1007/s10439-024-03625-7","url":null,"abstract":"<p><p>The \"publish or perish\" culture in academia has intensified trends in medical research, particularly around artificial intelligence (AI) and machine learning. This letter highlights how the pressure to publish positive findings during research trends, such as artificial intelligence in medicine, exacerbates the replication crisis. Issues like data leakage and lack of cross-institutional validation in AI models, particularly in clinical radiology, raise concerns about their reliability. The letter urges authors, reviewers, and editors to enforce rigorous standards to ensure reproducibility and safeguard the integrity of medical research.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"3-4"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142339687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Vivo Deformation of the Human Basilar Artery.","authors":"Jaemin Kim, Kaiyu Zhang, Gador Canton, Niranjan Balu, Kenneth Meyer, Reza Saber, David Paydarfar, Chun Yuan, Michael S Sacks","doi":"10.1007/s10439-024-03605-x","DOIUrl":"10.1007/s10439-024-03605-x","url":null,"abstract":"<p><p>An estimated 6.8 million people in the United States have an unruptured intracranial aneurysms, with approximately 30,000 people suffering from intracranial aneurysms rupture each year. Despite the development of population-based scores to evaluate the risk of rupture, retrospective analyses have suggested the limited usage of these scores in guiding clinical decision-making. With recent advancements in imaging technologies, artery wall motion has emerged as a promising biomarker for the general study of neurovascular mechanics and in assessing the risk of intracranial aneurysms. However, measuring arterial wall deformations in vivo itself poses several challenges, including how to image local wall motion and deriving the anisotropic wall strains over the cardiac cycle. To overcome these difficulties, we first developed a novel in vivo MRI-based imaging method to acquire cardiac gated images of the human basilar artery (BA) over the cardiac cycle. Next, complete BA endoluminal surfaces from each frame were segmented, producing high-resolution point clouds of the endoluminal surfaces. From these point clouds we developed a novel B-spline-based surface representation, then exploited the local support nature of B-splines to determine the local endoluminal surface strains. Results indicated distinct regional and temporal variations in BA wall deformation, highlighting the heterogeneous nature BA function. These included large circumferential strains (up to <math><mo>∼</mo></math> 20 <math><mo>%</mo></math> ), and small longitudinal strains, which were often contractile and out of phase with the circumferential strains patterns. Of particular interest was the temporal phase lag in the maximum circumferential perimeter length, which indicated that the BA deforms asynchronously over the cardiac cycle. In summary, the proposed method enabled local deformation analysis, allowing for the successful reproduction of local features of the BA, such as regional principal stretches, areal changes, and pulsatile motion. Integrating the proposed method into existing population-based scores has the potential to improve our understanding of mechanical properties of human BA and enhance clinical decision-making.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"83-98"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142139023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Steady-State Metabolic Power in Cerebral Palsy, Stroke, and the Elderly During Walking With and Without Assistive Devices.","authors":"Karl Harshe, Benjamin C Conner, Zachary F Lerner","doi":"10.1007/s10439-024-03614-w","DOIUrl":"10.1007/s10439-024-03614-w","url":null,"abstract":"<p><strong>Purpose: </strong>Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions.</p><p><strong>Methods: </strong>Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data.</p><p><strong>Results: </strong>We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on average across all walking conditions and patient populations, including with assistive devices and on different terrains.</p><p><strong>Conclusion: </strong>This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"99-108"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound Probe Pressure Affects Aortic Wall Stiffness: A Patient-Specific Computational Study in Abdominal Aortic Aneurysms.","authors":"Marta Irene Bracco, Ali Akbar Karkhaneh Yousefi, Laurence Rouet, Stéphane Avril","doi":"10.1007/s10439-024-03608-8","DOIUrl":"10.1007/s10439-024-03608-8","url":null,"abstract":"<p><strong>Purpose: </strong>Ultrasound imaging is key in the management of patients with an abdominal aortic aneurysm (AAA). It was recently shown that the cyclic diameter variations between diastole and systole, which can be quantified with US imaging, increase significantly with the strength of the applied probe pressure on the patient's abdomen. The goal of this study is to investigate this effect more thoroughly.</p><p><strong>Methods: </strong>With finite-element modeling, pulsatile blood pressure and probe pressure are simulated in three patient-specific geometries. Two distinct models for the aortic wall were simulated: a nonlinear hyperelastic and a linear elastic model. In addition, varying stiffness was considered for the surrounding tissues. The effect of light, moderate, and firm probe pressure was quantified on the stresses and strains in the aortic wall, and on two in vivo stiffness measures. In addition, the Elasticity Loss Index was proposed to quantify the change in stiffness due to probe pressure.</p><p><strong>Results: </strong>Firm probe pressure decreased the measured aortic stiffness, and material stiffness was affected only when the wall was modeled as nonlinear, suggesting a shift in the stress-strain curve. In addition, stiffer surrounding tissues and a more elongated aneurysm sac decreased the responsiveness to the probe pressure.</p><p><strong>Conclusion: </strong>The effect of probe pressure on the AAA wall stiffness was clarified. In particular, the AAA wall nonlinear behavior was found to be of primary importance in determining the probe pressure response. Thus, further work will intend to make use of this novel finding in a clinical context.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"71-82"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142124596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controlling Biomedical Devices Using Pneumatic Logic.","authors":"Shane Hoang, Mabel Shehada, Konstantinos Karydis, Philip Brisk, William H Grover","doi":"10.1007/s10439-024-03628-4","DOIUrl":"10.1007/s10439-024-03628-4","url":null,"abstract":"<p><p>Many biomedical devices are powered and controlled by electrical components. These electronics add to the cost of a device (possibly making the device too expensive for use in resource-limited or point-of-care settings) and can also render the device unsuitable for use in some environments (for example, high-humidity areas such as incubators where condensation could cause electrical short circuits, ovens where electronic components may overheat, or explosive or flammable environments where electric sparks could cause serious accidents). In this work, we show that pneumatic logic can be used to power and control biomedical devices without the need for electricity or electric components. Originally developed for controlling microfluidic \"lab-on-a-chip\" devices, these circuits use microfluidic valves like transistors in air-powered logic \"circuits.\" We show that a modification to the basic valve design-adding additional air channels in parallel through the valve-creates a \"high-flow\" valve that is suitable for controlling a broad range of bioinstruments, not just microfluidics. As a proof-of-concept, we developed a high-flow pneumatic oscillator that uses five high-flow Boolean NOT gates arranged in a loop. Powered by a single constant vacuum source, the oscillator provides five out-of-phase pneumatic outputs that switch between vacuum and atmospheric pressure every 1.3 s. Additionally, a user can adjust the frequency of the oscillator by squeezing a bellows attached to one of the pneumatic outputs. We then used the pneumatic oscillator to power a low-cost 3D-printed laboratory rocker/shaker commonly used to keep blood products, cell cultures, and other heterogeneous samples in suspension. Our air-powered rocker costs around $12 USD to build and performs as well as conventional electronic rockers that cost $1000 USD or more. This is the first of many biomedical devices that can be made cheaper and safer using pneumatic logic.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"207-216"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Physical Therapy: Evaluating ChatGPT's Role in Clinical Decision Support for Musculoskeletal Care.","authors":"Jie Hao, Zixuan Yao, Yaogeng Tang, Andréas Remis, Kangchao Wu, Xin Yu","doi":"10.1007/s10439-025-03676-4","DOIUrl":"10.1007/s10439-025-03676-4","url":null,"abstract":"<p><strong>Background: </strong>The integration of artificial intelligence into medicine has attracted increasing attention in recent years. ChatGPT has emerged as a promising tool for delivering evidence-based recommendations in various clinical domains. However, the application of ChatGPT to physical therapy for musculoskeletal conditions has yet to be investigated.</p><p><strong>Methods: </strong>Thirty clinical questions related to spinal, lower extremity, and upper extremity conditions were quired to ChatGPT-4. Responses were assessed for accuracy against clinical practice guidelines by two reviewers. Intra- and inter-rater reliability were measured using Fleiss' kappa (k).</p><p><strong>Results: </strong>ChatGPT's responses were consistent with CPG recommendations for 80% of the questions. Performance was highest for upper extremity conditions (100%) and lowest for spinal conditions (60%), with a moderate performance for lower extremity conditions (87%). Intra-rater reliability was good (k = 0.698 and k = 0.631 for the two reviewers), and inter-rater reliability was very good (k = 0.847).</p><p><strong>Conclusion: </strong>ChatGPT demonstrates promise as a supplementary decision-making support tool for physical therapy, with good accuracy and reliability in aligning with clinical practice guideline recommendations. Further research is needed to evaluate its performance across broader scenarios and refine its clinical applicability.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"9-13"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.","authors":"Guoxin Zhang, Tommy Tung-Ho Hong, Li Li, Ming Zhang","doi":"10.1007/s10439-024-03603-z","DOIUrl":"10.1007/s10439-024-03603-z","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.</p><p><strong>Methods: </strong>The smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60 min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation.</p><p><strong>Results: </strong>Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79 ms), and portability (32 g).</p><p><strong>Conclusion: </strong>The proposed smart device can detect fatigue patterns with high precision and in real time.</p><p><strong>Significance: </strong>The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.</p>","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"48-58"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}