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Bioengineering
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Advanced Engineering Technologies in Orthopaedic Research
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Advanced Engineering Technologies in Orthopaedic Research

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1077

Special Issue Editors

Dr. Guoan Li

E-Mail Website
Guest Editor
Orthopaedic Bioengineering Research Center, Newton-Wellesley Hospital/Massachusetts General Brigham, 159 Wells Avenue, Newton, MA 02459, USA
Interests: orthopaedic biomechanics; knee; spine; total joint replacement
Dr. Tsung-Yuan Tsai

E-Mail Website
Guest Editor
Orthopaedic Biomechanics Laboratory, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: orthopaedic biomechanics; digital image processing; human motion analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaobai Wang

E-Mail Website
Guest Editor
School of Exercise and Health, Shanghai University of Sport, Shanghai, China
Interests: biomechanics; orthopaedics; sports medicine; rehabilitation; robotics

Special Issue Information

Dear Colleagues,

We are collaborating with Bioengineering to publish a Special Issue focusing on applications of advanced engineering technology in orthopedic biomechanical research. The development of advanced bioengineering technology has dramatically enhanced orthopedic biomechanical research and greatly influenced treatments for orthopedic patients. For example, robotic testing systems have been widely used in the biomechanical testing of cadaveric joints. This has revolutionized the traditional testing methodology by introducing 6DOF, hybrid displacement and force controls, and the ability to test a joint under simulated physiological loading conditions. Advanced computational analysis and modeling methods include technologies such as digital motion analysis techniques, musculoskeletal system modeling, and finite element analysis (FEA). While 3D motion capture and force plate technology can be applied to investigating dynamic joint motion and mechanics, advanced musculoskeletal system modeling methods can predict muscle forces, joint reaction forces, and tissue dynamics under simulated physiological conditions. The predicted loading conditions can be used as inputs for FEA models to simulate physiological stress, strain, and deformation in bones, joints, and implants to predict injuries and failures and to optimize implant designs. This type of research is crucial in designing prosthetics and rehabilitation plans. Recently, advanced imaging techniques, including high-resolution CT, MRI, and dynamic dual X-ray imaging systems, have enabled the precise visualization of in vivo articular joint contact motion, allowing the investigation of the precise mechanisms of musculoskeletal joint motion, injury, and repair. These innovations in orthopedic biomechanics research are not only revolutionizing basic science research but also helping to improve patient outcomes through tailored and advanced orthopedic device designs and treatment strategies. Recently, artificial intelligence and machine learning methods have also been intensively applied in orthopedic biomechanical research. AI-driven models show great potential in enabling the discovery of tissue abnormalities such as joint osteoarthritis and spinal disk degeneration, predicting musculoskeletal injuries such as ACL tears, bone fractures, implant failures, and surgical outcomes, and tailoring treatment plans based on genetic, biomechanical, and lifestyle factors.

We are pleased to invite you to submit a paper to this Special Issue with a focus on advanced engineering technology in orthopedic research. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the development of advanced technologies for orthopedic biomechanics research, such as advanced testing systems, computational simulation methods, advanced imaging techniques, robotic technologies, or AI and ML methods, and the application of advanced engineering technologies in basic science and clinical research in musculoskeletal joint biomechanics, such as intrinsic joint biomechanics, joint injury mechanisms, and the efficacy of treatment methods for joint diseases.

Dr. Guoan Li
Dr. Tsung-Yuan Tsai
Prof. Dr. Shaobai Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • orthopaedic biomechanics
  • kinematics
  • articular joints
  • knee
  • hip
  • ankle
  • shoulder
  • spine
  • MRI
  • CT
  • robotic testing system
  • bi-plane imaging
  • FEA
  • computation modeling

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Published Papers (4 papers)

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Research

12 pages, 33851 KiB  
Article
Development and Validation of a Deep Learning System for the Detection of Nondisplaced Femoral Neck Fractures
by Lianxin Wang, Ce Zhang, Yaozong Wang, Xin Yue, Yunbang Liang and Naikun Sun
Bioengineering 2025, 12(5), 466; https://doi.org/10.3390/bioengineering12050466 - 28 Apr 2025
Viewed by 34
Abstract
Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. This study addresses the challenge of diagnosing nondisplaced femoral neck fractures, which are [...] Read more.
Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. This study addresses the challenge of diagnosing nondisplaced femoral neck fractures, which are often difficult to detect with standard radiographs, especially in elderly patients. This research evaluates a deep learning model that employs a convolutional neural network (CNN) within a ResNet framework, designed to enhance diagnostic accuracy for nondisplaced femoral neck fractures. The model was trained and validated on a dataset of 2032 hip radiographs from two hospitals, with additional external validation performed on datasets from other institutions. The AI model achieved an accuracy of 94.8% and an Area Under Curve of 0.991 on anteroposterior pelvic/hip radiographs, outperforming emergency physicians and delivering results comparable to expert physicians. External validation confirmed the model’s robust accuracy and generalizability across diverse datasets. This study underscores the potential of deep learning models to act as a supplementary tool in clinical settings, potentially reducing diagnostic errors and improving patient outcomes by facilitating a quicker diagnosis and treatment. Full article
(This article belongs to the Special Issue Advanced Engineering Technologies in Orthopaedic Research)
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12 pages, 3415 KiB  
Article
Causal Relationship Between Blood Metabolites and Osteoporosis: A Two-Sample Mendelian Randomization and Genetic Correlation Analysis
by Xu Liu, Guang Yang, Yusheng Li, Wenfeng Xiao, Bangbao Lu and Yaping Wang
Bioengineering 2025, 12(5), 435; https://doi.org/10.3390/bioengineering12050435 - 22 Apr 2025
Viewed by 168
Abstract
Background: Osteoporosis (OP) is a systemic bone disease often undiagnosed until fractures occur. Metabolites may influence OP, offering potential biomarkers or therapeutic targets. This study investigates the causal relationship between circulating metabolites and OP-related phenotypes using Mendelian Randomization (MR). Methods: GWAS data on [...] Read more.
Background: Osteoporosis (OP) is a systemic bone disease often undiagnosed until fractures occur. Metabolites may influence OP, offering potential biomarkers or therapeutic targets. This study investigates the causal relationship between circulating metabolites and OP-related phenotypes using Mendelian Randomization (MR). Methods: GWAS data on 233 metabolic traits from 136,016 participants were analyzed through two-sample MR. Linkage disequilibrium score regression (LDCS) was used to estimate genetic correlations between metabolic traits and OP-related phenotypes, leveraging European ancestry linkage disequilibrium scores to account for polygenicity and stratification. MR employed the inverse-variance weighted (IVW) method, with sensitivity analyses via MR-Egger, MR-PRESSO, and weighted median methods to address pleiotropy and confounders. Results: LDCS identified significant genetic correlations between metabolites and bone mineral density (BMD) phenotypes, with total body BMD (toBMD) showing the strongest associations. Thirty-five metabolite traits, including apolipoprotein A-I, exhibited significant linkages. Among 79 metabolites influencing BMD, serum acetate levels were significantly associated with femoral neck BMD (OR: 1.28, 95% CI: 1.02–1.62), lumbar spine BMD (OR: 1.73, 95% CI: 1.32–2.27), and total body BMD (OR: 1.21, 95% CI: 1.04–1.42). Creatinine levels were consistently linked to reduced BMD, including lumbar spine BMD (OR: 0.88, 95% CI: 0.79–0.99). Triglycerides in IDL and VLDL particles also contributed to BMD variation. Conclusions: Significant genetic correlations and causal relationships were observed between specific metabolites and OP, highlighting key traits as potential biomarkers of bone health. These findings enhance the understanding of OP pathogenesis and suggest future preventive strategies. Full article
(This article belongs to the Special Issue Advanced Engineering Technologies in Orthopaedic Research)
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17 pages, 4482 KiB  
Article
The Effect of Dental Implant Restoration on the Biomechanics of the Temporomandibular Joint in Patients with Posterior Tooth Loss: A Pilot Study
by Yuanli Zhang, Chongzhi Yin, Fei Chen, Guizhi Zhang, Po Hao, Yongli Pu, Haidong Teng, Hong Huang and Zhan Liu
Bioengineering 2025, 12(4), 419; https://doi.org/10.3390/bioengineering12040419 - 14 Apr 2025
Viewed by 203
Abstract
Currently, controversy persists over whether dental implant restoration exacerbates or alleviates temporomandibular disorders (TMDs). This study aimed to analyze the impact of dental implant restoration on the biomechanics of the temporomandibular joint (TMJ) in patients with posterior tooth loss. Ten healthy volunteers (Control [...] Read more.
Currently, controversy persists over whether dental implant restoration exacerbates or alleviates temporomandibular disorders (TMDs). This study aimed to analyze the impact of dental implant restoration on the biomechanics of the temporomandibular joint (TMJ) in patients with posterior tooth loss. Ten healthy volunteers (Control group) and twenty patients with posterior tooth loss (preoperative in the Pre group and postoperative in the Post group) were recruited. Three-dimensional maxillofacial models of the maxilla, mandible, dentition, and articular discs were reconstructed. The von Mises, contact, and tensile stresses of the TMJ were analyzed. Before implant restoration, the stresses of the TMJ in the Pre group were considerably higher than those in the Control group, especially on the missing tooth side. After restoration, the stresses in the Post group decreased significantly, with a near-symmetrical distribution. Additionally, before restoration, the patients with TMD had the highest stresses of the TMJ, followed by those without TMD, and the Control group had the lowest. After restoration, the stress magnitudes in the patients with or without TMD returned to the normal range. In summary, dental implant restoration can significantly improve the asymmetric stress distribution of the TMJs, substantially reduce excessive stress caused by tooth loss, and alleviate or eliminate the symptoms related to TMDs. Full article
(This article belongs to the Special Issue Advanced Engineering Technologies in Orthopaedic Research)
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13 pages, 2727 KiB  
Article
Surgical Robots Improve Tunnel Angle and Graft Bending Angle in Anatomic ACL Reconstruction: A Multicenter Study
by Ling Zhang, Hansheng Hu, Wennuo Huang, Mengling Hu, Zhuman Li, Jinzhong Zhao, Wenyong Fei and Shaobai Wang
Bioengineering 2025, 12(4), 338; https://doi.org/10.3390/bioengineering12040338 - 24 Mar 2025
Viewed by 368
Abstract
The anatomic characteristics of the graft and tunnel, i.e., the tunnel position, angle, length, and the graft bending angle, influence knee joint stability and postoperative functional recovery. The purpose of this study was to evaluate the tunnel position, length and angle, as well [...] Read more.
The anatomic characteristics of the graft and tunnel, i.e., the tunnel position, angle, length, and the graft bending angle, influence knee joint stability and postoperative functional recovery. The purpose of this study was to evaluate the tunnel position, length and angle, as well as graft bending angle after ACL reconstruction assisted by a surgical robot. A total of 70 patients were randomized into two groups: the surgical robot group (robot group, n = 35) and the traditional handheld locator group (control group, n = 35). Postoperative computed tomography (CT) was employed to assess the positions and lengths of the tunnels, as well as the tunnel angle and the graft bending angle. Additionally, the posterior wall distance was measured by determining the minimum vertical distance from the long axis of the tunnel to the posterior wall region. There were no significant differences between the two groups in the mean position or length of the femoral and tibial tunnel (p > 0.05). However, the femoral tunnel angle was significantly larger in the robot group compared to the handheld locator group (p = 0.012). The graft bending angle was significantly less acute in the robot group than in the control group (p = 0.008). Additionally, the posterior wall distance was significantly greater in the robot group compared to the control group (p < 0.001). The results suggest that surgical robot-assisted ACL reconstruction enhances safety in the inclination of the tunnel and graft, helping to avoid potential biomechanical issues such as the wiper effect and the bungee effect, which may lead to tunnel widening and surgical failure. Full article
(This article belongs to the Special Issue Advanced Engineering Technologies in Orthopaedic Research)
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