Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine最新文献

The mechanical response of biological tissue is an important basis for evaluating its state during the surgical operation. Accurate prediction of mechanical response is helpful to improve the precision of surgical operation. In this paper, An advanced prediction method based on hybrid prediction model is proposed and used to predict the mechanical response of soft tissue. Firstly, the simulation model of soft tissue indentation experiment was established to obtain the mechanical response under continuous loading condition. The mechanical response of kindy tissue under discontinuous loading was obtained by the actual indentation experiment. Secondly, the mechanical response is predicted and the influence of loading parameters on the prediction accuracy is analyzed. The mechanical response under continuous loading was obtained by simulation, and the mechanical response under non-continuous loading was obtained by indentation experiment. The proposed advanced prediction method is verified by the obtained mechanical responses. The results show that the proposed method can predict the mechanical response of soft tissue well. The proposed prediction algorithm is helpful to predict the mechanical response in advance and avoid the potential tissue damage caused by surgical operation.

The aim of this study is to develop Deep Learning (DL) enabled robotic systems to identify audio-based emotional pragmatics deficits in individuals with social pragmatic communication deficits. The novelty of the work stems from its integration of deep learning with a robotics platform for identifying emotional pragmatics deficits. In this study, the proposed methodology utilizes the implementation of machine and DL-based classification techniques, which have been applied to a collection of open-source datasets to identify audio emotions. The application of pre-processing and converting audio signals of different emotions utilizing Mel-Frequency Cepstral Coefficients (MFCC) resulted in improved emotion classification. The data generated using MFCC were used for the training of machine or DL models. The trained models were then tested on a randomly selected dataset. DL has been proven to be more effective in the identification of emotions using robotic structure. As the data generated by MFCC is of a single dimension, therefore, one-dimensional DL algorithms, such as 1D-Convolution Neural Network, Long Short-Term Memory, and Bidirectional-Long Short-Term Memory, were utilized. In comparison to other algorithms, bidirectional Long Short-Term Memory model has resulted in higher accuracy (96.24%), loss (0.2524 in value), precision (92.87%), and recall (92.87%) in comparison to other machine and DL algorithms. Further, the proposed model was deployed on the robotic structure for real-time detection for improvement of social-emotional pragmatic responses in individuals with deficits. The approach can serve as a potential tool for the individuals with pragmatic communication deficits.

Additive manufacturing (AM) is revolutionizing healthcare by enabling the creation of customized 3D printed (3DP) medical equipment, implants, orthoses, prosthetics, drugs, and organs. With the availability of different types of materials suitable for 3DP and healthcare applications, this technology allows for the precise fabrication of patient-oriented prosthetics, dental implants, and orthopedic devices, significantly improving fit and functionality. Additionally, 3DP drugs, such as Oral Dispersible Formulations (ODFs) and polypills, are surpassing the traditional "one pill fits all" concept, offering more tailored medication solutions. This innovation also supports the development of personalized medications and bioprinted tissues, opening the way for advancements in regenerative medications and tailored therapies. 3D-bioprinted organs are addressing the growing demand for organ transplants. In surgical planning, 3D-printed anatomical models provide students and professionals with hands-on practice, which is crucial for skill development and understanding complex anatomies. Surgeons can also practice and refine techniques before actual procedures, enhancing precision and improving outcomes during real operations. This paper focus on highlighting the progression and motivations behind the cross-disciplinary applications of AM within the healthcare sector providing customized medical devices, drug delivery systems and diagnostic tools for personalized treatment and skill refinement. This paper is designed for a broad audience, including manufacturing professionals and researchers, who are interested in exploring the medical implications of this transformative technology.

Pelvic organ prolapse (POP) limits people's quality of life and is quite common in women, especially with advancing age. As cases increase, there is an urgent need for alternative treatments, especially for severe POP, where options remain limited. Surgical procedures involving synthetic meshes are associated with several complications, leading to the FDA (Food and Drug Administration) banning the commercialisation of these meshes to treat cases of transvaginal prolapse. Biodegradable meshes offer a potential solution to address the drawbacks associated with synthetic meshes, offering improved biocompatibility and biomechanical properties.This study developed computational models with variations in pore geometry, pore size, filament thickness and the inclusion of filaments in specific regions of the mesh. One of the meshes was then 3D printed to validate the simulation results. Subsequently, a uniaxial tensile test was performed on sow vaginal tissue to compare its mechanical behaviour with the simulations and identify meshes that closely mimic vaginal tissue behaviour. The mesh that most closely replicates vaginal tissue behaviour featured a smaller pore diameter (1.50 mm), filaments placed in specific areas, and variable filament thickness across the mesh. However, when compared to the uterosacral ligament, the meshes did not exhibit similar mechanical properties. Additionally, the commercially available mesh failed to replicate the behaviour of both vaginal tissue and the uterosacral ligament, suggesting it may not be the optimal option for POP repair. Biodegradable meshes, with their customisable properties, show great promise as future solutions for personalised and safer surgical treatment of POP.

A tremor is a neurological disorder that results in trembling or shaking in one or more body parts. A thorough literature review was conducted to investigate the methods for suppressing tremors. We looked for articles published between 1995 and 2024 in the databases CINAHL (Cumulative Index to Nursing and Allied Health Literature), PubMed, Medline, Embase, Scopus, and Cochrane. Two thousand two hundred fifty distinct items were discovered after an extensive search. Based only on the title, 250 were included. Two hundred papers were deemed ineligible after the abstracts were assessed. The remaining 26 articles were shortlisted after screening titles and abstracts and categorized based on treatment methods for hand tremors. According to the study's findings, deep brain stimulation (DBS) and electrical stimulation both reduced tremors considerably. It was also evident that attenuation systems and passive devices lessen the effects of tremors; target tracking tasks can lessen physiological tremors in postural posture; ET may have better hand functions after cold water treatment than warm water or at baseline; and targeted ultrasound thalamotomy is an effective treatment for ET, as it improved quality of life (QoL) significantly. Additionally, the design, development, and evaluation of wearable devices and pharmaceutical interventions for tremor suppression were investigated in detail. The main objective was to perform a comparative analysis of the merits and demerits of both treatment methodologies in terms of functional outcomes, users' comfort, and side effects. The review highlights wearable devices as a beneficial option for tremor suppression, offering comfort, safety, and advanced technology over pharmaceutical intervention methodologies.

Most preoperative planning calculations of impingement-free range of motion (IFROM) and impingement-free safe zone (IFSZ) rarely consider non-standard shaped prostheses and bony impingement (BI) for total hip arthroplasty (THA). This research developed a novel algorithm that considers BI, prosthetic impingement, pelvic tilt angle (PT) in the sagittal plane, and non-standard-shaped hip prostheses. This research aimed to investigate the effect of BI and PT on hip IFROM, IFSZ, and the BI rate. Using this algorithm to calculate a case, we found that when considering BI, (1) the upper limit of the hip IFROM was decreased, and the different PT affected the upper limit of the hip IFROM of various movements; (2) the BI rate of the flat-rim liner in standing and sitting postures were 54.6% and 67%; and (3) the maximum IFSZ size of the flat-rim liner was reduced by 12%, the reduction rate of the combined pelvic position with a non-zero IFSZ size was 83.2% for the flat-rim liner. Consideration of BI further reduces the IFROM, the IFSZ size, and the number of the combined position of the pelvis with a non-zero IFSZ size of the hip joint. Importantly, this algorithm provides a reliable tool for personalized prosthesis positioning for THA. This algorithm has excellent applications in personalized surgical planning and surgical robotics.

Osteoporosis, a common bone disease in older adults, is associated with low bone mineral density (BMD) and an increased risk of fractures. While fracture risk is often assessed using T-scores derived from dual-energy X-ray absorptiometry (DXA) scans, these measures are not fully effective in identifying individuals at greatest risk. To address this, a Statistical Shape and Appearance Modeling (SSAM) tool was previously developed to analyze femur shape and BMD distribution and demonstrated superior fracture risk prediction compared to T-scores using hip DXA scans exported in JPG format. The present study aimed to evaluate whether changes in DXA-imaging protocol (hip protocol vs. High-Definition Instant Vertebral Assessment (IVA-HD)) may influence the image and the SSAM tool's fracture risk predictions. The effect of image file type (JPG vs. PNG) was also explored, as native formats such as Digital Imaging and Communications in Medicine (DICOM) cannot be readily exported or saved in large databases. DXA scans from 36 subjects and seven cadaveric femurs were analyzed across four imaging conditions (file types and imaging protocols). Structural Similarity Index Measures (SSIM) quantified image differences, and Bland-Altman plots assessed agreement in fracture risk predictions. Minimal differences were found in SSAM tool outputs across protocols and file types. Higher-resolution scans (IVA-HD) and lossless file types (PNG) did not improve the accuracy of risk predictions compared to the standard hip protocol in JPG format. These findings suggest that the SSAM tool is robust to variations in imaging conditions, supporting its use with standard DXA imaging protocols and file formats.

Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The objective of the current study is to introduce a data augmentation strategy that relies on a variational autoencoder to generate synthetic data of kinetic and kinematic variables. The kinematic and kinetic variables consist of hip and knee joint angles and moments, respectively, in both sagittal and frontal plane, and ground reaction forces. Statistical parametric mapping (SPM) did not detect significant differences between real and synthetic data for each of the biomechanical variables considered. To further evaluate the effectiveness of this approach, a long-short term model (LSTM) was trained both only on real data (R) and on the combination of real and synthetic data (R&S); the performance of each of these two trained models was then assessed on real test data unseen during training. The principal findings included achieving comparable results in terms of nRMSE when predicting knee joint moments in the frontal (R&S: 9.86% vs R: 10.72%) and sagittal plane (R&S: 9.21% vs R: 9.75%), and hip joint moments in the frontal (R&S: 16.93% vs R: 16.79%) and sagittal plane (R&S: 13.29% vs R: 14.60%). The main novelty of this study lies in introducing an effective data augmentation approach in motion analysis settings.

Trabecular bone (TB) texture regions selected on hand and knee X-ray images can be used to detect and predict osteoarthritis (OA). However, the analysis has been impeded by increasing data volume and diversification of data formats. To address this problem, a novel storage platform, called Bone Data Lake (BDL) is proposed for the collection and retention of large numbers of images, TB texture regions and parameters, regardless of their structure, size and source. BDL consists of three components, i.e.: a raw data storage, a processed data storage, and a data reference system. The performance of the BDL was evaluated using 20,000 knee and hand X-ray images of various formats (DICOM, PNG, JPEG, BMP, and compressed TIFF) and sizes (from 0.3 to 66.7 MB). The images were uploaded into BDL and automatically converted into a standardized 8-bit grayscale uncompressed TIFF format. TB regions of interest were then selected on the standardized images, and a data catalog containing metadata information about the regions was constructed. Next, TB texture parameters were calculated for the regions using Variance Orientation Transform (VOT) and Augmented VOT (AVOT) methods and stored in XLSX files. The files were uploaded into BDL, and then transformed into CSV files and cataloged. Results showed that the BDL efficiently transforms images and catalogs bone regions and texture parameters. BDL can serve as the foundation of a reliable, secure and collaborative system for OA detection and prediction based on radiographs and TB texture.

Pelvic floor disorders (PFD), including Pelvic Organ Prolapse (POP), can negatively impact a woman's daily activities and quality of life. POP is a growing concern, with an increasing number of cases each year and significant numbers of women going through surgery to alleviate it. Traditional interventions like the use of mesh implants have certain limitations such as repeated surgeries. An alternative surgical intervention technique using injectable biodegradable cog threads was suggested. The application of Finite element analysis (FEA) to this research allows us to personalize and select suitable POP correction techniques and study the effect of alternative reinforcement techniques. The 3D computational model of the vagina will be used to simulate defect repair using cog threads. To accurately model this, we conducted uniaxial tensile tests on both the polycaprolactone (PCL) cog threads and the sow's vaginal tissues, which mimic human tissue, providing vital data for precise finite element modeling. The study's findings suggest that cog threads may have the potential to provide benefits in the treatment of POP. This study provides a starting point for further research on cog threads as one possible treatment option for POP.