The clinical implementation of currently used devices for intraoperative fixation monitoring of femoral implants via vibration-based methods in cementless total hip arthroplasty is challenging, due to practical and regulatory issues. Motivated by the effectiveness of electromagnetic excitation in similar dental applications, this study investigates the use of electromagnetic excitation for femoral implant stability monitoring during cementless total hip arthroplasty. The results obtained from electromagnetic excitation were largely consistent with reference results obtained through impact excitation, with a Pearson Correlation Coefficient of 0.79 in the 0.1-8 kHz frequency band. Moreover, the peak frequencies obtained via the two methods yielded a relative difference of 0.20 ± 0.22 %. Next, the excitation device was successfully utilized in conjunction with a laser vibrometer to monitor the stability of the femoral implant during an in vitro insertion, proving the feasibility of contactless implant stability monitoring. These results indicate the promising potential of this contactless method for clinical implementation.

Nucleus segmentation and counting play a crucial role in many cell analysis applications. However, the dense distribution and blurry boundaries of nucleus make nucleus segmentation tasks challenging. This paper proposes a novel segmentation and counting method. Firstly, TC-UNet++ is proposed to achieve a global segmentation. Then, the distance watershed method is used to finish local segmentation, which separate the adhesion and overlap part of the image. Finally, counting method is performed to obtain information on the counting number, area and center of mass of nucleus. TC-UNet++ achieved a Dice coefficient of 89.95% for cell instance segmentation on the Data Science Bowl dataset, surpassing the original U-Net++ by 0.23%. It also showed a 5.09% improvement in counting results compared to other methods. On the ALL-IDB dataset, TC-UNet++ reached a Dice coefficient of 83.97%, a 7.93% increase over the original U-Net++. Additionally, its counting results improved by 16.82% compared to other approaches. These results indicate that our method has a more complete and reasonable nucleus segmentation and counting scheme compared to other methods.

Cancellous bone plays a critical role as a shock absorber in the human skeletal system. Accurate assessment of its microstructure and mechanical properties is crucial for osteoporosis diagnosis and treatment. However, various methods with different indicators are adopted currently in the clinical and laboratory assessments which lead to confusion and inconvenience for cancellous bone analysis. In the current work, correlations among clinical indicators including CT-derived Hounsfield Unit (HU) & bone mineral density (BMD), laboratory indicators (mass density & volume fraction), and mechanical properties (modulus & strength) are explored. The results show that different indicators can be linearly linked through the HU value which can be adopted as a good microstructure indicator of cancellous bone. Additionally, the impacts of cancellous bone specimen preparation on clinical CT imaging and mechanical properties are also investigated. The results indicate common marrow-removal treatment can lead to decrease in mean HU value, deviation in HU value distribution, while it will increase the modulus and strength. The current work provides a valuable insight into the cancellous properties based on comprehensive analysis on the clinical and laboratory assessments which is critical for accurate diagnosis and personalized treatment.

Radiofrequency (RF) hyperthermia has been widely used for tumor ablation since magnetic-fluid-hyperthermia (MFH) can be utilized for increasing temperature in tumor-region as a complementary-method for hyperthermia. In this study, the effectiveness of using the magnetite-nanoparticles (FeO) in RF hyperthermia for breast cancer (BC) treatment by determining 3D-temperature-distribution using bioheat-transfer-mapping was evaluated. A breast-phantom with a tumor region was placed in an RF-device with 13.56 MHz frequency in different states (with and without-nanomagnetite). Parallelly, the calculations of the RF-wave and bioheat-equation were accomplished by numerical-simulation and finite-element-method (FEM) in COMSOL-software. The temperature differences were experimentally measured at different points of the phantom with a precision of 0.1 °C, with temperature of 3.6 °C and 6.1 °C in without and with nanomagnetic conditions in tumor area, respectively, and also for normal area with temperature of 1.8 °C and 1.9 °C in non-presence and presence states of 0.05 gr magnetite for both conditions, respectively. Moreover, the difference between the simulation and the experimental results was 0.54-1.1 %. The conformity between temperature measurement in experimental and simulation studies in tumor and normal areas showed the effectiveness of the application of MNPs for RF hyperthermia in tissue equivalent breast phantom. Finally, the positive effect of 0.05 gr of MNPs on BC treatment was confirmed.

The mechanical properties of ligaments are important for multiple applications and are often estimated from laxity tests. However, the typical laxity tests are not optimized for this application and, a potential exists to develop better laxity tests in this respect. Therefore, the purpose of this study was to develop a methodology to identify optimal, dynamic laxity tests that isolate the stretch of the individual ligaments from each other. To this end, we applied an existing rigid body-based knee model and a dataset of ∼100.000 random samples of applied forces (0-150 N), moments (0-10 Nm) and knee flexion angles (0-90°) through Monte Carlo Simulations. For each modelled ligament bundle, we identified ten load cases; one producing the highest force and nine equally spaced between the maximal and zero force, where the maximal force in all other ligament bundles were minimized. We compared these novel laxity tests to standard internal/external and varus/valgus laxity tests using an isolation metric. We found that no laxity test could stretch the anterior part of the posterior cruciate and medial cruciate ligaments (PCL and MCL), whereas for all other ligaments, except the posterior PCL, the new laxity tests isolated the ligament stretch 28 % to 450 % better than standard tests. From our study, we conclude that it is possible to define better laxity tests than currently exist and these may be highly relevant for determination of mechanical properties of ligaments in vivo. Future studies should generalize our results and translate them to modern laxity measurements technologies.

To reveal the penetration mechanism and present the penetration characteristics of high-speed micro-jet with injection volume larger than 0.3 mL into soft tissue, the present study conducted experimental research on high-speed water-jet penetration into ballistic gelatin. The free jet dynamics of an air-powered needle-free injector that can emit up to 1.27 mL of liquid at once and the penetration dynamics were visualized to reveal the details of the penetration process. In the early unstable stage, the jet is emitted in the form of pulses, and the first jet pulse can rapidly generate an initial slender channel in gelatin in a very short time. In the subsequent stable stage, energy input produces dispersion and further increases the penetration depth slowly. Changing the driving pressure by the power source mainly changes the penetration depth increment by dispersion; while changing the nozzle diameter mainly affects the penetration depth in the initial stage. The central position of the dispersion area in the injection direction was firstly defined in the present work and it was found that an approximate linear relationship between this position and the maximum penetration depth exits for different nozzle diameters and driving pressures when injecting the same liquid dose. These research results can provide a basis for a thorough understanding of the penetration characteristics of high-speed micro-jet with injection volume larger than 0.3 mL into soft tissue, as well as the design and operation of the air-powered needle-free injector.

Addressing the complexities of manual wheelchair (MWC) vibrations is crucial for the well-being of users and their integration into society. This study investigates the experimental choices influencing the assessment of vibration exposure, aiming to contribute for enhanced MWC developments and standardized design principles. By conducting a comprehensive full factorial experiment, the impact of various factors, including four MWC loads, two speeds, five floor types, and two MWC models was examined. Notably, findings highlight the predominant influence of floor type on vibration exposure, followed by speed and, to a lesser extent, MWC properties. Furthermore, the study suggests that enlisting an able-bodied participant is more representative than using a dummy when loading the MWC, providing valuable insights into the genuine MWC/user dyad response to vibrations. This research sets the stage for a more informed and standardized approach to address the vibration exposure faced by MWC users.

Wearable sensors, including accelerometers, are a widely accepted tool to assess gait in clinical and free-living environments. Methods to identify phases and subphases of the gait cycle are necessary for comprehensive assessment of pathological gait. The current study evaluated the accuracy of a finite state machine (FSM) algorithm to detect strides by identifying gait cycle subphases from ankle-worn accelerometry. Algorithm performance was challenged across a range of speeds (0.4-2.6 m/s), task conditions (e.g., single- and dual-task walking), and individual characteristics. Specifically, the study included a range of treadmill speeds in young adults and overground walking conditions in older adults with neurological disease. Manually counted and algorithm-derived stride detection from acceleration data were evaluated using error analysis and Bland-Altman plots for visualization. Overall, the algorithm successfully detected strides (>96 % accuracy) across gait speed ranges and tasks, for young and older adults. The accuracy of an FSM algorithm combined with ankle-worn accelerometers, provides an analytical approach with affordable and portable tools that permits comprehensive assessment of gait unbounded by setting and proves to perform well in in walking tasks characterized by variable walking. These algorithm capabilities and advancements are critical for identifying phase dependent gait impairments in clinical and free-living assessment.

Porous interbody cages are often used in spinal fusion surgery since they allow bone ingrowth which facilitates long-term stability. However, the extent of bone ingrowth in and around porous interbody cages has scarcely been investigated. Moreover, tissue differentiation might not be similar around the superior and inferior cage-bone interfaces. Using mechanobiology-based numerical framework and physiologic loading conditions, the study investigates the spatial distribution of evolutionary bone ingrowth within randomly distributed porous interbody cages, having varied porosities. Finite Element (FE) microscale models, corresponding to cage porosities of 60 %, 72 %, and 83 %, were developed for the superior and inferior interfacial regions of the cage, along with the macroscale model of the implanted lumbar spine. The implant-bone relative displacements of different porosity models were mapped from macroscale to microscale model. Bone formation of 10-40 % was predicted across the porous cage models, resulting in an average Young's modulus ranging between 765 MPa and 915 MPa. Maximum bone ingrowth of ∼34 % was observed for the 83 % porous cage, which was subject to low implant-bone relative displacements (maximum 50μm). New bone formation was found to be greater at the superior interface (∼34 %) as compared to the inferior interface (∼30 %) for P83 model. Relatively greater volume of fibrous tissue was formed at the implant-bone interface for the cage with 60 % and 72 % porosities, which might lead to cage migration and eventual failure of the implant. Hence, the interbody cage with 83 % porosity appears to be most favorable for bone ingrowth, provided sufficient mechanical strength is offered.

Challenges in minimally invasive surgeries, such as intramedullary nailing for long bone fractures, include radiation overexposure for patients and surgeons, potential malreduction, and physical burden on surgeons in maintaining the reduction status. A robotic bone fracture reduction system was developed in this study to address these problems. The system consists of a hexapod with six degrees of freedom, with a fracture reduction device and a master device. This study aimed to evaluate the novel system in a preclinical setting. The length of the six axes in the system can be adjusted to precisely control the length, angle, and rotation so that no additional traction is required. Fluoroscopic images can be remotely examined to reduce the risk of radiation exposure for surgeons. In this study, alignment accuracy and radiation exposure were measured using 32 bovine bone fracture models, and these surgical outcomes were compared to those of conventional manual surgery to verify the clinical usability and effectiveness of the system. The alignment accuracy was assessed by analyzing length, angulation, and rotation. The four surgeons participating in this study were divided into two groups (expert and novice) according to their clinical experience. All parameters in robotic surgery significantly decreased by approximately 4 mm and 8° on average (p ≤ 0.05) compared to conventional surgery. The mean radiation exposure in robot-assisted surgery was 0.11 mSv, showing a significant decrease compared to conventional surgery (p < 0.05). Reduction accuracy was higher in robotic surgery performed by the novice group than in conventional surgery performed by the expert group; however, standard deviation values were inversed. In conclusion, the bone fracture reduction robot system increased the alignment accuracy through precise control while reducing radiation exposure in surgeons, as the surgery was performed remotely. The use of this system is predicted to improve the accuracy and reproducibility of the surgery and the safety of medical staff..

B-mode sonography is commonly used to monitor focused ultrasound ablation surgery (FUAS), but has limitations in sensitivity. More accurate and reliable prediction of coagulation is required.

Advances in cancer diagnostics play a pivotal role in increasing early detection of cancer. Integrating laser-induced breakdown spectroscopy (LIBS) with machine learning algorithms has attracted wide interest in cancer diagnosis. However, using a single model`s efficacy is limited by algorithm principles, making it challenging to meet the comprehensive needs of cancer diagnosis. Here, we demonstrate a bagging-voting fusion (BVF) algorithm for the detection and identification of multiple types of cancer. In the BVF model of this paper, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), and random forest (RF) models, which have relatively small homogeneity to obtain more comprehensive decision boundaries, are fused at both the training and decision levels. LIBS spectral data was collected from four types of serum samples, including liver cancer, lung cancer, esophageal cancer, and healthy control. LIBS detection was conducted on the samples, which were directly dropped onto ordered microarray silicon substrates and dried. The results showed that the BVF model achieved an accuracy of 92.53 % and a recall of 92.92 % across the four types of serum, outperforming the best single machine-learning model (SVM: accuracy 75.86 %, recall 77.50 %). Moreover, the BVF model with manual line selection feature extraction required only 140 s for a single detection and identification. In conclusion, the aforementioned results demonstrated that LIBS with BVF has excellent performance in detecting a multitude of cancers, and is expected to provide a new method for efficient and accurate cancer diagnosis.

This article introduces a fast fractal method for classifying breast cancerous lesions in mammography. While fractal methods are valuable for extracting information, they often come with a high computational load and time consumption. This paper demonstrates that extracting optimal fractal information and focusing only on valuable information for classification not only improves computation speed and reduces process load but also enhances classification accuracy. To achieve this, we define an objective function based on accurate classification of benign and malignant masses to identify the best scale. Instead of extracting information from all nine scales, we extract and employ information solely from the best scale for classification. We validate the obtained scales using three classifiers: Support Vector Machine (SVM), Genetic Algorithm (GA), and Deep Learning (DL), which confirm the effectiveness of the proposed method. Comparative analysis with other studies reveals improved classification performance with the presented method.

Emerging research has demonstrated the advantage of continuous glucose monitoring for use in artificial pancreas and diabetes management in general. Recent studies demonstrate that glucose level forecasting using deep learning can help avoid postprandial hyperglycemia (≥ 180 mg/dL) or hypoglycemia (≤70 mg/dL) from delayed or increased insulin dosing in artificial pancreas. In this paper, a novel hybrid deep learning framework with integration of content-based attention learning is presented, to effectively predict the glucose measurements with prediction horizons (PH) = 15, 30 and, 60 minutes for T1D and T2D patients based on past data. We also present a complete cloud-based system and mobile app used for collecting CGM sensor, physical activity data, CHO values and insulin measurements to perform glucose forecasts using the proposed model running on Cloud. This model was validated using clinical data of individual with Type 1 diabetes (OhioT1DM) and individual with Type 2 diabetes. The mean absolute relative difference (MARD) was 12.33±3.15, 7.14±1.76% for PH=60 and, 30 min respectively on OhioT1DM clinical Dataset. The root mean squared error (RMSE) was 29.41±5.92 mg/dL and 17.19±3.22 mg/dL and the mean absolute error (MAE) was 21.96±4.67 mg/dL and 12.58±2.34 mg/dL for PH=60 and, 30 min respectively on the same clinical dataset. It was observed that inclusion of physical activity leads to improved glucose forecasting accuracy. Furthermore, all these results were obtained by training the model on only 8 days of clinical data of a single patient, followed by testing on clinical data on the following days. The results indicate that training on a single patient data may lead to better personalisation and better glucose forecasting results compared to existing works.

In this paper, the mechanical behaviour and failure of porous additively manufactured (AM) polylactide (PLA) scaffolds based on the triply periodic minimal surfaces (TPMS) is investigated using numerical calculations of their unit cells and representative volumes. The strain-amplification factor is chosen as the main parameter, and the most critical locations for failure of different types of scaffold structures are evaluated. The results obtained are presented in comparison with a multiple-crack-growth algorithm using the extended finite element method (XFEM), underpinned by the experimentally obtained fracture properties of PLA. The effect of morphology of TPMS structures on the pre-critical, critical and post-critical behaviours of scaffolds under monotonic loading regimes is assessed. The results provide an understanding of the fracture behaviour and main risk points for crack initiation in structures of AM-PLA scaffolds based on typical commonly used types of TPMS, as well as the influence of structure type and external load on this behaviour.

This study introduces a new method for modeling electrocardiogram (ECG) waveforms using Fractional Differential Equations (FDEs). By incorporating fractional calculus into the well-established McSharry model, the proposed approach achieves improved representation and high precision for a wide range of ECG waveforms. The research focuses on the impact of integrating fractional derivatives into Integer Differential Equation (IDE) models, enhancing the fidelity of ECG signal modeling. To optimize the model's unknown parameters, a combination of the Predictor-Corrector method for solving FDEs and genetic algorithms for optimization is utilized. The effectiveness of the fractional-order model is assessed through distortion metrics, providing a comprehensive evaluation of the modeling quality. Comparisons show that the fractional-order model outperforms the traditional McSharry IDE model in modeling quality and compression efficiency. It improves modeling quality by 48.40 % in MSE and compression efficiency by 23.18 % when applied on five beat types of MIT/BIH arrhythmia database. The fractional-order model demonstrates enhanced flexibility while preserving essential McSharry model characteristics, with fractional orders (α) ranging from 0.96 to 0.99 across five beat types.

Individuals with incomplete spinal cord injury (iSCI) demonstrate impaired upright balance, resulting in increased fall risk. Task-specific visual feedback balance training (VFBT) has previously been shown to improve upright balance. In addition, therapies using functional electrical stimulation (FES) have been shown to improve various motor functions. Combining VFBT with FES therapy (FES+VFBT) may synergistically improve balance control for those with iSCI. Here we developed the FES+VFBT system that delivered physiologically relevant electrical stimulations to soleus (SOL) and tibialis anterior (TA) muscles during VFBT. Ten young able-bodied individuals participated. Kinematic, kinetic, SOL and TA electromyography (EMG) data during quiet standing and limits-of-stability test were used to design the controller for the FES+VFBT system. To evaluate the performance of the designed controller, the controller outputs, which represented stimulation intensities, were compared with the recorded SOL and TA EMG during the four tasks associated with VFBT (i.e., bullseye, hunting, colour-matching, and ellipse tasks). Except for the bullseye task, the designed controller outputs were highly correlated with the recorded EMG, suggesting that the controller could generate electrical stimulations in a physiological manner. We expect that the addition of FES therapy to VFBT could contribute to improving standing balance for individuals with iSCI.

Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment. Machine learning (ML)-based algorithms that use features extracted from electrocardiogram (ECG) signals are known to provide good accuracy in predicting various CVDs. Thus, in order to build more effective and efficient machine learning models, it is necessary to extract significant features from ECGs. In order to reduce overfitting and training overhead and improve model performance even more, feature selection or dimensionality reduction is essential. In this regard, the current work uses the grey wolf optimization (GWO) technique to pick a reduced feature set after extracting pertinent characteristics from ECG signals in order to identify five different types of CVDs. On the basis of the feature relevance of the chosen features, a feature-specific extreme gradient boosting approach (FS-XGB) is also suggested. The suggested FS-XGB classifier's performance is contrasted with that of other machine learning techniques, including gradient boosting method, AdaBoost, naïve Bayes, and support vector machine (SVM). The proposed methodology achieves a maximum classification accuracy, precision, recall, F1-score, and AUC value of 98.8 %, 100 %, 99.8 %, 100 %, and 98.8 %, respectively, with just seven optimal features, significantly fewer than the number of features used in existing works.

In response to the difficulties in accurately reproducing the resistance drop generated by puncturing key tissue layers with a needle and the poor experience in existing simulators, based on the continuous controllability and rapid response of magnetorheological fluid under the influence of a magnetic field, this paper proposes a lumbar puncture training simulator(LPTS) that can accurately simulate the puncture feedback force within tissues such as the skin, subcutaneous fat, and supraspinous ligament throughout the entire process. By using a dual rod structure and reasonably arranging the damping channel gap, the influence of mechanical friction and zero-field damping force on the feedback force during tissue progression is minimized. This paper introduces the acquisition and modeling analysis of raw data, and based on this, the design, simulation, and mechanical testing of the simulator are carried out. Finally, a performance testing platform for the simulator is established to evaluate its tracking performance of the expected puncture strength and the reproducibility of the puncture sensation. The results show that the experimental puncture strength deviates from the expected puncture strength by 0.35 N to 0.61 N in the crucial steps of breaking through the supraspinous ligament, interspinous ligament, ligamentum flavum, and dura mater, with a relative error below 10 %.

Robotic minimally invasive surgery (MIS) has changed numerous surgical techniques in the past few years and enhanced their results. Haptic feedback is integrated into robotic surgical systems to restore the surgeon's perception of forces in response to interaction with objects in the surgical environment. The ideal exact emulation of the robot's interaction with its physical environment in free space is a very challenging problem to solve completely. Previously, we introduced the surgical robotic platform (SRP) with a novel concentric connector joint (CCJ). This study aims to develop a haptic control system that integrates an active constraint controller into a surgical robot platform. We have successfully established haptic feedback control for the surgical robot using constraint control and inverse kinematic relationships integrated into the overall positioning structure. A preliminary feasibility study, modelling, and simulation were presented.