In virtual surgical training, it is crucial to achieve real-time, high-fidelity simulation of the tissue deformation. The anisotropic and nonlinear characteristics of the organ with multi-component make accurate real-time deformation simulation difficult. A localized tissue constraint progressive transfer learning method is proposed in this paper, where the base-compensated dual-output transfer learning strategy and the localized tissue constraint progressive learning architecture are developed. The proposed strategy enriches the multi-component biomechanical dataset to fully represent complex force-displacement with minimal high-quality data. Meanwhile, the proposed architecture adopts focused and progressive model to accurately describe tissues with varied biomechanical properties rather than singular homogeneous model. We made comparison with 4 state-of-the-art (SOTA) methods in simulating multi-component biomechanical deformations of organs with 100 pairs of testing data. Results show that the accuracy of our method is 50% higher than other methods in different validation matrix. And our method can stably simulate the deformations in 0.005 s per frame, which largely improves the computing efficiency.

Mechanotherapy has emerged as a promising treatment for tissue injury. However, existing robots for mechanotherapy are often designed on intuition, lack remote and wireless control, and have limited motion modes. Herein, through topology optimization and hybrid fabrication, wireless magneto-active soft robots are created that can achieve various modes of programmatic deformations under remote magnetic actuation and apply mechanical forces to tissues in a precise and predictable manner. These soft robots can quickly and wirelessly deform under magnetic actuation and are able to deliver compressing, stretching, shearing, and multimodal forces to the surrounding tissues. The design framework considers the hierarchical tissue-robot interaction and, therefore, can design customized soft robots for different types of tissues with varied mechanical properties. It is shown that these customized robots with different programmable motions can induce precise deformations of porcine muscle, liver, and heart tissues with excellent durability. The soft robots, the underlying design principles, and the fabrication approach provide a new avenue for developing next-generation mechanotherapy.

BACKGROUND: Residual limb discomfort and injury is a common experience for people living with lower limb amputation. Frequently, inadequate load distribution between the prosthetic device and the residual limb is the root cause of this issue. To advance our understanding of prosthetic interface fit, tools are needed to evaluate the mechanical interaction at the prosthetic interface, allowing interface designs to be evaluated and optimised.
OBJECTIVE: Present a methodology report designed to facilitate comprehension of the mechanical interaction between the prosthetic interface and the residual limb. As a pilot study, this methodology is used to compare a hands-on and hands-off interface for a single transtibial prosthesis user using secondary Magnetic Resonance Imaging (MRI) data.
METHODS: MRI data of the residual limb while wearing a prosthetic interface is segmented into a hard tissue and a skin surface model. These models are exported as stereolithography (STL) files. Two methods are used to analyse the interface designs. Firstly, CloudCompare software is used to compute the nearest vertex on the skin surface for every vertex on the compiled internal bony surface for both interface types. Secondly, CloudCompare software is used to compare registered skin surfaces of the residual limb while wearing the hands-on and hands-off interfaces.
RESULTS: The maximum and minimum nearest distances between the internal bony surface and skin surface were similar between interface types. However, the distribution of nearest distances was different. When comparing the skin surface while wearing both interfaces, where the fit is more compressive can be visualized. For the dataset used in this study, the classic features of a hands-on Patella Tendon Bearing interface and hands-off pressure cast interface could be identified.
CONCLUSIONS: The methodology presented in this report may give researchers a further tool to better understand how interface designs affect the soft tissues of the residual limb.

Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the \"respect for tissue.\" The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety.

OBJECTIVE: This study aimed to predict tissue deformation based on the pressure applied while lying in the prone position and physiological factors.
METHODS: Healthy volunteers were instructed to lie on mattresses of four different hardness levels (50, 87.5, 175, and 262.5 N). The order in which the mattresses were used was randomized per participant. Pressure at the iliac crests was measured using a pressure mapping sensor sheet. Participants were placed in the prone position for 10 min, with pressure data used from the latter 5 min. For the tissue deformation at the iliac crests, our previous study data were used. Multiple regression analysis was used to identify predictive mechanical and physiological factors.
RESULTS: The distance between the left and right greater trochanters, maximum interface pressure and age were significant predictors for compression of the skin and soft tissue. Significant predictors of internal soft tissue displacement were the distances between the left and right anterior superior iliac spines and greater trochanters. No factors predicted skin surface displacement.
CONCLUSIONS: Our study provided predictive factors that may be measured easily in a clinical setting to reduce the risk of pressure ulcers during surgery in the prone position.

Acoustic cavitation and tissue deformation are studied by modifying a level-set method for compressible two-phase flows to consider viscoelastic tissue deformation. The numerical simulations performed using different shear moduli and bubble-tissue distances demonstrate various interactions between bubble and viscoelastic tissue, including inverted cone-shape bubbles, bubble migration, liquid jet formation, compressive and expansive tissue deformation, and tissue perforation. The bubble is observed to grow larger with increasing tissue bulk modulus and density. The maximum tissue deformation generally increases with decreasing initial bubble-tissue distance and with increasing tissue bulk modulus and density. The tissue shear modulus conditions that maximize tissue deformation are in the range of 1-10 MPa, unless the tissue density is very large.

Lymphatic uptake is essential for transporting nutrients, wastes, immune cells, and therapeutic proteins. Despite its importance, the literature lacks a quantitative analysis of the factors that affect lymphatic uptake, including interstitial pressure, downstream pressure, and tissue deformation. In this paper, we present a coupled model of a poroelastic tissue with initial lymphatics and quantify the impact of these factors on the rate of lymphatic uptake. Our results indicate that the lymphatic uptake increases with the amplitude of the oscillating downstream pressure when the amplitude exceeds a threshold. Additionally, the cross-sectional area of initial lymphatics increases with the volumetric strain of the tissue, while the interstitial pressure increases when the strain rate becomes negative. Therefore, the lymphatic uptake reaches its maximum when the tissue has positive volumetric strain while being compressed. We have also investigated the effect of intersection angles and positions of two initial lymphatics and concluded that they have minor impacts on lymphatic uptake. However, the lymphatic uptake per unit length of initial lymphatics decreases with their total length. These findings advance our understanding of lymphatic uptake and can guide the development of strategies to accelerate the transport of therapeutics.

Bone marrow biopsy (BMB) needles are frequently used in medical procedures, including extracting biological tissue to identify specific lesions or abnormalities discovered during a medical examination or a radiological scan. The forces applied by the needle during the cutting operation significantly impact the sample quality. Excessive needle insertion force and possible deflection might cause tissue damage, compromising the integrity of the biopsy specimen. The present study aims at proposing a revolutionary bioinspired needle design that will be utilized during the BMB procedure. A non-linear finite element method (FEM) has been used to analyze the insertion/extraction mechanisms of the honeybee-inspired biopsy needle with barbs into/from the human skin-bone domain (i.e., iliac crest model). It can be seen from the results of the FEM analysis that stresses are concentrated around the bioinspired biopsy needle tip and barbs during the needle insertion process. Also, these needles reduce the insertion force and reduce the tip deflection. The insertion force in the current study has been reduced by 8.6% for bone tissue and 22.66% for skin tissue layers. Similarly, the extraction force has been reduced by an average of 57.54%. Additionally, it has been observed that the needle-tip deflection got reduced from 10.44 mm for a plain bevel needle to 6.3 mm for a barbed biopsy bevel needle. According to the research findings, the proposed bioinspired barbed biopsy needle design could be utilized to create and produce novel biopsy needles for successful and minimally invasive piercing operations.

The aim of this work is to propose a mosquito-inspired (bioinspired) design of a surgical needle that can decrease the insertion force and the tissue deformation, which are the main causes of target inaccuracy during percutaneous procedures. The bioinspired needle was developed by mimicking the geometrical shapes of mosquito proboscis. Needle prototypes were manufactured and tested to determine optimized needle shapes and geometries. Needle insertion tests on a tissue-mimicking polyvinylchloride (PVC) gel were then performed to emulate the mosquito-proboscis stinging dynamics by applying vibration and insertion velocity during the insertion. An insertion test setup equipped with a sensing system was constructed to measure the insertion force and to assess the deformation of the tissue. It was discovered that using the proposed bioinspired design, the needle insertion force was decreased by 60% and the tissue deformation was reduced by 48%. This finding is significant for improving needle-based medical procedures.

Many preterm infants require mechanical ventilation as life-saving therapy. However, ventilation-induced overpressure can result in lung diseases. Considering the lung as a viscoelastic material, positive pressure inside the lung results in increased hydrostatic pressure and tissue compression. To elucidate the effect of positive pressure on lung tissue mechanics and cell behavior, we mimic the effect of overpressure by employing an uniaxial load onto fetal and adult rat lungs with different deformation rates. Additionally, tissue expansion during tidal breathing due to a negative intrathoracic pressure was addressed by uniaxial tension. We found a hyperelastic deformation behavior of fetal tissues under compression and tension with a remarkable strain stiffening. In contrast, adult lungs exhibited a similar response only during compression. Young\'s moduli were always larger during tension compared to compression, while only during compression a strong deformation-rate dependency was found. In fact, fetal lung tissue under compression showed clear viscoelastic features even for small strains. Thus, we propose that the fetal lung is much more vulnerable during inflation by mechanical ventilation compared to normal inspiration. Electrophysiological experiments with different hydrostatic pressure gradients acting on primary fetal distal lung epithelial cells revealed that the activity of the epithelial sodium channel (ENaC) and the sodium-potassium pump (Na,K-ATPase) dropped during pressures of 30 cmH2O. Thus, pressures used during mechanical ventilation might impair alveolar fluid clearance important for normal lung function.