A computational multilayer model to simulate hollow needle insertion into biological porcine liver tissue

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials

BACKGROUND AND OBJECTIVE

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.

METHODS

The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.

RESULTS

ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determinationR 2 . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.

CONCLUSION

ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve highR 2 values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young's modulus (E ) and Poisson's ratio (ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Kirchhoff's law-based velocity-controlled motion models to predict real-time cutting forces in minimally invasive surgeries

A theoretical framework, united by a "system effect" is formulated to model the cutting/haptic force evolution at the cutting edge of a surgical cutting instrument during its penetration into soft biological tissue in minimally invasive surgery. Other cutting process responses, including tissue fracture force, friction force, and damping, are predicted by the model as well. The model is based on a velocity-controlled formulation of the corresponding equations of motion, derived for a surgical cutting instrument and tissue based on Kirchhoff's fundamental energy conservation law. It provides nearly zero residues (absolute errors) in the equations of motion balances. In addition, concurrent closing relationships for the fracture force, friction coefficient, friction force, process damping, strain rate function (a constitutive tissue model), and their implementation within the proposed theoretical framework are established. The advantage of the method is its ability to make precise real-time predictions of the aperiodic fluctuating evolutions of the cutting forces and the other process responses. It allows for the robust modeling of the interactions between a medical instrument and a nonlinear viscoelastic tissue under any physically feasible working conditions. The cutting process model was partially qualitatively verified through numerical simulations and by comparing the computed cutting forces with experimentally measured values during robotic uniaxial biopsy needle constant velocity insertion into artificial gel tissue, obtained from previous experimental research. The comparison has shown a qualitatively similar adequate trend in the evolution of the experimentally measured and numerically predicted cutting forces during insertion of the needle.

Assessment of needle-tissue force models based on ex vivo measurements

Needle insertion is one of the most common procedures in clinical practice. Existing statistics reveal that success rates of needle insertions can be low, leading to potential complications and patient discomfort. Real-time imaging techniques like ultrasound and X-ray can assist in improving precision, but even experienced practitioners may face challenges in visualizing the needle tip. Researchers have proposed models of force interactions during needle insertions into biological tissue to enhance accuracy. This article presents an evaluation of the forces acting on intravenous needles during insertion into skin. The aim was to explore mathematical models, compare them with data from tests on animal specimens, and select the most suitable model for future research. The experimental setup involved conducting needle insertion tests on animal-originated cadavers, using the Brucker Universal Mechanical Tester device, which measured the force response during vertical movement of the needle. The research was divided into 2 stages. In Stage I, force measurements were recorded for both the insertion and extraction phases of the hypodermic needles. The measurements were conducted for several different needle sizes, speed and insertion angles. In Stage II, five different models were examined to determine how well they matched the experimental data. Based on the analysis of fit quality coefficients, the Gordon's exponential model was identified as the best fit to the measured data. The influence of needle size, insertion angle, and insertion speed on the measured force values was confirmed. Different insertion speeds revealed the viscoelastic properties of the tested samples. The presence of the skin layer affected the puncture force and force values for subsequent layers.

A high-fidelity virtual liver model incorporating biological characteristics

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, we proposed a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics. Besides, to the best of our knowledge, our study is the first to introduce the viscoplasticity of biological tissues to improve the fidelity of the liver model. This mothod was proposed to describe the viscoplastic characteristics of the diseased liver resection process, when the liver is in a state of excessive deformation and loss of elasticity, however, there are few works focusing on this problem. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity.

A study on modeling the deflection of surgical needle during insertion into multilayer tissues

The use of subcutaneous and percutaneous needle and catheter insertions is standard in modern clinical practice. However, a common issue with bevel tip surgical needles is their tendency to deflect, causing them to miss the intended target inside the tissue. This study aims to understand the interaction between the needle and soft tissue and develop a model to predict the deflection of a bevel tip needle during insertion into multi-layered soft tissues. The study examined the mechanics of needle-tissue interaction and modeled the forces involved during insertion. The force model includes cutting force, deformation force, and friction between the needle and tissue. There was an 8%-23% difference between the total analytical and experimental force measurements. A modified Euler-Bernoulli beam elastic foundation theory was used to create an analytical model to predict the needle tip deflection in soft tissue. To validate the results, the analytical deflection model was then compared to the deflection from needle insertion experiments on multi-layered phantom tissues, showing a 9%-21% error between the two. While there is a slight discrepancy between the analytical and experimental results, the study shows that the proposed model can accurately predict needle tip deflection during insertion.

Honeybee stinger-based biopsy needle and influence of the barbs on needle forces during insertion/extraction into the iliac crest: A multilayer finite element approach

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.

A new soft tissue deformation model based on Runge-Kutta: Application in lung

Flexible body deformation model is the most critical research in the field of telemedicine, which decides whether the deformation process of tissues and organs can be simulated in real time and realistically. Basically, the improvement of model accuracy often means the loss of real-time performance. In order to effectively balance between accuracy and real-time performance, this paper proposes a new model, which uses the step-variable fourth-order Runge-Kutta method for the first time to solve the relationship problem between the external force and displacement of the nodes in the finite element deformation of the lung. To improve the real-time performance of the model, a one-stage solution optimization algorithm is proposed to optimize the step size selection problem. Finally, an accelerated two-level node update algorithm is proposed to further improve the real-time performance. A lung surgery simulation platform with 3DMax2020 and OpenGL4.5 is built to verify the accuracy, efficiency, realism and applicability of the model. Experimental results show that the proposed lung model can achieve real-world visual reproduction during remote surgery, and exceeds other 13 reference models in real-time performance, with natural deformation effect, high simulation accuracy, which is suitable for modeling normal lung and four types of lungs suffering from diseases.