Technical Considerations, Applications, and Benefits of Organogels in Topical Drug Delivery Systems.

Organogels represent semi-solid systems where an organic liquid phase is entrapped within a three-dimensional network formed by self-assembled, crosslinked, or entangled gelator fibers. These versatile materials find applications in a wide range of fields, including chemistry, pharmaceuticals, cosmetics, biotechnology, and food technology. Notably, in pharmacology, they serve as valuable platforms for drug and vaccine delivery, facilitating the transport of active ingredients through various routes such as transdermal, oral, and parenteral. However, their previous utility as drug delivery systems was hindered by the toxicity associated with the organic solvents used. The pharmacokinetics of medications delivered via organogels are primarily influenced by the distinctive properties of these materials, specifically their "high permeability and poor aqueous solubility," which can impact the bioavailability of the drugs. Organogels can be employed topically or for the controlled release of medications through cutaneous administration and percutaneous absorption, expanding their scope of application beyond conventional drug delivery methods. Organogels hold significant promise as drug delivery vehicles due to their biocompatibility, non-irritating properties, and thermoremanent characteristics. They enable the formulation of diverse drug delivery systems by incorporating both hydrophilic and hydrophobic bioactive compounds within the gel matrix. This comprehensive review offers an overview of organogels, encompassing their nature, synthesis, characterization, and properties. Special attention is directed towards cutting-edge technologies employed in designing organogels as potential controlled delivery systems, with a focus on their emerging therapeutic applications.

Topical delivery of insulin using novel organogel formulations: An approach for the management of diabetic wounds.

Diabetes mellitus is a growing chronic form of diabetes, with lengthy health implications. It is predicted as poor diabetic wound recovery affects roughly 25% of all diabetes mellitus patients, frequently resulting in lower traumatic injury and severe external factors and emotional expenses. The insulin-resistant condition increases biofilm development, making diabetic wounds harder to treat. Nowadays, medical treatment and management of diabetic wounds, which have a significant amputation rate, a high-frequency rate, and a high death rate, have become a global concern. Topical formulations have played a significant part in diabetic wound management and have been developed to achieve a number of features. Because of its significant biocompatibility, moisture retention, and therapeutic qualities, topical insulin has emerged as an appealing and feasible wound healing process effector. With a greater comprehension of the etiology of diabetic wounds, numerous functionalized topical insulins have been described and shown good outcomes in recent years, which has improved some diabetic injuries. The healing of wounds is a physiological phenomenon that restores skin integrity and heals damaged tissues. Insulin, a powerful wound-healing factor, is also used in several experimental and clinical studies accelerate healing of diverse injuries.

Design, Development and In-Vitro Characterization of Insulin Loaded Topical Pluronic-Lecithin Based Organogel Formulation for the Management of Diabetic Wound.

AIM: To develop and characterize the topical insulin-loaded organogel formulation for the management of diabetic wounds. OBJECTIVES: To formulate and evaluate organogel of insulin that can serve as a topical administration for promoting enhanced wound healing in diabetic patients by providing sustained and localized delivery of drug to the wound site. METHODOLOGY: The insulin organogel formulated by the micro-emulsion method involves mixing the "aqueous and oil phases" at high shear. Physical and chemical properties, as well as an in vitro study with a Franz diffusion chamber, were used to evaluate the prepared organogel. RESULTS: All formulations proved to be off-white, homogeneous, washable, and had a pH between 6 and 6.5; moreover, they were non-irritating and skin-compatible. Formulations F1-F6 had viscosity ranging from 2058 to 3168 cps, spreadability ranges of 0.35 to 0.52 g*cm/s, and gel transition ranges of 28.33 to 35.33 °C. In formulations F1-F3, the concentration of lecithin was gradually increased, and in formulations F4-F6, the concentration of PF-127 was increased, resulting in a decrease in gel transition temperature, an increase in viscosity, and a gradual change in spreadability. The higher-viscosity formulations were much more stable and had better drug release. All formulations were fitted to a kinetic model belonging to first-order kinetics. However, after examining the parameter evaluation, it was found that the formulations F2 and F6 were better suited to the kinetic model and were consistent with the first-order and Higuchi models in Korsmeyer-Peppas F2 (r2 = 0.9544 and n = 1.0412); F6 (r2 = 0.9019 and n = 1.0822), which was a confirmation of the sustainability of the release system with matrix diffusion and drug delivery mechanisms that were based on the Super-Case II transport. CONCLUSION: Further research and clinical trials are needed to validate its efficacy, optimize the formulation, and establish its long-term safety. Topical insulin organogel has the potential to revolutionize diabetic wound management by improving healing outcomes, reducing complications, and raising the standard of living for those who have diabetes.

Diabetic Wound: Pathophysiology, Complications and Treatment Strategies.

Diabetic wound healing is expected to affect 25% of all diabetics, resulting in less severe external factors, economic costs, and less trauma. Topical formulations have been continually improved to achieve a range of amazing properties and have had a significant impact on the management of diabetic wounds. Topical insulin has become one of the most attractive and convenient wound healing techniques due to its excellent biocompatibility, water retention, and therapeutic properties. Multiple versatile topical insulins have been identified and have shown promise over the past few years as they greatly facilitate the management of diabetic wounds as we understand their etiology. The physiological wound healing process repairs damaged tissue and restores skin integrity. For about a century, insulin, a powerful healing agent, and it has been utilized in several clinical and experimental researches research studies to accelerate the healing of various injuries.

Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors.

Resistive skin biosensors refer to a class of imperceptible wearable devices for health monitoring and human-machine interfacing, in which conductive materials are deposited onto or incorporated into an elastomeric polymeric sheet. A wide range of resistive skins has been developed so far to detect a wide variety of biometric signals including blood pressure, skin strain, body temperature and acoustic vibrations; however, they are typically non-specific, with one resistive signal corresponding to a single type of biometric data (one-mode sensors). Here we show a hierarchically resistive skin sensor made of a laminated cracked platinum film, vertically aligned gold nanowires and a percolated gold nanowire film, all integrated into a single sensor. As a result, hierarchically resistive skin displays a staircase-shaped resistive response to tensile strain, with distinct sensing regimes associated to a specific active material. We show that we can, through one resistive signal, identify up to five physical or physiological activities associated with the human throat speech: heartbeats, breathing, touch and neck movement (that is, a multimodal sensor). We develop a frequency/amplitude-based neural network, Deep Hybrid-Spectro, that can automatically disentangle multiple biometrics from a single resistive signal. This system can classify 11 activities-with different combinations of speech, neck movement and touch-with an accuracy of 92.73 ± 0.82% while simultaneously measuring respiration and heart rates. We validated the classification accuracy of several biometrics with an overall accuracy of >82%, demonstrating the generality of our concept.

A triple compound pendulum model to analyse the effect of an ankle-foot orthosis on swing phase kinematics.

Powered ankle-foot orthoses can be utilised to overcome gait abnormalities such as foot drop; however, normal gait is rarely restored with compensatory gait patterns arising and prevalence of gait asymmetry. Therefore, this study aims to determine the effect of orthosis mass and mass distribution on the swing phase of gait, to understand residual gait asymmetry with orthosis use. Using a triple compound pendulum model, which accounts for mass distribution of the limb and orthosis, the swing phase of gait is simulated in terms of natural dynamics and the effect of an orthosis on kinematic parameters is quantitatively determined. It was found that additional mass causes faster and shorter steps on the affected side due to rapid knee extension and reduced hip flexion, with particular actuator positions and natural cadence causing varying severity of these effects. Our study suggests that this model could be used as a preliminary design tool to identify subject specific optimum orthosis mass distribution of a powered ankle-foot orthosis, without the need for motion data or experimental trials. This optimisation intends to more accurately mimic natural swing phase kinematics, consequently allowing for the reduction in severity of gait asymmetry and the potential to improve rehabilitative outcomes.

Effects of powered ankle-foot orthoses mass distribution on lower limb muscle forces-a simulation study.

This simulation study aimed to explore the effects of mass and mass distribution of powered ankle-foot orthoses, on net joint moments and individual muscle forces throughout the lower limb. Using OpenSim inverse kinematics, dynamics, and static optimization tools, the gait cycles of ten subjects were analyzed. The biomechanical models of these subjects were appended with ideal powered ankle-foot orthoses of different masses and actuator positions, as to determine the effect that these design factors had on the subject's kinetics during normal walking. It was found that when the mass of the device was distributed more distally and posteriorly on the leg, both the net joint moments and overall lower limb muscle forces were more negatively impacted. However, individual muscle forces were found to have varying results which were attributed to the flow-on effect of the orthosis, the antagonistic pairing of muscles, and how the activity of individual muscles affect each other. It was found that mass and mass distribution of powered ankle-foot orthoses could be optimized as to more accurately mimic natural kinetics, reducing net joint moments and overall muscle forces of the lower limb, and must consider individual muscles as to reduce potentially detrimental muscle fatigue or muscular disuse. OpenSim modelling method to explore the effect of mass and mass distribution on muscle forces and joint moments, showing potential mass positioning and the effects of these positions, mass, and actuation on the muscle force integral.

Improved real-time segmentation of Intravascular Ultrasound images using coordinate-aware fully convolutional networks.

Segmentation of Intravascular Ultrasound (IVUS) images into Lumen and Media (interior and exterior) artery vessel walls is highly clinically relevant in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. When fused with position data, such segmentations also play a key role in reconstructing 3D representations of arteries. Automated segmentation in real-time is known to be a difficult image analysis problem, primarily due to artefacts commonly present in IVUS ultrasound images such as shadows, guide-wire effects, and side-branches. An additional challenge is the limited amount of expert labelled IVUS data, which limits the application of many well-performing deep learning models from other domains. To exploit the circular layered structure of the artery in B-Mode images, we propose a multi-class fully convolutional semantic segmentation network based on a minimal U-Net architecture augmented with learned translation dependence in the polar domain. The coordinate awareness in the multi-class segmentation allows the model to exploit relative spatial context about the interior and exterior vessel walls which are simply separable in polar coordinates. After training on 109 expert-labelled examples, our model significantly outperforms the state-of-the art in terms of mean Jaccard Measure (0.91 vs. 0.89) and Hausdorff distance (0.32 mm vs. 0.48 mm) on Media segmentation, and reaches equivalent performance on Lumen segmentation when evaluated on a standard publicly available dataset of 326 IVUS B-Mode images captured by 20 Mhz ultrasound probes. Using an order of magnitude fewer trainable parameters than the previous state-of-the-art, our model runs over 50 times faster and is able to execute in only 3 ms on a common GPU, achieving both leading accuracy and practical real-time performance.

Joint angle estimation with wavelet neural networks.

This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.

Towards real-time finite-strain anisotropic thermo-visco-elastodynamic analysis of soft tissues for thermal ablative therapy.

BACKGROUND AND OBJECTIVES: Accurate and efficient prediction of soft tissue temperatures is essential to computer-assisted treatment systems for thermal ablation. It can be used to predict tissue temperatures and ablation volumes for personalised treatment planning and image-guided intervention. Numerically, it requires full nonlinear modelling of the coupled computational bioheat transfer and biomechanics, and efficient solution procedures; however, existing studies considered the bioheat analysis alone or the coupled linear analysis, without the fully coupled nonlinear analysis. METHODS: We present a coupled thermo-visco-hyperelastic finite element algorithm, based on finite-strain thermoelasticity and total Lagrangian explicit dynamics. It considers the coupled nonlinear analysis of (i) bioheat transfer under soft tissue deformations and (ii) soft tissue deformations due to thermal expansion/shrinkage. The presented method accounts for anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, and it is implemented using GPU acceleration for real-time computation. RESULTS: The presented method can achieve thermo-visco-elastodynamic analysis of anisotropic soft tissues undergoing large deformations with high computational speeds in tetrahedral and hexahedral finite element meshes for surgical simulation of thermal ablation. We also demonstrate the translational benefits of the presented method for clinical applications using a simulation of thermal ablation in the liver. CONCLUSION: The key advantage of the presented method is that it enables full nonlinear modelling of the anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, instead of linear elastic, linear viscoelastic, and thermal-only modelling in the existing methods. It also provides high computational speeds for computer-assisted treatment systems towards enabling the operator to simulate thermal ablation accurately and visualise tissue temperatures and ablation zones immediately.

Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques.

BACKGROUND AND OBJECTIVE: Surgical skill assessment aims to objectively evaluate and provide constructive feedback for trainee surgeons. Conventional methods require direct observation with assessment from surgical experts which are both unscalable and subjective. The recent involvement of surgical robotic systems in the operating room has facilitated the ability of automated evaluation of the expertise level of trainees for certain representative maneuvers by using machine learning for motion analysis. The features extraction technique plays a critical role in such an automated surgical skill assessment system. METHODS: We present a direct comparison of nine well-known feature extraction techniques which are statistical features, principal component analysis, discrete Fourier/Cosine transform, codebook, deep learning models and auto-encoder for automated surgical skills evaluation. Towards near real-time evaluation, we also investigate the effect of time interval on the classification accuracy and efficiency. RESULTS: We validate the study on the benchmark robotic surgical training JIGSAWS dataset. An accuracy of 95.63, 90.17 and 90.26% by the Principal Component Analysis and 96.84, 92.75 and 95.36% by the deep Convolutional Neural Network for suturing, knot tying and needle passing, respectively, highlighted the effectiveness of these two techniques in extracting the most discriminative features among different surgical skill levels. CONCLUSIONS: This study contributes toward the development of an online automated and efficient surgical skills assessment technique.

Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation.

BACKGROUND AND OBJECTIVES: During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to surgical tool-tissue interaction and patient movement can affect the distribution of thermal energy induced. Soft tissue temperature must be obtained from the deformed tissue for precise delivery of thermal energy. However, the classical Pennes bio-heat transfer model can handle only the static non-moving state of tissue. In addition, in order to enable a surgeon to visualise the simulated results immediately, the solution procedure must be suitable for real-time thermal applications. METHODS: This paper presents a formulation of bio-heat transfer under the effect of soft tissue deformation for fast or near real-time tissue temperature prediction, based on fast explicit dynamics finite element algorithm (FED-FEM) for transient heat transfer. The proposed thermal analysis under deformation is achieved by transformation of the unknown deformed tissue state to the known initial static state via a mapping function. The appropriateness and effectiveness of the proposed formulation are evaluated on a realistic virtual human liver model with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer. RESULTS: For numerical accuracy, the proposed formulation can achieve a typical 10-3 level of normalised relative error at nodes and between 10-4 and 10-5 level of total errors for the simulation, by comparing solutions against the commercial finite element analysis package. For computation time, the proposed formulation under tissue deformation with anisotropic temperature-dependent properties consumes 2.518 × 10-4 ms for one element thermal loads computation, compared to 2.237 × 10-4 ms for the formulation without deformation which is 0.89 times of the former. Comparisons with three other formulations for isotropic and temperature-independent properties are also presented. CONCLUSIONS: Compared to conventional methods focusing on numerical accuracy, convergence and stability, the proposed formulation focuses on computational performance for fast tissue thermal analysis. Compared to the classical Pennes model that handles only the static state of tissue, the proposed formulation can achieve fast thermal analysis on deformed states of tissue and can be applied in addition to tissue deformable models for non-linear heating analysis at even large deformation of soft tissue, leading to great translational potential in dynamic tissue temperature analysis and thermal dosimetry computation for computer-integrated medical education and personalised treatment.

Neural network methodology for real-time modelling of bio-heat transfer during thermo-therapeutic applications.

Real-time simulation of bio-heat transfer can improve surgical feedback in thermo-therapeutic treatment, leading to technical innovations to surgical process and improvements to patient outcomes; however, it is challenging to achieve real-time computational performance by conventional methods. This paper presents a cellular neural network (CNN) methodology for fast and real-time modelling of bio-heat transfer with medical applications in thermo-therapeutic treatment. It formulates nonlinear dynamics of the bio-heat transfer process and spatially discretised bio-heat transfer equation as the nonlinear neural dynamics and local neural connectivity of CNN, respectively. The proposed CNN methodology considers three-dimensional (3-D) volumetric bio-heat transfer behaviour in tissue and applies the concept of control volumes for discretisation of the Pennes bio-heat transfer equation on 3-D irregular grids, leading to novel neural network models embedded with bio-heat transfer mechanism for computation of tissue temperature and associated thermal dose. Simulations and comparative analyses demonstrate that the proposed CNN models can achieve good agreement with the commercial finite element analysis package, ABAQUS/CAE, in numerical accuracy and reduce computation time by 304 and 772.86 times compared to those of with and without ABAQUS parallel execution, far exceeding the computational performance of the commercial finite element codes. The medical application is demonstrated using a high-intensity focused ultrasound (HIFU)-based thermal ablation of hepatic cancer for prediction of tissue temperature and estimation of thermal dose.

Surgical skill levels: Classification and analysis using deep neural network model and motion signals.

BACKGROUND AND OBJECTIVES: Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system. METHODS: We propose to use a deep neural network model that can analyze raw surgical motion data with minimal preprocessing. A platform with inertial measurement unit sensors was developed and participants with different levels of surgical experience were recruited to perform core open surgical skills tasks. JIGSAWS a publicly available robot based surgical training dataset was used to evaluate the generalization of our deep network model. 15 participants (4 experts, 4 intermediates and 7 novices) were recruited into the study. RESULTS: The proposed deep model achieved an accuracy of 98.2%. With comparison to JIGSAWS; our method outperformed some existing approaches with an accuracy of 98.4%, 98.4% and 94.7% for suturing, needle-passing, and knot-tying, respectively. The experimental results demonstrated the applicability of this method in both open surgery and robot-assisted minimally invasive surgery. CONCLUSIONS: This study demonstrated the potential ability of the proposed deep network model to learn the discriminative features between different surgical skills levels.

Ultrasound-based liver tracking utilizing a hybrid template/optical flow approach.

PURPOSE: With the ongoing shift toward reduced invasiveness in many surgical procedures, methods for tracking moving targets within the body become vital. Non-invasive treatment methods such as stereotactic radiation therapy and high intensity focused ultrasound, in particular, rely on the accurate localization of targets throughout treatment to ensure optimal treatment provision. This work aims at developing a robust, accurate and fast method for target tracking based on ultrasound images. METHODS: A method for tracking of targets in real-time ultrasound image data was developed, based on the combination of template matching, dense optical flow and image intensity information. A weighting map is generated from each of these approaches which are then normalized, weighted and combined, with the weighted mean position then calculated to predict the current position. The approach was evaluated on the Challenge for Liver Ultrasound Tracking 2015 dataset, consisting of a total of 24 training and 39 test datasets with a total of 53 and 85 annotated targets throughout the liver, respectively. RESULTS: The proposed method was implemented in MATLAB and achieved an accuracy of [Formula: see text] (95%: 1.91) mm and [Formula: see text] (95%: 1.85) mm on the training and test data, respectively. Tracking frequencies of between 8 and 36 fps (mean of 22 fps) were observed, largely dependent on the size of the region of interest. The achieved results represent an improvement in mean accuracy of approximately 0.3 mm over the reported methods in existing literature. CONCLUSIONS: This work describes an accurate and robust method for the tracking of points of interest within 2D ultrasound data, based on a combination of multi-template matching, dense optical flow and relative image intensity information.

Automated geometric optimization for robotic HIFU treatment of liver tumors.

BACKGROUND: High intensity focused ultrasound (HIFU) represents a non-invasive method for the destruction of cancerous tissue within the body. Heating of targeted tissue by focused ultrasound transducers results in the creation of ellipsoidal lesions at the target site, the locations of which can have a significant impact on treatment outcomes. Towards this end, this work describes a method for the optimization of lesion positions within arbitrary tumors, with specific anatomical constraints. MATERIALS & METHODS: A force-based optimization framework was extended to the case of arbitrary tumor position and constrained orientation. Analysis of the approximate reachable treatment volume for the specific case of treatment of liver tumors was performed based on four transducer configurations and constraint conditions derived. Evaluation was completed utilizing simplified spherical and ellipsoidal tumor models and randomly generated tumor volumes. The total volume treated, lesion overlap and healthy tissue ablated was evaluated. Two evaluation scenarios were defined and optimized treatment plans assessed. RESULTS: The optimization framework resulted in improvements of up to 10% in tumor volume treated, and reductions of up to 20% in healthy tissue ablated as compared to the standard lesion rastering approach. Generation of optimized plans proved feasible for both sub- and intercostally located tumors. CONCLUSIONS: This work describes an optimized method for the planning of lesion positions during HIFU treatment of liver tumors. The approach allows the determination of optimal lesion locations and orientations, and can be applied to arbitrary tumor shapes and sizes.

Quantitative Study on the Effect of Abnormalities on Respiration-Induced Kidney Movement.

Respiration-induced movement of abdominal organs hampers the targeting accuracy of non-invasive surgical techniques such as focused ultrasound surgery and radiosurgery. Unaccounted organ movement can result in either under dosage or damage to intervening healthy tissues. The respiration-induced movement is known to be significantly large in kidneys; however, the impact of abnormalities such as tumors and cysts on kidney movement is poorly understood. In this study, we quantified the movement patterns of kidneys in 48 normal and 62 affected kidneys (43 calcified cysts, 11 angiomyolipomas, 4 renal cell carcinomas and 4 polycystic kidneys) using ultrasound and simultaneously tracked the respiratory movement patterns using a stereo camera system. The kidneys were localized from 2-D ultrasound sequences using a template matching technique. The average movements of the right and left kidneys were, respectively, 24.54 ± 6.4 and 17.06 ± 3.66 mm in the superior-inferior and 13.62 ± 3.71 and 9.80 ± 3.32 mm in the transverse directions. Average movement in the superior-inferior direction of normal kidneys was greater than that of affected kidneys for both right (26.9 ± 5.1 vs. 22.6 ± 3.3, p < 0.001) and left (17.8 ± 2.5 vs. 16.1 ± 4.2, p = 0.01) kidneys. On the basis of spatial extent of abnormality, affected kidneys were categorized as category A (<10 mm in 26 patients), category B (10-20 mm in 22 patients) and category C (>20 mm in 14 patients). Compared with normal patients, the extent of movement was significantly reduced in abnormal categories B (p < 0.001) and C (p < 0.001), but the change was not significant in category A (p = 0.04). Hysteresis plots of the kidneys revealed a maximum change of 12.3 mm. The movement patterns of the kidneys also closely correlated with the respiratory movement pattern (Pearson correlation = 0.89 [right] and 0.87 [left]). We expect that the movement pattern analyses and quantification carried out in this study would aid in developing movement adaptive surgical protocols for non-invasive treatment of kidney tumors/cancers.

Vision-based force measurement using neural networks for biological cell microinjection.

This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo.

Empirical modeling of renal motion for improved targeting during focused ultrasound surgery.

Non-invasive surgery looks at ways of eliminating physical contact with the target tissues while maintaining necessary levels of accuracy. Focused Ultrasound Surgery (FUS) is one such treatment modality, which uses a tightly focused beam of high intensity ultrasound to ablate tumors in various parts of the body. For trans-abdominal access, respiration induced movement of the tissue targets remains a major issue during FUS. Respiration induced movements are known to be significant in liver and kidney. In this paper, we attempt to address this problem using non-linear prediction and modeling techniques as applicable to kidney movement patterns. Kidney movement patterns are known to be three dimensional and vastly complicated compared to movement patterns of the liver. Monitoring and quantification of the nature and extent of kidney movement is yet to be explored in depth for effective compensation and accurate targeting. Apart from the respiratory cycle, the movement of the kidney is also affected by several factors, such as the movement of the ribs, spleen and liver. Modeling of these movements is imperative for motion adaptive FUS. Since kidney movements are highly subject specific, generic statistical models cannot be used for compensation. The system latency and real-time performance of the imaging modality also induce additional parametric dependence in target tracking. In this work, we focus on empirical modeling and prediction of the kidney movement to for error analysis and computing system latency. The accuracy of existing modeling techniques is compared with a newly developed empirical model. From the study conducted in healthy volunteers, it was found that the kidney movement was complex and subject specific and could be effectively modeled using the new shape function based model. The model was further fine-tuned using Kalman filter based predictors and Adaptive Neuro-Fuzzy Inference System (ANFIS) which gave more than 85% accuracy in prediction.

Respiration-induced movement correlation for synchronous noninvasive renal cancer surgery.

Noninvasive surgery (NIS), such as high-intensity focused ultrasound (HIFU)-based ablation or radiosurgery, is used for treating tumors and cancers in various parts of the body. The soft tissue targets (usually organs) deform and move as a result of physiological processes such as respiration. Moreover, other deformations induced during surgery by changes in patient position, changes in physical properties caused by repeated exposures and uncertainties resulting from cavitation also occur. In this paper, we present a correlation-based movement prediction technique to address respiration-induced movement of the urological organs while targeting through extracorporeal trans-abdominal route access. Among other organs, kidneys are worst affected during respiratory cycles, with significant three-dimensional displacements observed on the order of 20 mm. Remote access to renal targets such as renal carcinomas and cysts during noninvasive surgery, therefore, requires a tightly controlled real-time motion tracking and quantitative estimate for compensation routine to synchronize the energy source(s) for precise energy delivery to the intended regions. The correlation model finds a mapping between the movement patterns of external skin markers placed on the abdominal access window and the internal movement of the targeted kidney. The coarse estimate of position is then fine-tuned using the Adaptive Neuro-Fuzzy Inference System (ANFIS), thereby achieving a nonlinear mapping. The technical issues involved in this tracking scheme are threefold: the model must have sufficient accuracy in mapping the movement pattern; there must be an image-based tracking scheme to provide the organ position within allowable system latency; and the processing delay resulting from modeling and tracking must be within the achievable prediction horizon to accommodate the latency in the therapeutic delivery system. The concept was tested on ultrasound image sequences collected from 20 healthy volunteers. The results indicate that the modeling technique can be practically integrated into an image-guided noninvasive robotic surgical system with an indicative targeting accuracy of more than 94%. A comparative analysis showed the superiority of this technique over conventional linear mapping and modelfree blind search techniques.