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Minimally invasive surgical techniques using catheter is now used in many procedures. Development of surgical training of such procedures requires real‐time simulation of tool‐organ interaction. In such processes, each subsequent step of interaction would be based on the current configuration of the surgical tool (guidewire in this case), leading to development of techniques to solve and visualize the configuration of tool at every time step. This paper presents a Finite Element (FEM) based approach to simulate the tool‐organ interaction. [ABSTRACT FROM AUTHOR]

The kymograph is device for measuring and presenting pressure‐based signals, such as human heart beat and artery volume pressure. The Haptic Kymograph is a haptically‐enhanced tele‐medicine system which is used to acquire human vital signs and then to transform these signs into sensible, scalable and ubiquitous media so that a user can easily comprehend subtle and ambiguous signals in a remote place. In an experiment setup a patient's artery pressure pulse was captured at 200 Hz of sampling rate, transmitted via TCP/IP network, and replicated in a remote place using a PHANToM haptic device coupled with a real‐time visual interface. In this paper we report our recent progresses in developing a low‐cost input system, network interfaces and haptic replication of the human artery volume pulse signal. [ABSTRACT FROM AUTHOR]

Subsurface modeling of deformable objects, such as soft tissues and organs, involves the use of non‐destructive methods of determining the properties of an object encased by a material. Some of the properties that can be determined from subsurface modeling include: shape, hardness, texture and possibly material. The ability to determine these properties is based on the accuracy of the method used and the properties of the surface encasing the object. As computers become more powerful and are able to produce even more realistic graphics, it will be possible to store and recreate precise duplicates of the original for later analysis. This paper will present a method of approximately modeling both the surface and an object below the surface of the skin by a method of palpation and then present this data in an interactive 3‐D model. [ABSTRACT FROM AUTHOR]

Endovascular neurosurgery has gained acceptance as the best method of treatment of vascular abnormalities like cerebral aneurysms. However, the procedure is associated with difficulties in tool/tissue manipulation. Navigation of stent, catheter and, guide wire through complex arteries without any force information often causes stent snagging, plaque dislocations and formation of thrombosis caused by the damage of the arterial wall. Currently, there is no haptic device available which can provide the surgeons with the force information, related to stent placement procedure. The goal of this work is to create a data base for a fast synthetic endovascular force simulator, which will provide the surgeon with force information during tool-artery interaction, based on the various combinations of tool sizes and vessel complexity, [1, 2] to facilitate better preoperative planning, safer interventions, and advanced training of new surgeons. [ABSTRACT FROM AUTHOR]

Trocar insertion, the first step to most micro surgery procedures is a difficult procedure to learn and practice because procedure is carried out almost entirely without any visual feedback of the organs underlying the tissue being punctured. A majority of injuries is attributed to the excessive use of force by the surgeon. This paper looks at developing a haptic based trocar insertion simulator which will assist in training and skill advancement in carrying out this procedure. Different issues regarding development of force model and and trocar-tissue interaction is studied. [ABSTRACT FROM AUTHOR]

Grounded in communication models of cultural competence, this study reports on the development and testing of the first module in a larger virtual reality (VR) implicit bias training for physicians to help them better: (a) recognize implicit bias and its effects on communication, patients, and patient care; (b) identify their own implicit biases and exercise strategies for managing them; and (c) learn and practice communicating with BIPOC patients in a culture-centered manner that demonstrates respect and builds trust. Led by communication faculty, a large, interdisciplinary team of researchers, clinicians, and engineers developed the first module tested herein focused on training goal (a). Within the module, participants observe five scenes between patient Marilyn Hayes (a Black woman) and Dr. Richard Flynn (her obstetrician, a White man) during a postpartum visit. The interaction contains examples of implicit bias, and participants are asked to both identify and consider how implicit bias impacts communication, the patient, and patient care. The team recruited 30 medical students and resident physicians to participate in a lab-based study that included a pretest, a training experience of the module using a head-mounted VR display, and a posttest. Following the training, participants reported improved attitudes toward implicit bias instruction, greater importance of determining patients' beliefs and perspectives for history-taking, treatment, and providing quality health care; and greater communication efficacy. Participants' agreement with the importance of assessing patients' perspectives, opinions, and psychosocial and cultural contexts did not significantly change. Implications for medical education about cultural competency and implicit bias are discussed.

Although cone-beam computed tomography (CBCT) is the predominant cross-sectional imaging modality used by interventional radiologists, advancements in hybrid angiography-computed tomography (Angio-CT) technology have demonstrated a strong potential for increased utilization in the angiography suite. The benefits of increased workflow efficiency, decreased relative cost, multipurpose utility, and most importantly improved patient care, may encourage institutions to accept the higher upfront cost of Angio-CT rooms for potential long-term benefits. Hybrid Angio-CT setups are less prone to motion and streak artifacts and may provide superior image quality compared to CBCT. Although Angio-CT technology has been most frequently utilized for oncologic treatments, its use is being expanded across multiple applications. In this review, we illustrate 6 non-hepatic cases at two tertiary hospitals that each showcase how Angio-CT technology enabled an intervention., (Copyright © 2025. Published by Elsevier Inc.)

Competing Interests: None.

Objectives: To evaluate suturing skills of veterinary students using 3 common performance assessments (PAs) and to compare findings to data obtained by an electromyographic armband., Sample: 16 second-year veterinary students., Procedures: Students performed 4 suturing tasks on synthetic tissue models 1, 3, and 5 weeks after a surgical skills course. Digital videos were scored by 4 expert surgeons using 3 PAs (an Objective Structured Clinical Examination [OSCE]- style surgical binary checklist, an Objective Structured Assessment of Technical Skill [OSATS] checklist, and a surgical Global Rating Scale [GRS]). Surface electromyography (sEMG) data collected from the dominant forearm were input to machine learning algorithms. Performance assessment scores were compared between experts and correlated to task completion times and sEMG data. Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC). Inter-rater agreement was calculated using percent agreement with varying levels of tolerance., Results: Reliability was moderate for the OSCE and OSATS checklists and poor for the GRS. Agreement was achieved for the checklists when moderate tolerance was applied but remained poor for the GRS. sEMG signals did not correlate well with checklist scores or task times, but features extracted from signals permitted task differentiation by routine statistical comparison and correct task classification using machine learning algorithms., Clinical Relevance: Reliability and agreement of an OSCE-style checklist, OSATS checklist, and surgical GRS assessment were insufficient to characterize suturing skills of veterinary students. To avoid subjectivity associated with PA by raters, further study of kinematics and EMG data is warranted in the surgical skills evaluation of veterinary students.

Noroviruses (NoVs) cause over 90% of non-bacterial gastroenteritis outbreaks in adults and children in developed countries. Therefore, there is a need for approaches to mitigate the transmission of noroviruses in workplaces to reduce their substantial health burden. We developed and validated a low-cost, autonomous robot called the UVBot to disinfect occupational spaces using ultraviolet (UV) lamps. The total cost of the UVBOT is less than USD 1000, which is much lower than existing commercial robots that cost as much as USD 35,000. The user-friendly desktop application allows users to control the robot remotely, check the disinfection map, and add virtual walls to the map. A 2D LiDAR and a simultaneous localization and mapping (SLAM) algorithm was used to generate a map of the space being disinfected. Tulane virus (TV), a human norovirus surrogate, was used to validate the UVBot's effectiveness. TV was deposited on a painted drywall and exposed to UV radiation at different doses. A 3-log (99.9%) reduction of TV infectivity was achieved at a UV dose of 45 mJ/cm 2 . We further calculated the sanitizing speed as 3.5 cm/s and the efficient sanitizing distance reached up to 40 cm from the UV bulb. The design, software, and environment test data are available to the public so that any organization with minimal engineering capabilities can reproduce the UVBot system.

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Manjunatha, Pareek, Jujjavarapu, Ghobadi, Kesavadas and Esfahani.)

Currently, most of the high-performance models for frequency recognition of steady-state visual evoked potentials (SSVEPs) are linear. However, SSVEPs collected from different channels can have non-linear relationship among each other. Linearly combining electroencephalogram (EEG) from multiple channels is not the most accurate solution in SSVEPs classification. To further improve the performance of SSVEP-based brain-computer interface (BCI), we propose a convolutional neural network-based non-linear model, i.e. convolutional correlation analysis (Conv-CA). Different from pure deep learning models, Conv-CA use convolutional neural networks (CNNs) at the top of a self-defined correlation layer. The CNNs function on how to transform multiple channel EEGs into a single EEG signal. The correlation layer calculates the correlation coefficients between the transformed single EEG signal and reference signals. The CNNs provide non-linear operations to combine EEGs in different channels and different time. And the correlation layer constrains the fitting space of the deep learning model. A comparison study between the proposed Conv-CA method and the task-related component analysis (TRCA) based methods is conducted. Both methods are validated on a 40-class SSVEP benchmark dataset recorded from 35 subjects. The study verifies that the Conv-CA method significantly outperforms the TRCA-based methods. Moreover, Conv-CA has good explainability since its inputs of the correlation layer can be analyzed for visualizing what the model learnt from the data. Conv-CA is a non-linear extension of spatial filters. Its CNN structures can be further explored and tuned for reaching a better performance. The structure of combining neural networks and unsupervised features has the potential to be applied to the classification of other signals.

Surgeons, while performing manual endovascular procedures with conventional surgical tools (catheters and guidewires), experience forces on the tool outside the patient's body that are proximal to the point of actuation. Currently, most of the robotic systems for endovascular procedures use active catheters to navigate vasculature and to measure the contact forces at the distal end (tool tip). These tools are more expensive than the conventional surgical tools used in endovascular procedures. To avoid dependence on specialized devices like active catheters, we have developed a novel endovascular robotic system (ERS) that uses conventional surgical tools. Our robot can indirectly measure proximal forces and provide haptic feedback to surgeons. This paper discusses the theory, methodology, and calibration of indirect proximal force measurement. This new calibration technique is presented as a nested optimization problem that is solved using bi-level optimization. The results of experimental validation of the new force calibration methodology are also discussed. The results show that unbiasing of the indirect force measurement by means of force calibration will allow the use of conventional tools in robotic endovascular procedures., (© 2019 John Wiley & Sons, Ltd.)