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Evaluating Smoothness of Force for Surgical Skill Assessment

Evaluating Smoothness of Force for Surgical Skill Assessment

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Simar P. Singh Amir Mehdi Shayan Jianxin Gao Joe Biblev Richard E. Groff Ravikiran Singapogu Department of Bioengineering Clemson University Clemson SC USA Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Mathematical and Statistical Sciences Clemson University Clemson SC USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The efficient execution of surgical operations plays a crucial role in optimizing patient outcomes, evidencing the need for effective training methods to improve surgical skills. Medical training simulators, praised for objective, automated skill assessment, require instrumented sensors and relevant metrics for targeted feedback on important aspects of a surgical procedure. Traditional metrics that capture a single instance of force, such as peak or range, lack the characterization of the entire force profile and lose subtleties that may limit accurate evaluation of the skilled application of force, a valuable aspect of assessment in surgery. This study introduces novel force metrics inspired by motion smoothness-based measures, analyzed on an extensive dataset of 97 subjects suturing on an open vascular suturing simulator. We validated the effectiveness of these metrics by comparing the metric scores for subjects with different skill levels. Our findings highlight the value of these advanced force metrics as robust indicators of suturing performance, demonstrating their valuable potential for more accurate and objective skill assessment in surgical *** Relevance—- The force metrics presented in this study seek to quantify the widely used category of assessment in surgery, "respect for tissue", potentially benefiting surgical education with an improved evaluation of this aspect of the suturing skill.

关键词： Measurement Training Force measurement Accuracy Instruments Force Surgery Sensor phenomena and characterization Motion measurement engineering in medicine and biology

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Active Cancellation of Direct Feedthrough Enables 100-fold More Sensitive Mass-Limited Detection in Magnetic Particle Sensing Systems

Active Cancellation of Direct Feedthrough Enables 100-fold M...

引用

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Quincy Huynh Owen Doyle K.L Barry Fung Chinmoy Saayujya Steven Conolly Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA Department of Bioengineering University of California Berkeley Berkeley CA USA

Magnetic particle imaging (MPI) is a tracer imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIOs), enabling sensitive, radiation-free imaging of cells and disease pathologies. The arbitrary waveform relaxometer (AWR) is an indispensable platform for developing magnetic nanoparticle tracers and evaluating tracer performance for magnetic particle imaging applications. One of the biggest challenges in arbitrary waveform excitation is direct feedthrough interference, which is usually six orders of magnitude larger than the signal from magnetic nanoparticles. This work will showcase hardware that suppresses this interference by an order of magnitude, increasing the dynamic range of the instrument and enabling mass-limited detection at full scale range.

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Enhanced Breast Cancer Molecular Biomarker Classification: A Novel Two-Stage Machine Learning Pipeline for Accurate Histological Analysis of Whole Slide Images

Enhanced Breast Cancer Molecular Biomarker Classification: A...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Aboudessouki, A. Ali, Kh.M. Alksas, A. Elsharkawy, M. Rahma, M. Abo Ghazal, M. Mekky, N. El-Daydamony, E. Gondim, D. El-Baz, A. BioImaging Laboratory Bioengineering Department University of Louisville United States Pathology Department Faculty of Medicine Mansoura University Egypt Mansoura University Hospital Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University United Arab Emirates Department of Information Technology Faculty of Computers and Information Mansoura University Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

Breast cancer, a prevalent and diverse form of cancer, is characterized by unique clinicopathologic features. Accurate classification of its molecular subtype, essential for targeted treatment and improved survival rates, relies on testing molecular biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 antigen. This study introduces an innovative two-stage machine learning pipeline for breast cancer PR molecular biomarker classification using whole slide immunohistochemistry images. Our approach consists of two specialized stages. In the first stage, a watershed algorithm segments the Cells from the input image. Morphological and statistical features extracted from these segments are then used by machine learning classifiers to distinguish between tumor and nontumor tissues [1]-[3]. The second stage focuses on the tumor tissues that were identified in the first stage, extracting texture and appearance features to classify them into positive or negative biomarker responses, again using machine learning classifiers [4], [5]. This method not only automates the classification of PR biomarkers in tumor regions but also generates a detailed image map for each cell in the Whole Slide Image (WSI). By doing so, it can determine whether the tissue represents tumor cells and whether the tumor tissue exhibits positive or negative PR status. The results show the ability of our machine learning-based approach to augment pathologists' diagnostic capabilities, offering significant advancements in the automated classification of histopathological images. © 2025 IEEE.

关键词： Breast cancer Histopathology Immunohistochemistry Machine Learning Marker-based Watershed Segmentation Molecular biomarker Status Molecular biomarkers Tissue classification Tumor Detection Watershed Segmentation

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Analysis of a Broadband Metamaterial Absorber at Terahertz Frequencies

Analysis of a Broadband Metamaterial Absorber at Terahertz F...

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International Conference on electrical Information and Communication Technology (EICT)

作者： Oishi Jyoti Pritu Parna Sarkar Md. Samiul Habib Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Bioengineering & Biomedical Engineering University of Texas Rio Grande Valley Edinburg Texas United States Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

In this paper, we present a vanadium dioxide (VO 2 ) based metamaterial absorber at terahertz (THz) frequencies, achieving near unity absorption from 3.5 THz to 5 THz through full vector numerical simulation which refers to a computational method that considers complete vector nature of electromagnetic fields in three dimensions. Our proposed absorber is based on a single layered elliptic shaped ring resonator, which provides more design freedom than the existing absorbers and is easier to fabricate. Our numerical simulations show that modifying the conductivity of VO 2 significantly enhances the absorption peak, and reaches near-perfect absorption when the conductivity of VO 2 reaches the metallic phase. We illustrate that for a specific conductivity of the metal layer, the absorption peak of the absorber can be controlled by adjusting the width of the ring, the ellipticity of the ring, and the thickness of the dielectric. We also explain the physical mechanism and origin of broadband absorption through impedance matching theory. We believe that our proposed absorber may find applications in sensing and imaging technology.

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Control Strategies for Electric Vehicle Drives: FOC Implementation with MTPA and Field Weakening

Control Strategies for Electric Vehicle Drives: FOC Implemen...

引用

ELEKTRO

作者： Majda Sadaf Adil Farooq Zahid Ullah Muhammad Ahsan School of Electrical and Electronic Engineering Newcastle University United Kingdom Department of Electronic Engineering Maynooth University Ireland Department of Electronics Information and Bioengineering Politecnico di Milano Italy Department of Computer Science University of Alabama at Birmingham USA

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

The reliability and performance of Electric vehicles (EVs) rely on effective control of electric drives, thereby improving the overall driving experience. To efficiently control the electric drive, this paper implemented a Field Oriented Control (FOC) scheme for controlling a permanent magnet synchronous motor (PMSM) drive designed for EVs. The FOC scheme is integrated with Maximum Torque per Ampere (MTPA) and field weakening control strategies to broaden the speed range of the drive at rated power density and maximum torque. The entire FOC control scheme and MTPA control reference function block are modeled using MATLAB/SIMULINK. The paper comprehensively reviews the mathematical equations governing FOC, field weakening control, and MTPA methods. The research also incorporates simulation results obtained under various driving conditions for EVs, demonstrating the effectiveness of the implemented control strategies.

关键词： Power system measurements Torque Reviews Simulation Electric vehicles Synchronous motors Permanent magnet motors

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Normative Modeling Via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer’s Disease

Normative Modeling Via Conditional Variational Autoencoder a...

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IEEE International Symposium on Biomedical Imaging

作者： Xuetong Wang Rong Zhou Kanhao Zhao Alex Leow Yu Zhang Lifang He Department of Computer Science and Engineering Lehigh University PA USA Department of Bioengineering Lehigh University PA USA Department of Psychiatry University of Illinois at Chicago IL USA Department of Electrical and Computer Engineering Lehigh University PA USA

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer’s Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.

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Opto2P-FCM: A MEMS Based Miniature Two-Photon Microscope with Patterned Optogenetic Stimulation

Opto2P-FCM: A MEMS Based Miniature Two-Photon Microscope wit...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Futia, Gregory L. Zohrabi, Mo McCullough, Connor Teel, Alec de Souza, Fabio Simoes Oroke, Ryan Bright, Victor M. Restrepo, Diego Gopinath, Juliet T. Gibson, Emily A. Department of Bioengineering University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Cell and Developmental Biology University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

Miniaturized microscopes for monitoring neural activity are an indispensable tool for neuroscience research. We present a novel MEMS based miniature microscope with patterned optogenetic stimulation capabilities enabl... 详细信息

ISBN: (纸本)9781957171340

关键词： Microscopes

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3D Virtual Refocusing of Point Spread Function (PSF) Engineered Images Using Cascaded Neural Networks

3D Virtual Refocusing of Point Spread Function (PSF) Enginee...

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CLEO: Science and Innovations, S and I 2022

作者： Yang, Xilin Huang, Luzhe Luo, Yilin Wu, Yichen Wang, Hongda Rivenson, Yair Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

We present a virtual refocusing method over an extended depth-of-field (DOF) using point-spread function (PSF) engineering and cascaded deep neural networks. © Optica Publishing Group 2022, © 2022 The Author(s)

ISBN: (纸本)9781557528209

关键词： Optical transfer function

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Virtual histological stain transformations through cascaded deep neural networks

Virtual histological stain transformations through cascaded ...

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Frontiers in Optics, FiO 2022

作者： Yang, Xilin Bai, Bijie Li, Yuzhu Zhang, Yijie Liu, Tairan de Haan, Kevin Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

We present a deep learning-based framework to virtually transfer brightfield images of H&E-stained tissue slides to other types of stains using cascaded networks, providing high-quality images of special stains fr...

ISBN: (纸本)9781557528209

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All-Optical Computation of an Arbitrary Linear Transform Using Diffractive Networks

All-Optical Computation of an Arbitrary Linear Transform Usi...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Kulce, Onur Mengu, Deniz Rivenson, Yair Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

ISBN: (纸本)9781557528209

关键词： Deep learning

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