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computers in Cardiology (CinC)

作者： Jake A Bergquist Lindsay C Rupp Anna Busatto Ben Orkild Brian Zenger Wilson Good Jaume Coll-Font Akil Narayan Jess Tate Dana Brooks Rob S MacLeod Scientific Computing and Imaging Institute University of Utah SLC UT USA Nora Eccles Treadwell CVRTI University of Utah SLC UT USA Department of Biomedical Engineering University of Utah SLC UT USA School of Medicine University of Utah SLC UT USA Acutus Medical Carlsbad CA USA Department of Mathematics University of Utah SLC UT USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

Electrocardiographic imaging (ECGI) is a clinical and research tool for noninvasive diagnosis of cardiac electrical dysfunction. The position of the heart within the torso is both an input and common source of error in ECGI. Many studies have sought to improve cardiac localization accuracy, however, few have examined quantitatively the effects of uncertainty in the position of the heart within the torso. Recently developed uncertainty quantification (UQ) tools enable the robust application of UQ to ECGI reconstructions. In this study, we developed an ECGI formulation, which for the first time, directly incorporated uncertainty in the heart position. The result is an ECGI solution that is robust to variation in heart position. Using data from two Langendorff experimental preparations, each with 120 heartbeats distributed across three activation sequences, we found that as heart position uncertainty increased above ±10 mm, the solution quality of the ECGI degraded. However, even at large heart position uncertainty (±40 mm) our novel UQ-ECGI formulation produced reasonable solutions (root mean squared error < 1 mV, spatial correlation >0.6, temporal correlation >0.75).

关键词： Heart Torso Location awareness Uncertainty Correlation Inverse problems Heart beat

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Machine learning methods to predict transvalvular gradient waveform post–transcatheter aortic valve replacement using preprocedural echocardiogram

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Journal of Thoracic and Cardiovascular Surgery 2025年

作者： Song, Wenyuan Sirset-Becker, Taylor Mata Quinonez, Luis René Polsani, Dhruv Polsani, Venkateshwar Yadav, Pradeep Thourani, Vinod Dasi, Lakshmi Prasad School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA United States Department of Biomedical Sciences The Ohio State University College of Medicine Columbus OH United States Department of Cardiac Surgery Piedmont Heart Institute Atlanta GA United States

Objective: Time-varying transvalvular pressure gradient after transcatheter aortic valve replacement indicates the effectiveness of the therapy. The objective was to develop a novel machine learning method enhanced by generative artificial intelligence and smart data selection strategies to predict the post–transcatheter aortic valve replacement gradient waveform using preprocedural Doppler echocardiogram. Methods: A total of 110 patients undergoing transcatheter aortic valve replacement (mean age 78.2 ± 9.0 years, 52.5% female) were included for pressure gradient collection. A deep machine learning model was trained and tested to predict postprocedural pressure gradient waveform from preprocedural pressure gradient waveform based on the proposed generative active learning framework. Results: The trained model demonstrated an average prediction accuracy of 84.85% across the 10 test patients measured from the relative mean absolute error between the predicted gradient waveform and the ground truth. The generative method improved prediction accuracy by 3.11%, whereas the data selection strategy increased it by 16.03% compared with the baseline experimental group using plain machine learning. Additionally, Bland–Altman analysis demonstrated a strong agreement between the proposed method and clinical measurements for both mean and peak pressure gradient predictions. Conclusions: A deep, generative, active machine learning model was developed to output the prediction of post–transcatheter aortic valve replacement time-varying pressure gradient from the preprocedural time-varying gradient obtained from Doppler echocardiogram. Such a predictive method may help guide decision-making for the prevention of various post–transcatheter aortic valve replacement complications. Further studies are necessary to investigate the gradient change of other valve types. © 2025 The American Association for Thoracic Surgery

关键词： deep machine learning generative active learning generative artificial intelligence predictive surgical planning pressure gradient transcatheter aortic valve implantation/replacement

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Deep networks as logical circuits: Generalization and interpretation

arXiv

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arXiv 2020年

作者： Snyder, Christopher Vishwanath, Sriram Department of Biomedical Engineering University of Texas Austin United States Department of Electrical and Computer Engineering University of Texas Austin United States

Not only are Deep Neural Networks (DNNs) black box models, but also we frequently conceptualize them as such. We lack good interpretations of the mechanisms linking inputs to outputs. Therefore, we find it difficult to analyze in human-meaningful terms (1) what the network learned and (2) whether the network learned. We present a hierarchical decomposition of the DNN discrete classification map into logical (AND/OR) combinations of intermediate (True/False) classifiers of the input. Those classifiers that can not be further decomposed, called atoms, are (interpretable) linear classifiers. Taken together, we obtain a logical circuit with linear classifier inputs that computes the same label as the DNN. This circuit does not structurally resemble the network architecture, and it may require many fewer parameters, depending on the configuration of weights. In these cases, we obtain simultaneously an interpretation and generalization bound (for the original DNN), connecting two fronts which have historically been investigated separately. Unlike compression techniques, our representation is exact. We motivate the utility of this perspective by studying DNNs in simple, controlled settings, where we obtain superior generalization bounds despite using only combinatorial information (e.g. no margin information). We demonstrate how to "open the black box" on the MNIST dataset. We show that the learned, internal, logical computations correspond to semantically meaningful (unlabeled) categories that allow DNN descriptions in plain English. We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components within the logical circuit that is the DNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Scientometric Analysis of Artificial Intelligence Research in Agriculture

Scientometric Analysis of Artificial Intelligence Research i...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Andi Pramono Miftahul Hamim Sukeipah Yuli Prihatin Rahman Arifuddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Entrepreneurship Business Creation BINUS Business School Bina Nusantara University Jakarta Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ( $\mathrm{N}=287$ ), followed by the United Kingdom at second ( $\mathrm{N}=139$ ) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index.

关键词： Data analysis Databases Multimedia systems Bibliometrics Market research Agriculture Indexes

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Imaging depth limit analysis in multiphoton microscopy using the beam propagation method

Imaging depth limit analysis in multiphoton microscopy using...

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Optics and the Brain, BRAIN 2020

作者： Cheng, Xiaojun Sadegh, Sanaz Zilpelwar, Sharvari Devor, Anna Tian, Lei Boas, David A. Department of Biomedical Engineering Boston University MA02215 United States Department of Electrical and Computer Engineering Boston University MA02215 United States Neurophotonics Center Boston University MA02215 United States Departments of Neurosciences and Radiology University of California San DiegoCA92093 United States

We apply our recently developed beam propagation model to simulate wave propagation in scattering biological tissue. The imaging depth limit is estimated for two-photon, three-photon, and non-degenerate two-photon mic... 详细信息

ISBN: (纸本)9781943580743

关键词： Photons

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Dose Requirements for Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1968-1969页

作者： Manassa, Jason Schwartz, Jonathan Jiang, Yi Zheng, Huihuo Fessler, Jeffrey A. Wendy Di, Zichao Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Phased Array Beamforming Methods for Powering biomedical Ultrasonic Implants

arXiv

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arXiv 2022年

作者： Benedict, Braeden C. Ghanbari, Mohammad Meraj Muller, Rikky UC Berkeley UC San Francisco Joint Program in Bioengineering BerkeleyCA94720 United States The Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States The Chan Zuckerberg Biohub San FranciscoCA94158 United States

Millimeter-scale implants using ultrasound for power and communication have been proposed for a range of deep-tissue applications, including neural recording and stimulation. However, published implementations have shown high sensitivity to misalignment with the external ultrasound transducer. Ultrasonic beamforming using a phased array to these implants can improve tolerance to misalignment, reduce implant volume, and allow multiple implants to be operated simultaneously in different locations. This paper details the design of a custom planar phased array ultrasound system, which is capable of steering and focusing ultrasound power within a 3D volume. Analysis and simulation is performed to determine the choice of array element pitch, with special attention given to maximizing the power available at the implant while meeting FDA limits for diagnostic ultrasound. Time reversal is proposed as a computationally simple approach to beamforming that is robust despite scattering and inhomogeneity of the acoustic medium. This technique is demonstrated both in active drive and pulse-echo modes, and it is experimentally compared with other beamforming techniques by measuring energy transfer efficiency. Simultaneous power delivery to multiple implants is also demonstrated. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Volumetric and Functional Changes in Patients with Benign and Low-Grade Tumors Receiving Partial Brain Radiotherapy

SSRN

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SSRN 2023年

作者： Finkelstein, Alan J. Tivarus, Madalina E. Venkataraman, Arun Culakova, Eva Uddin, Md Nasir Usuki, Kenneth Zhong, Jianhui Schifitto, Giovanni Milano, Michael T. Janelsins, Michelle Hardy, Sara J. Department of Biomedical Engineering University of Rochester RochesterNY United States Department of Imaging Sciences University of Rochester Medical Center RochesterNY United States Department of Neuroscience University of Rochester Medical Center RochesterNY United States Department of Neurology University of Rochester Medical Center RochesterNY United States Department of Physics and Astronomy University of Rochester RochesterNY United States Department of Radiation Oncology University of Rochester Medical Center RochesterNY United States Department of Surgery Supportive Care in Cancer Division University of Rochester Medical Center RochesterNY United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States

Background: Patients receiving partial cranial radiation therapy (RT) often experience cognitive impairment. RT is associated with dose-dependent atrophy of the cortex, hippocampus, and amygdala. However, volumetric changes may present as late as 6-12 months post-RT. Sensitive MRI biomarkers are needed to identify early RT damage to intracranial structures. We aimed to determine whether partial brain RT impacted functional network topology using resting-state functional MRI and quantifying subcortical atrophy. We hypothesized that changes in functional connectivity precedes volumetric changes. Methods: Functional connectivity and subcortical volumes were evaluated in 19 patients with low-grade or benign brain tumors receiving partial brain RT. Patients underwent baseline and six-months post-RT assessments, including 3D volumetric MRI and resting-state fMRI (rs-fMRI). The effect of RT dose on volumetric and graph theory measurements of subcortical gray matter, was analyzed. Local graph theory metrics (clustering coefficient, local efficiency) and global metrics (global efficiency, transitivity) were evaluated. Results Multivariate analysis revealed mean RT dose was significantly correlated with volumetric loss in the hippocampus, amygdala, and thalamus. Mean RT dose also significantly predicted decreases in local efficiency and clustering coefficient in the amygdala, thalamus, and putamen. Total brain RT dose significantly predicted a reduction in global efficiency and transitivity. Conclusions: Measures of functional connectivity and subcortical volume are sensitive to RT dose. Functional connectivity analysis using graph theory is a complementary method to structural MRI, capable of elucidating changes in brain network topology in the setting of partial brain radiation therapy that are not apparent with conventional structural MRI analyses. © 2023, The Authors. All rights reserved.

关键词： Radiotherapy

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A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure 23rd

A Generative Modeling Approach for Interpreting Population-L...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Ran Subakan, Cem Balwani, Aishwarya H. Whitesell, Jennifer Harris, Julie Koyejo, Sanmi Dyer, Eva L. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Mila - Quebec Artificial Intelligence Institute Montreal Canada Neuroanatomy Division Allen Institute for Brain Science Seattle United States Department of Computer Science University of Illinois at Urbana Champaign Urbana United States Department of Biomedical Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9783030597214

Understanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. Our approach starts with training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25μ m resolution. We then tap into the learned factors and validate the models expressiveness, via a novel bi-directional technique that makes structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space of the generative model to provide insights into the representations of brain structure formed in deep networks. © 2020, Springer Nature Switzerland AG.

关键词： Taps

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