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Deep learning-enhanced chemiluminescence vertical flow assay for high-sensitivity cardiac troponin I testing

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arXiv 2024年

作者： Han, Gyeo-Re Goncharov, Artem Eryilmaz, Merve Ye, Shun Joung, Hyou-Arm Ghosh, Rajesh Ngo, Emily Tomoeda, Aoi Lee, Yena Ngo, Kevin Melton, Elizabeth Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan Electrical & Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States Department of Psychology University of California Los AngelesCA90095 United States Chemical and Biomolecular Engineering Department University of California Los AngelesCA90095 United States Mechanical and Aerospace Engineering Department University of California Los AngelesCA90095 United States Biomedical Engineering Department University of California DavisCA95616 United States Department of Pathology Laboratory Medicine University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Surgery University of California Los AngelesCA90095 United States

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, we demonstrate high-sensitivity detection of cardiac troponin I (cTnI) through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertical flow assay (CL-VFA) enables rapid, low-cost, and precise quantification of cTnI, a key cardiac protein for assessing heart muscle damage and myocardial infarction. The CL-VFA integrates a user-friendly chemiluminescent paper-based sensor, a polymerized enzyme-based conjugate, a portable high-performance CL reader, and a neural network-based cTnI concentration inference algorithm. The CL-VFA measures cTnI over a broad dynamic range covering six orders of magnitude and operates with 50 µL of serum per test, delivering results in 25 min. This system achieves a detection limit of 0.16 pg/mL with an average coefficient of variation under 15%, surpassing traditional benchtop analyzers in sensitivity by an order of magnitude. In blinded validation, the computational CL-VFA accurately measured cTnI concentrations in patient samples, demonstrating a robust correlation against a clinical-grade FDA-cleared analyzer. These results highlight the potential of CL-VFA as a robust diagnostic tool for accessible, rapid cardiac biomarker testing that meets the needs of diverse healthcare settings, from emergency care to underserved regions. Copyright © 2024, The Authors. All rights reserved.

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High-Order Cardiomyopathy Human Heart Model and Mesh Generation

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High-Order Cardiomyopathy Human Heart Model and Mesh Generat...

2021 Computing in Cardiology, CinC 2021

作者： Mohammadi, Fariba Shontz, Suzanne M Linte, Cristian A Department of Mechanical Engineering University of Kansas LawrenceKS United States Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Chester F Carlson Center for Imaging Science Rochester Institute of Technology RochesterNY United States Department of Biomedical Engineering Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9781665479165

Faithful, accurate, and successful cardiac biomechanics and electrophysiological simulations require patient-specific geometric models of the heart. Since the cardiac geometry consists of highly-curved boundaries, the use of high-order meshes with curved elements would ensure that the various curves and features present in the cardiac geometry are well-captured and preserved in the corresponding mesh. Most other existing mesh generation techniques require computer-aided design files to represent the geometric boundary, which are often not available for biomedical applications. Unlike such methods, our technique takes a high-order surface mesh, generated from patient medical images, as input and generates a high-order volume mesh directly from the curved surface mesh. In this paper, we use our direct high-order curvilinear tetrahedral mesh generation method [1] to generate several second-order cardiac meshes. Our meshes include the left ventricle myocardia of a healthy heart and hearts with dilated and hypertrophic cardiomyopathy. We show that our high-order cardiac meshes do not contain inverted elements and are of sufficiently high quality for use in cardiac finite element simulations. © 2021 Creative Commons.

关键词： Heart

Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems

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arXiv 2023年

作者： Huang, Luzhe Li, Jianing Ding, Xiaofu Zhang, Yijie Chen, Hanlong 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 Computer Science Department University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States

Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers. Here, we demonstrate an uncertainty quantification approach for deep neural networks used in inverse problems based on cycle consistency. We build forward-backward cycles using the physical forward model available and a trained deep neural network solving the inverse problem at hand, and accordingly derive uncertainty estimators through regression analysis on the consistency of these forward-backward cycles. We theoretically analyze cycle consistency metrics and derive their relationship with respect to uncertainty, bias, and robustness of the neural network inference. To demonstrate the effectiveness of these cycle consistency-based uncertainty estimators, we classified corrupted and out-of-distribution input image data using some of the widely used image deblurring and super-resolution neural networks as testbeds. The blind testing of our method outperformed other models in identifying unseen input data corruption and distribution shifts. This work provides a simple-to-implement and rapid uncertainty quantification method that can be universally applied to various neural networks used for solving inverse problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Phase-sensitive characterization of dynamics in biofilms in response to chemical cues

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Phase-sensitive characterization of dynamics in biofilms in ...

Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Choi, Honggu Zaki, Farzana Won, Jungeun Monory, Guillermo L. Boppart, Stephen A. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign UrbanaIL United States

Bacterial biofilm shows different dynamics in response to the environment. Three-dimensional phase-sensitive optical sectioning characterizes the dynamics inside of biofilm by the optical texture and the systematic Do... 详细信息

ISBN: (纸本)9781557528209

关键词： Biofilms

Single-Exposure, Single-Mask, Edge-Illumination X-ray Phase-Contrast Imaging Using a 7.8-µm Pixel Pitch Direct Conversion X-ray Detector

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Single-Exposure, Single-Mask, Edge-Illumination X-ray Phase-...

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Abdollah Pil-Ali Sahar Adnani Christopher C. Scott Alessandro Olivo Karim S. Karim Department of Electrical and Computer Engineering Centre for Bioengineering and Biotechnology University of Waterloo Waterloo CA KA Imaging Inc. Waterloo CA Department of Medical Physics and Biomedical Engineering University College London London UK

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Double-mask edge-illumination is a well-reported technique for phase-contrast x-ray imaging. Although phase retrieval using this technique is comparably easier than propagation-based phase-contrast x-ray imaging, edge-illumination can be inefficient in terms of dose efficiency and imaging time when it comes to computed tomography scanning. The illumination curve, which describes the beamlets’ intensity profile impinging upon the detector, is a key parameter that contributes to retrieving phase and absorption information. To obtain the illumination curve, multiple exposures are typically required which decreases x-ray dose efficiency and, more importantly, increases imaging time. Moreover, sample motion can negatively impact the image and information retrieval process. In this research, we employ a single mask in conjunction with a 7.8-µm pixel pitch amorphous selenium-CMOS hybrid direct conversion x-ray detector to obtain the beamlets’ intensity profile with only a single exposure. We demonstrate how using an ultra-high spatial resolution x-ray detector with a single-mask edge-illumination technique can potentially increase both dose efficiency and imaging time by at least a factor of 1.5X. Moreover, the resulting system using our approach is more compact with a source-to-detector distance of less than 30 cm. Single-exposure imaging can also help mitigate the impact of motion artifacts in the final image.

关键词： X-ray detectors Image edge detection Imaging Lighting Detectors Information retrieval Selenium

Assessing the ability of generative adversarial networks to learn canonical medical image statistics

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arXiv 2022年

作者： Kelkar, Varun A. Gotsis, Dimitrios S. Brooks, Frank J. Prabhat, K.C. Myers, Kyle J. Zeng, Rongping Anastasio, Mark A. The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Center for Devices and Radiological Health Food and Drug Administration Silver SpringMD20993 United States

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

Transformer Based Tissue Classification in Robotic Needle Biopsy

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arXiv 2024年

作者： Wang, Fanxin Cheng, Yikun Mukherjee, Sudipta S. Bhargava, Rohit Kesavadas, Thenkurussi Department of Mechatronics and Robotics Xi'an Jiaotong-Liverpool University 215123 China Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign ChampaignIL61801 United States Beckman Institute for Advanced Science and Technology Departments of Bioengineering Electrical & Computer Engineering Mechanical Science & Engineering Chemical and Biomolecular Engineering University of Illinois Urbana-Champaign ChampaignIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign ChampaignIL61801 United States Division of Research and Economic Development University at Albany State University of New York AlbanyNY12222 United States

Image-guided minimally invasive robotic surgery is commonly employed for tasks such as needle biopsies or localized therapies. However, the nonlinear deformation of various tissue types presents difficulties for surgeons in achieving precise needle tip placement, particularly when relying on low-fidelity biopsy imaging systems. In this paper, we introduce a method to classify needle biopsy interventions and identify tissue types based on a comprehensive needle-tissue contact model that incorporates both position and force parameters. We trained a transformer model using a comprehensive dataset collected from a formerly developed robotics platform, which consists of synthetic and porcine tissue from various locations (liver, kidney, heart, belly, hock) marked with interaction phases (pre-puncture, puncture, post-puncture, neutral). This model achieves a significant classification accuracy of 0.93. Our demonstrated method can assist surgeons in identifying transitions to different tissues, aiding surgeons with tissue awareness. © 2024, CC BY.

关键词： Biopsy

BPS2025 - Mapping of the molecular architecture of pathological protein deposits in Alzheimer's disease

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Biophysical Journal 2025年第3期124卷 407a-407a页

作者： Prakash Nepal Abdullah Al Bashit Theresa R.C. Stewart Bradley T. Hyman Lee Makowski Department of Bioengineering Northeastern University Boston MA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA Massachusetts General Hospital Harvard Medical School Boston MA USA

Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study

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Sleep and Vigilance 2022年第1期6卷 179-184页

作者： Agarwal, Chirag Gupta, Saransh Najjar, Muhammad Weaver, Terri E. Zhou, Xiaohong Joe Schonfeld, Dan Prasad, Bharati Department of Medicine University of Illinois Chicago 840 S. Wood Street MC 719 Chicago 60612 IL United States Jesse Brown VA Medical Center Chicago IL United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Biobehavioral Nursing Science College of Nursing University of Illinois Chicago Chicago IL United States Center for Magnetic Resonance Research University of Illinois Chicago Chicago IL United States Departments of Radiology Neurosurgery and Bioengineering University of Illinois Chicago Chicago IL United States Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL United States

Purpose: Persistent sustained attention deficit (SAD) after continuous positive airway pressure (CPAP) treatment is a source of quality of life and occupational impairment in obstructive sleep apnea (OSA). However, persistent SAD is difficult to predict in patients initiated on CPAP treatment. We performed secondary analyses of brain magnetic resonance (MR) images in treated OSA participants, using deep learning, to predict SAD. Methods: 26 middle-aged men with CPAP use of more than 6 h daily and MR imaging were included. SAD was defined by psychomotor vigilance task lapses of more than 2. 17 participants had SAD and 9 were without SAD. A Convolutional Neural Network (CNN) model was used for classifying the MR images into + SAD and − SAD categories. Results: The CNN model achieved an accuracy of 97.02 ± 0.80% in classifying MR images into + SAD and − SAD categories. Assuming a threshold of 90% probability for the MR image being correctly classified, the model provided a participant-level accuracy of 99.11 ± 0.55% and a stable image level accuracy of 97.45 ± 0.63%. Conclusion: Deep learning methods, such as the proposed CNN model, can accurately predict persistent SAD based on MR images. Further replication of these findings will allow early initiation of adjunctive pharmacologic treatment in high-risk patients, along with CPAP, to improve quality of life and occupational fitness. Future augmentation of this approach with explainable artificial intelligence methods may elucidate the neuroanatomical areas underlying persistent SAD to provide mechanistic insights and novel therapeutic targets. © 2021, This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply.

关键词： Alertness Daytime sleepiness MRI OSA Sleep apnea