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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Kim, Sujin Dahal, Madhav Bhakta, Avinash Bae, Jihye Division of Biomedical Informatics University of Kentucky 725 Rose Street LexingtonKY United States Deptment of Electrical and Computer Engineering University of Kentucky 512 Administration Drive LexingtonKY United States Department of Surgery University of Kentucky 800 Rose Street LexingtonKY United States

While colorectal cancer (CRC) screening rates are on the rise, significant disparities persist, particularly among underserved populations, highlighting ongoing challenges in achieving equitable access to preventive care. This study utilizes machine learning models to analyze multi-year data from the Health Information National Trends Survey (HINTS), identifying critical factors influencing CRC screening adherence across three distinct time periods (2003–2008, 2011–2013, 2018–2020). Using Random Forest and Logistic Regression models, interpreted through Shapley Additive exPlanations values, we examine the impact of sociodemographic characteristics, digital health engagement, and digital literacy on CRC screening behaviors. Findings reveal that age, prior screening behavior, and digital literacy are key predictors;individuals with higher digital literacy, for example, exhibited a 22% higher likelihood of adhering to CRC screening guidelines. Age emerged as a dominant factor, with screening rates peaking at 43% in the 50–64 age group. These results suggest that interventions targeting digital health literacy and enhancing provider communication may effectively improve CRC screening rates among underserved populations. This study underscores the value of data-driven approaches in informing public health strategies to increase CRC screening adherence and reduce health disparities. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Predicting Thresholds in an Auditory Overshoot Paradigm Using a Computational Subcortical Model with Efferent Feedback

Predicting Thresholds in an Auditory Overshoot Paradigm Usin...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Afagh Farhadi Laurel H. Carney Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Departments of Biomedical Engineering and Neuroscience University of Rochester Rochester NY USA

The threshold for detecting a brief tone in a long-duration Gaussian-noise masker is elevated for a tone presented near masker onset compared to a tone later in the masker. This phenomenon, referred to as overshoot, has been hypothesized to reflect the dynamic control of cochlear gain by the medial olivocochlear (MOC) component of the efferent system. In this study, we used a subcortical computational model of the auditory system that included the MOC efferent system to predict listener thresholds in the overshoot paradigm. Our simulation results matched listener thresholds, including the trend of decreasing thresholds as the delay of the tone with respect to noise onset increased from 2 to 200 ms. A model without MOC efferents had the opposite trend, with thresholds increasing in longer delay conditions. These findings support the hypothesis that the MOC efferents are involved in the overshoot effect.

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Graph Representation Learning For Stroke Recurrence Prediction 48

Graph Representation Learning For Stroke Recurrence Predicti...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Glaze, Nicholas Bayer, Artun Jiang, Xiaoqian Savitz, Sean Segarra, Santiago Rice University Department of Electrical and Computer Engineering HoustonTX United States University of Texas Health Science Center School of Biomedical Informatics HoustonTX United States University of Texas Health Science Center Department of Neurology HoustonTX United States

ISBN: (纸本)9781728163277

Stroke is one of the leading causes of death worldwide, and its mortality rate is drastically higher for patients who suffer recurrent strokes. Motivated by the recent success of graph learning methods on medical tasks, we introduce a graph representation framework for stroke recurrence prediction (GraSReP) based on patient data. In a nutshell, GraSReP sequentially consists of: i) a procedure for converting tabular, time-series patient data to a series of graphs, ii) a graph deep learning architecture that provides embeddings for the patients and, iii) a random forest classifier that predicts the patients' risk of recurrent stroke based on these graph embeddings. We demonstrate GraSReP's effectiveness for predicting recurrent strokes using real-world electronic health records, and discuss how it can be leveraged for the efficient application of preventive care. © 2023 IEEE.

关键词： graph neural network Graph representation learning stroke recurrence prediction

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Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

Evaluating Deep Learning Models for Breast Cancer Classifica...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

ISBN: (纸本)9781510686045

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 94%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. © 2025 SPIE.

关键词： Breast cancer Deep learning Tumor growth modeling Medical imaging Pathology Alternate lighting of surfaces computer engineering computer science Health informatics

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A predictive model of death from cerebrovascular diseases in intensive care units

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Intelligent Medicine 2023年第4期3卷 267-279页

作者： Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah Department of Biomedical Engineering Faculty of HealthTehran Medical SciencesIslamic Azad UniversityTehranIran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer EngineeringCollege of EngineeringUniversity of TehranTehranIran Departments of Biomedical Engineering Shahed UniversityTehranIran Loghman Medical Center Shahid Beheshti University of Medical SciencesTehranIran

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical *** approaches have been proposed forprediction in this *** of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood *** The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical *** proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU *** electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 *** time of admission and time ofdeath are labeled in all *** this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood *** the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been ***,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were *** sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than *** The results indicate that the new approach presented in this paper can be compared with other methodsor l

关键词： Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map

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An Artificial Intelligence Representation of Human Knowledge for Lung Nodule Classification

An Artificial Intelligence Representation of Human Knowledge...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Gao, Yongfeng Pomeroy, Marc Cao, Weiguo Han, Fangfang Liang, Zhengrong Stony Brook University Department of Radiology Stony BrookNY11794 United States Stony Brook University Departments of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781665488723

Low-dose computed tomography (LdCT), a recommended screening method for detection of early lung cancer, has high false positive (FP) rate, and lung nodule biopsy is a follow-up option to eliminate the FPs. It is challenging and meaningful to differentiate the nodule pathology by the LdCT screening data to avoid the costly interventional biopsy procedure. In this paper, we propose an artificial intelligence (AI) model to represent the human knowledge about lesion properties to differentiate the malignant nodules from benign ones. Three lesion properties in terms of heterogeneity, elasticity and growth are quantitatively represented by the proposed AI model. An augmented feature selection strategy was developed to integrate all lesion properties for the lesion classification. Experimental results show that the proposed AI model can achieve an AUC (area under the curve of receiver operating characteristics) score of 0.78 in the cases where physicians cannot determine the lesion type with AUC score around 0.5. © 2022 IEEE.

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MULTIMODAL DEEP LEARNING TO DIFFERENTIATE TUMOR RECURRENCE FROM TREATMENT EFFECT IN HUMAN GLIOBLASTOMA

arXiv

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arXiv 2023年

作者： Hossain, Tonmoy Qureshi, Zoraiz Jayakumar, Nivetha Muttikkal, Thomas Eluvathingal Patel, Sohil Schiff, David Zhang, Miaomiao Kundu, Bijoy Departments of Computer Science University of Virginia United States Departments of Radiology and Medical Imaging University of Virginia United States Departments of Neurology University of Virginia United States Departments of Electrical and Computer Engineering University of Virginia United States Departments of Biomedical Engineering University of Virginia United States

Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for 35 brain tumors from 26 subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was 0.56 using only the MR, 0.65 using only the SUV, and 0.71 using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to 0.62. On the other hand, MR and Ki voxels increased the test accuracy to 0.74. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM. © 2023, CC BY.

关键词： Brain

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TWIGMA: a dataset of ai-generated images with metadata from twitter 23

TWIGMA: a dataset of ai-generated images with metadata from ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yiqun T. Chen James Zou Department of Biomedical Data Science Stanford University Stanford CA Departments of Biomedical Data Science Electrical Engineering and Computer Science Stanford University Stanford CA

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://***/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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FEASIBILITY OF ASSESSING COGNITIVE IMPAIRMENT VIA DISTRIBUTED CAMERA NETWORK AND PRIVACY-PRESERVING EDGE COMPUTING

arXiv

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arXiv 2024年

作者： Hegde, Chaitra Kiarashi, Yashar Levey, Allan I. Rodriguez, Amy D. Kwon, Hyeokhyen Clifford, Gari D. Department of Biomedical Informatics Emory University School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States Department of Neurology Emory University AtlantaGA United States Department of Biomedical Informatics Emory University Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked to reduced social interactions and increased aimless movements, we aimed to automate the capture of these behaviors to enhance longitudinal monitoring. METHODS: Using a privacy-preserving distributed camera network, we collected movement and social interaction data from groups of individuals with MCI undergoing therapy within a 1700m2 space. We developed movement and social interaction features, which were then used to train a series of machine learning algorithms to distinguish between higher and lower cognitive functioning MCI groups. RESULTS: A Wilcoxon rank-sum test revealed statistically significant differences between high and low-functioning cohorts in features such as linear path length, walking speed, change in direction while walking, entropy of velocity and direction change, and number of group formations in the indoor space. Despite lacking individual identifiers to associate with specific levels of MCI, a machine learning approach using the most significant features provided a 71% accuracy. DISCUSSION: We provide evidence to show that a privacy-preserving low-cost camera network using edge computing framework has the potential to distinguish between different levels of cognitive impairment from the movements and social interactions captured during group activities. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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