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PPG Segmentation Using Deep Learning

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PPG Segmentation Using Deep Learning

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohammad Tabbakha Hassan Al Masri Abdullatif Hammad Mohammed Alsulatie Hiam Alquran Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Faculty of Electrical Engineering & Technology Campus Pauh Putra Advanced Computing (AdvComp) Centre of Excellence Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraqs

As an indicator regarding cardiovascular health, vascular dynamics, and more, Photoplethysmography (PPG) consists of a baseline with two pulsatile peaks that reflect the blood flow variations. In the absence of critical studies and a precise understanding of PPG signal this study proposes a deep learning approach to predict and define each phase of PPG waveform utilizing Gated recurrent unit (GRU) due to its optimal performance in the short-term dependencies in sequences, the model is composed mainly of GRU layer with 20 hidden units applying tanh and sigmoid activation function, followed by dropout layer and a fully connected layer, afterward a softmax layer was added, we conducted our study on a dataset combined of an accessible Bed-Based Ballistocardiography Dataset (BBB), after that, the data was manually labeled. Following up, hyperparameters such as learning rate, batch size, and other parameters were adjusted after numerous trials, which resulted in test accuracy, precision, and recall of 0.875, 0.892, and 0.854, respectively in the used dataset. These promising results might be the first step of more future investigations concerning PPG.

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DOTnet 2.0: Deep learning network for diffuse optical tomography image reconstruction

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Intelligence-Based Medicine 2024年 9卷 100133-100133页

作者： Ko, Zhen Yu Gordon Li, Yang Liu, Jiulong Ji, Hui Qiu, Anqi Chen, Nanguang Department of Biomedical Engineering National University of Singapore Singapore Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences Beijing China Department of Mathematics National University of Singapore Singapore Department of Biomedical Engineering Johns Hopkins University Baltimore MD United States Department of Health Technology and Informatics The Hong Kong Polytechnic University (PolyU) Hong Kong Hong Kong National University of Singapore (Suzhou) Research Institute Suzhou Industrial Park Jiangsu Suzhou China

Breast cancer is the most common cancer worldwide. The standard imaging modality for breast cancer screening is X-ray mammography, which suffers from low sensitivities in women with dense breasts and can potentially cause cancers despite a low radiation dosage. Diffuse Optical Tomography (DOT) is a noninvasive imaging technique that can potentially be employed to improve breast cancer early detection. However, conventional model-based algorithms for reconstructing DOT images usually produce low-quality images with limited resolution and low reconstruction accuracy. We propose to integrate deep neural networks (DNNs) with the conventional DOT reconstruction methods. This hybrid framework significantly enhances image quality. The DNNs have been trained and tested with sample data derived from clinically relevant breast models. The sample dataset contains blood vessel structures from breast structures and artificially created vessels using the Lindenmayer-system algorithm. By comparing the hybrid reconstruction with the ground truth image, we demonstrated a multi scale - structural similarity index measure (MS-SSIM) score of 0.80–0.90. Whereas using conventional reconstruction, MS-SSIM provided a much inferior score of 0.36–0.59. In terms of DOT image quality, both qualitative and quantitative assessments of the reconstructed images signify that the hybrid approach is superior to conventional methods. This improvement suggests that DOT can potentially become a viable alternative to breast cancer screening, providing a step towards the next-generation device for optical mammography. © 2024 The Authors

关键词： Breast cancer Convolutional neural network Deep learning Diffuse optical tomography Image reconstruction

User-centered requirements for patient fluid intake monitoring support 64th

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User-centered requirements for patient fluid intake monitori...

64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Bass, Ellen J. Abbate, Andrew J. Noaiseh, Yaman Dimaria-Ghalili, Rose Ann College of Computing & Informatics United States College of Nursing & Health Professions United States School of Biomedical Engineering Science & Health Systems Drexel University PhiladelphiaPA United States

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Ergonomics

Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

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arXiv 2024年

作者： Chandrashekar, Mayanka Goethert, Ian Haque, Md Inzamam Ul McMahon, Benjamin Dhaubhadel, Sayera Knight, Kathryn Erdos, Joseph Reagan, Donna Taylor, Caroline Kuzmak, Peter Gaziano, John Michael McAllister, Eileen Costa, Lauren Ho, Yuk-Lam Cho, Kelly Tamang, Suzanne Fodeh-Jarad, Samah Ovchinnikova, Olga S. Justice, Amy C. Hinkle, Jacob Danciu, Ioana Advanced Computing for Health Sciences Computational Sciences and Engineering Division Oak Ridge National Laboratory United States IT Services Division Oak Ridge National Laboratory United States Veteran Affair’s Association United States Los Alamos National Laboratory United States Yale University United States Stanford University United States Department of Biomedical Informatics Vanderbilt University Medical Center United States

Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods: We used a DenseNet121 model pretrained MIMIC-CXR dataset for deep learning-based multilabel classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The VA-CXR dataset comprises over 259k chest X-ray images spanning between the years 2010 and 2022. Results: The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in unseen datasets, except for the label"Enlarged Cardiomediastinum." The study year’s subgroup analyses exhibited the most significant variations in multi-label classification model performance. These findings underscore the importance of considering domain shift in chest X-ray classification tasks, particularly concerning study years. Conclusion: Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and equitable model development. Addressing these challenges is crucial for advancing medical imaging and enhancing patient care. © 2024, CC BY-NC-ND.

关键词： Population statistics

Dual-Level Masked Semantic Inference for Semi-Supervised Semantic Segmentation

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IEEE Transactions on Multimedia 2025年

作者： Ma, Qiankun Zhang, Ziyao Qiao, Pengchong Wang, Yu Ji, Rongrong Liu, Chang Chen, Jie Peking University School of Electronic and Computer Engineering Shenzhen518055 China Chinese Academy of Sciences Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of the Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518055 China Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Tsinghua University Department of Automation Beijing100084 China

Semi-supervised semantic segmentation pursues a holistic pixel-wise understanding of unseen images with limited annotation. To this end, existing methods focus on regularizing per-pixel prediction consistency within unlabeled data, while rarely modeling contextual relationships. But in fact, contextual semantics can provide valuable clues for scene understanding like inner-object continuity and spatial relationships' causality. Thus, in this paper, we propose a Dual-level Masked Semantics Inference (DMSI) that takes the initiative to explicitly learn contextual relationships via enforcing our model to infer the semantics of a pixel according to its surrounding contexts. This allows our model to exhaust accurate semantics by incorporating inter-pixel context clues, further leading to comprehensive segmentation. Specifically, DMSI comprises two main components: 1) Dual-level mask consistency regularization (DMCR) that learns the ability of semantics inference by aligning the predictions of masked views with the prediction of the complete view. The masked views here come from both the image level and feature level, where our model captures low-level attributes and high-level representations respectively. 2) AdaMask that provides a proper mask position and ratio for each image, guiding our model to focus on semantic-rich regions while providing balanced training between hard and easy samples. Through learning the ability of semantic inferring, DMSI remarkably enhances the interaction between pixels, further progressively intensifying the understanding of semantics. Extensive experiments under various settings on Cityscapes and Pascal VOC 2012 show that DMSI achieves new state-of-the-art performances. Furthermore, analysis indicates that our method has superiority in mining inter-pixel semantic relationships and improving robustness facing noise corruption. © 1999-2012 IEEE.

关键词： Latent semantic analysis

RespBERT: A multi-site validation of a Natural Language Processing algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS)

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TechRxiv

TechRxiv 2023年

作者： Pathak, Ashwin Marshall, Curtis Davis, Carolyn Yang, Philip Kamaleswaran, Rishikesan School of Interactive Computing Georgia Institute of Technology AtlantaGA30308 United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA30322 United States Department of Surgery Emory University School of Medicine AtlantaGA30322 United States Division of Pulmonary Allergy Critical Care and Sleep Medicine AtlantaGA30322 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30308 United States

Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction that is associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology associated with ARDS can be highly heterogeneous, with most cases being associated with infection or trauma. ARDS is often described as a clinical syndrome associated with poor oxygenation even in the presence of mechanical ventilation. The Berlin criteria of ARDS is the current gold standard for identifying whether patients had developed ARDS, however it often requires manual adjudication of the chest radiograph, resulting in limited tools to automate the process. Since the determination of ARDS is dependent on the presence of bilateral infiltrates on radiographic images, and this information is not typically available in Electronic Medical Record (EMR). Automated determination of the presence of radiological evidence would enable robust study of the syndrome by eliminating expensive individual inspection by physicians of the images. The text of radiological reports provides an opportunity for Natural Language Processing (NLP) to determine the status of the lungs for evaluating the imaging criterion. We developed a Natural Language Processing (NLP) pipeline to analyze radiology notes of 362 patients satisfying sepsis-3 criteria from the Electronic Medical Record (EMR) to determine possible ARDS diagnosis. The radiology notes were denoised and preprocessed. They were further vectorized through the word-embedding pipeline BERT and fitted to a classification layer using transfer learning. These classification models showed F1-score of 74.5% and 64.22% for Emory and Grady dataset respectively. © 2023, CC BY.

关键词： Radiology

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

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arXiv 2023年

作者： Ji, Yuelyu Gao, Yuhe Bao, Runxue Li, Qi Liu, Disheng Sun, Yiming Ye, Ye Department of Information Science School of Computing and Information University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh United States Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh United States School of Business State University of New York at New Paltz New Paltz United States

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

关键词： COVID-19

Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

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arXiv 2025年

作者： Zhang, Hang Lou, Qian Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Health Information Management University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare. © 2025, CC BY-NC-SA.

关键词： Patient treatment