IEEE Open Journal of Engineering in Medicine and Biology最新文献_第3页

Surface-Based Ultrasound Scans for the Screening of Prostate Cancer 基于表面的超声扫描筛查前列腺癌

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-20 DOI: 10.1109/OJEMB.2024.3503494

Rory Bennett;Tristan Barrett;Vincent J. Gnanapragasam;Zion Tse

{"title":"Surface-Based Ultrasound Scans for the Screening of Prostate Cancer","authors":"Rory Bennett;Tristan Barrett;Vincent J. Gnanapragasam;Zion Tse","doi":"10.1109/OJEMB.2024.3503494","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3503494","url":null,"abstract":"Surface-based ultrasound (SUS) systems have undergone substantial improvement over the years in image quality, ease-of-use, and reduction in size. Their ability to image organs non-invasively makes them a prime technology for the diagnosis and monitoring of various diseases and conditions. An example is the screening/risk- stratification of prostate cancer (PCa) using prostate-specific antigen density (PSAD). Current literature predominantly focuses on prostate volume (PV) estimation techniques that make use of magnetic resonance imaging (MRI) or transrectal ultrasound (TRUS) imaging, while SUS techniques are largely overlooked. If a reliable SUS PCa screening method can be introduced, patients may be able to forgo unnecessary MRI or TRUS scans. Such a screening procedure could be introduced into standard primary care settings with point-of-care ultrasound systems available at a fraction of the cost of their larger hospital counterparts. This review analyses whether literature suggests it is possible to use SUS-derived PV in the calculation of PSAD.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"212-218"},"PeriodicalIF":2.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring 基于混合深度学习的PICU患者监护中增强闭塞分割

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-20 DOI: 10.1109/OJEMB.2024.3503499

Mario Francisco Munoz;Hoang Vu Huy;Thanh-Dung Le;Philippe Jouvet;Rita Noumeir

{"title":"Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring","authors":"Mario Francisco Munoz;Hoang Vu Huy;Thanh-Dung Le;Philippe Jouvet;Rita Noumeir","doi":"10.1109/OJEMB.2024.3503499","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3503499","url":null,"abstract":"Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. \u0000<italic>Goal:\u0000 In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. \u0000<italic>Methods:\u0000 First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85%. \u0000<italic>Results:\u0000 Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75% gain in performance compared to the baseline CNN-based framework. \u0000<italic>Conclusions:\u0000 Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"176-182"},"PeriodicalIF":2.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Corrections to “Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis” 用于胃癌前病变诊断的胃部切片相关网络 "的更正

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-11 DOI: 10.1109/OJEMB.2024.3452970

Jyun-Yao Jhang;Yu-Ching Tsai;Tzu-Chun Hsu;Chun-Rong Huang;Hsiu-Chi Cheng;Bor-Shyang Sheu

引用次数: 0

IEEE Open Journal of Engineering in Medicine and Biology Author Instructions IEEE Open Journal of Engineering in Medicine and Biology 作者说明

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-08 DOI: 10.1109/OJEMB.2024.3387893

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Design and Validation of a Tripping-Eliciting Platform Based on Compliant Random Obstacles 基于柔性随机障碍物的触发平台设计与验证

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-08 DOI: 10.1109/OJEMB.2024.3493619

Eugenio Anselmino;Lorenzo Pittoni;Tommaso Ciapetti;Michele Piazzini;Claudio Macchi;Alberto Mazzoni;Silvestro Micera;Arturo Forner-Cordero

{"title":"Design and Validation of a Tripping-Eliciting Platform Based on Compliant Random Obstacles","authors":"Eugenio Anselmino;Lorenzo Pittoni;Tommaso Ciapetti;Michele Piazzini;Claudio Macchi;Alberto Mazzoni;Silvestro Micera;Arturo Forner-Cordero","doi":"10.1109/OJEMB.2024.3493619","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3493619","url":null,"abstract":"<italic>Goal:\u0000 The experimental study of the stumble phenomena is essential to develop novel technological solutions to limit harmful effects in at-risk populations. A versatile platform to deliver realistic and unanticipated tripping perturbations, controllable in their strength and timing, would be beneficial for this field of study. \u0000<italic>Methods:\u0000 We built a modular tripping-eliciting system based on multiple compliant trip blocks that deliver unanticipated tripping perturbations. The system was validated with a study with 9 healthy subjects. \u0000<italic>Results:\u0000 The system delivered 33 out of 34 perturbations (a minimum of 3 per subject) during the desired gait phase, and 31 effectively induced a tripping event. The recovery strategies adopted after the perturbations were qualitatively consistent with the literature. The analysis of the inertial motion unit signals and the questionnaires suggests a limited adaptation to the perturbation throughout experiments. \u0000<italic>Conclusions:\u0000 The platform succeeded in providing realistic trip perturbations, concurrently limiting subjects’ adaptation. The presence of multiple compliant obstacles, tunable regarding position and perturbation strength, represents a novelty in the field, allowing the study of stumbling phenomena caused by obstacles with different levels of sturdiness. The overall system is modular and can be easily adapted for different applications.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"168-175"},"PeriodicalIF":2.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Open Journal of Engineering in Medicine and Biology Editorial Board Information IEEE Open Journal of Engineering in Medicine and Biology 编辑委员会信息

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-08 DOI: 10.1109/OJEMB.2024.3387895

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Guest Editorial: Introduction to the Special Series on Advances in Cardiovascular and Respiratory Systems Engineering 特约编辑：心血管和呼吸系统工程进展特别丛书简介

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IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-11-07 DOI: 10.1109/OJEMB.2024.3486457

Riccardo Barbieri;Maximiliano Mollura

引用次数: 0

HCM-Echo-VAR-Ensemble: Deep Ensemble Fusion to Detect Hypertrophic Cardiomyopathy in Echocardiograms HCM-Echo-VAR-Ensemble：深Ensemble融合在超声心动图中检测肥厚性心肌病

IF 2.7

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-10-25 DOI: 10.1109/OJEMB.2024.3486541

Abdulsalam Almadani;Atifa Sarwar;Emmanuel Agu;Monica Ahluwalia;Jacques Kpodonu

{"title":"HCM-Echo-VAR-Ensemble: Deep Ensemble Fusion to Detect Hypertrophic Cardiomyopathy in Echocardiograms","authors":"Abdulsalam Almadani;Atifa Sarwar;Emmanuel Agu;Monica Ahluwalia;Jacques Kpodonu","doi":"10.1109/OJEMB.2024.3486541","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3486541","url":null,"abstract":"<italic>Goal:\u0000 To detect Hypertrophic Cardiomyopathy (HCM) from multiple views of Echocardiogram (cardiac ultrasound) videos. \u0000<italic>Methods:\u0000 we propose \u0000<italic>HCM-Echo-VAR-Ensemble\u0000, a novel framework that performs binary classification (HCM vs. no HCM) of echocardiogram videos directly using an ensemble of state-of-the-art deep VAR architectures models (SlowFast and I3D), and fuses their predictions using majority averaging ensembling. \u0000<italic>Results:\u0000 \u0000<italic>HCM-Echo-VAR-Ensemble\u0000 achieved state-of-the-art accuracy of 95.28%, an F1-Score of 95.20%, a specificity of 96.20%, a sensitivity of 93.97%, a PPV of 96.46%, an NPV of 94.17%, and an AUC of 98.42%, outperforming a comprehensive set of baselines including other ensembling approaches. \u0000<italic>Conclusions:\u0000 Our proposed HCM-Echo-VAR-Ensemble framework demonstrates significant potential for improving the sensitivity and accuracy of HCM detection in clinical settings, particularly by ensembling the complementary strengths of the SlowFast and I3D deep VAR models. This approach can enhance diagnostic consistency and accuracy, enabling reliable HCM diagnoses even in low-resource environments.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"193-201"},"PeriodicalIF":2.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in Clinical Evaluation and Regulatory Frameworks for AI-Driven Software as a Medical Device (SaMD) 人工智能驱动的软件作为医疗设备（SaMD）的临床评估和监管框架的进展

IF 2.7

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-10-23 DOI: 10.1109/OJEMB.2024.3485534

Shiau-Ru Yang;Jen-Tzung Chien;Chen-Yi Lee

{"title":"Advancements in Clinical Evaluation and Regulatory Frameworks for AI-Driven Software as a Medical Device (SaMD)","authors":"Shiau-Ru Yang;Jen-Tzung Chien;Chen-Yi Lee","doi":"10.1109/OJEMB.2024.3485534","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3485534","url":null,"abstract":"Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important transformations in healthcare, particularly in radiology. To ensure the high quality, safety, and effectiveness of AI and machine learning (ML) medical devices, the US Food and Drug Administration (FDA) has established regulatory guidelines to support the performance evaluation of medical devices. Furthermore, the FDA has proposed continuous surveillance requirements for AI/ML medical devices. This paper presents a summary of SaMD products that have passed the FDA 510 (k) AI/ML pathway, the challenges associated with the current AI/ML software-as-a-medical-device, and solutions for promoting the development of AI technologies in medicine. We hope to provide valuable information pertaining to medical-device design, development, and monitoring to ultimately achieve safer and more effective personalized medical services.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"147-151"},"PeriodicalIF":2.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10729844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control 利用带特征控制的瓦瑟斯坦生成式对抗网络生成地震心动图心音

IF 2.7

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-10-23 DOI: 10.1109/OJEMB.2024.3485535

James Skoric;Yannick D'Mello;David V. Plant

{"title":"Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control","authors":"James Skoric;Yannick D'Mello;David V. Plant","doi":"10.1109/OJEMB.2024.3485535","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3485535","url":null,"abstract":"<italic>Goal:\u0000 Seismocardiography (SCG) offers critical insights into cardiac performance, but its analysis often faces challenges due to the limited availability of data. This study aims to generate synthetic SCG heartbeats which can augment existing datasets to enable more research avenues. \u0000<italic>Methods\u0000: We trained a Wasserstein generative adversarial network (GAN) with gradient penalty on authentic SCG heartbeats. It was conditioned with embedded subject-specific identifiers to create individualized heartbeats. We employed linear permutations in the latent and conditional spaces to control signal features, and a convolutional network to classify lung volume states from real and synthetic data separately. \u0000<italic>Results\u0000: The model effectively replicated SCG signal morphology, while maintaining a level of variance which matches the variability of cardiac activity. Comparisons with real SCG waveforms yielded Pearson's r-squared correlation of 0.62 for average heartbeats. Linear manipulations were successful in controlling simple features although they were limited in more complex characteristics. Additionally, the model demonstrated strong performance in practical applications, with the synthetic data achieving an accuracy of 88% in lung volume classification as compared to 89% achieved with real data. Augmenting real data with additional synthetic data improved performance by 3%. \u0000<italic>Conclusions\u0000: GANs for artificial SCG heartbeat generation produce realistic and diverse results that have the potential to overcome data limitations, thereby enhancing SCG-based research.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"119-126"},"PeriodicalIF":2.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0