IEEE Journal of Biomedical and Health Informatics最新文献_第2页

Self-supervised Multi-scale Multi-modal Graph Pool Transformer for Sellar Region Tumor Diagnosis. 用于塞拉区域肿瘤诊断的自监督多尺度多模态图池变换器

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496700

Baiying Lei, Gege Cai, Yun Zhu, Tianfu Wang, Lei Dong, Cheng Zhao, Xinzhi Hu, Huijun Zhu, Lin Lu, Feng Feng, Ming Feng, Renzhi Wang

{"title":"Self-supervised Multi-scale Multi-modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.","authors":"Baiying Lei, Gege Cai, Yun Zhu, Tianfu Wang, Lei Dong, Cheng Zhao, Xinzhi Hu, Huijun Zhu, Lin Lu, Feng Feng, Ming Feng, Renzhi Wang","doi":"10.1109/JBHI.2024.3496700","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496700","url":null,"abstract":"The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pa-tients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies. BloodPatrol：彻底改变血癌诊断--利用深度学习和云技术进行先进的实时检测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496294

Jinhang Wei, Longyue Wang, Zhecheng Zhou, Linlin Zhuo, Xiangxiang Zeng, Xiangzheng Fu, Quan Zou, Keqin Li, Zhongjun Zhou

{"title":"BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies.","authors":"Jinhang Wei, Longyue Wang, Zhecheng Zhou, Linlin Zhuo, Xiangxiang Zeng, Xiangzheng Fu, Quan Zou, Keqin Li, Zhongjun Zhou","doi":"10.1109/JBHI.2024.3496294","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496294","url":null,"abstract":"Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer. However, the high computation and resource consumption severely limit its practical application, especifically in regions with limited medical and computational resources. In this study, with the help of cloud computing and IoT technologies, we develop a novel blood cancer dynamic monitoring diagnostic model named BloodPatrol based on an intelligent feature weight fusion mechanism. The proposed model is capable of capturing the dual-view importance relationship between cell samples and features, greatly improving prediction accuracy and significantly surpassing previous models. Besides, benefiting from the powerful processing ability of cloud computing, BloodPatrol can run on a distributed network to efficiently process large-scale cell data, which provides immediate and scalable blood cancer diagnostic services. We have also created a cloud diagnostic platform to facilitate access to our work, the latest access link and updates are available at: https://github.com/kkkayle/BloodPatrol.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis. SleepECG-Net：利用心电图诊断小儿睡眠呼吸暂停的可解释深度学习方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3495975

Clara Garcia-Vicente, Gonzalo C Gutierrez-Tobal, Fernando Vaquerizo-Villar, Adrian Martin-Montero, David Gozal, Roberto Hornero

{"title":"SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis.","authors":"Clara Garcia-Vicente, Gonzalo C Gutierrez-Tobal, Fernando Vaquerizo-Villar, Adrian Martin-Montero, David Gozal, Roberto Hornero","doi":"10.1109/JBHI.2024.3495975","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495975","url":null,"abstract":"Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability. Our study introduces a novel interpretable DL approach (SleepECG-Net) for directly estimating OSA severity in at-risk children. A combination of convolutional and recurrent neural networks (CNN-RNN) was trained on overnight electrocardiogram (ECG) signals. Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable Artificial Intelligence (XAI) algorithm, was applied to explain model decisions and extract ECG patterns relevant to pediatric OSA. Accordingly, ECG signals from the semi-public Childhood Adenotonsillectomy Trial (CHAT, n = 1610) and Cleveland Family Study (CFS, n = 64), and the private University of Chicago (UofC, n = 981) databases were used. OSA diagnostic performance reached 4-class Cohen's Kappa of 0.410, 0.335, and 0.249 in CHAT, UofC, and CFS, respectively. The proposal demonstrated improved performance with increased severity along with heightened cardiovascular risk. XAI findings highlighted the detection of established ECG features linked to OSA, such as bradycardia-tachycardia events and delayed ECG patterns during apnea/hypopnea occurrences, focusing on clusters of events. Furthermore, Grad-CAM heatmaps identified potential ECG patterns indicating cardiovascular risk, such as P, T, and U waves, QT intervals, and QRS complex variations. Hence, SleepECG-Net approach may improve pediatric OSA diagnosis by also offering cardiac risk factor information, thereby increasing clinician confidence in automated systems, and promoting their effective adoption in clinical practice.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biomedical Information Integration via Adaptive Large Language Model Construction. 通过自适应大型语言模型构建实现生物医学信息整合。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496495

Xingsi Xue, Mu-En Wu, Fazlullah Khan

{"title":"Biomedical Information Integration via Adaptive Large Language Model Construction.","authors":"Xingsi Xue, Mu-En Wu, Fazlullah Khan","doi":"10.1109/JBHI.2024.3496495","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496495","url":null,"abstract":"Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment. 用于开放手术缝合技能评估的手部旋转功能数据分析。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496122

Amir Mehdi Shayan, David B Hitchcock, Simar Singh, Jianxin Gao, Richard E Groff, Ravikiran B Singapogu

{"title":"Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment.","authors":"Amir Mehdi Shayan, David B Hitchcock, Simar Singh, Jianxin Gao, Richard E Groff, Ravikiran B Singapogu","doi":"10.1109/JBHI.2024.3496122","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496122","url":null,"abstract":"This study explores the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on hand roll profiles from 96 subjects, categorized into advanced surgeons, trainee surgeons, and novices. Functional k-means, using dynamic time-warping to align curves, were used to partition the data into two preset numbers of clusters (3 and 6). Both clustering models (3-cluster and 6-cluster) effectively clustered performance into groups with distinct characteristics and levels of skill (evident from visual inspection of cluster centroids). The relationship between cluster membership and suturing skills was corroborated using proxy measures of skill: expert global rating scale ratings, clinical status and expertise, and simulator-derived metrics. The findings of this study offer valuable insight into essential components of suturing skill and can improve the autonomy and efficiency of simulation-based suturing training. The clinical relevance of our results is immediately pertinent to the field of surgical skill assessment, where FDA-based methods could potentially be employed for objective feedback and training.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks. 利用黎曼几何网络对运动意象脑电图进行多级分类的框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496757

Yuxuan Shi, Aimin Jiang, Ju Zhong, Min Li, Yanping Zhu

{"title":"Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.","authors":"Yuxuan Shi, Aimin Jiang, Ju Zhong, Min Li, Yanping Zhu","doi":"10.1109/JBHI.2024.3496757","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496757","url":null,"abstract":"In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Video-Based Hand Taxonomy Using Egocentric Video in the Wild. 利用野外以自我为中心的视频进行基于视频的个性化手分类学研究

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3495699

Mehdy Dousty, David J Fleet, Jose Zariffa

{"title":"Personalized Video-Based Hand Taxonomy Using Egocentric Video in the Wild.","authors":"Mehdy Dousty, David J Fleet, Jose Zariffa","doi":"10.1109/JBHI.2024.3495699","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495699","url":null,"abstract":"Objective: Hand function is central to inter- actions with our environment. Developing a comprehen- sive model of hand grasps in naturalistic environments is crucial across various disciplines, including robotics, ergonomics, and rehabilitation. Creating such a taxonomy poses challenges due to the significant variation in grasp- ing strategies that individuals may employ. For instance, individuals with impaired hands, such as those with spinal cord injuries (SCI), may develop unique grasps not used by unimpaired individuals. These grasping techniques may differ from person to person, influenced by variable senso- rimotor impairment, creating a need for personalized meth- ods of analysis.Method: This study aimed to automatically identify the dominant distinct hand grasps for each indi- vidual without reliance on a priori taxonomies, by applying semantic clustering to egocentric video. Egocentric video recordings collected in the homes of 19 individual with cervical SCI were used to cluster grasping actions with semantic significance. A deep learning model integrating posture and appearance data was employed to create a per- sonalized hand taxonomy.Results: Quantitative analysis reveals a cluster purity of 67.6% ± 24.2% with 18.0% ± 21.8% redundancy. Qualitative assessment revealed meaningful clusters in video content.Discussion: This methodology provides a flexible and effective strategy to analyze hand function in the wild, with applications in clinical assess- ment and in-depth characterization of human-environment interactions in a variety of contexts.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models. 利用现成的扩散模型实现医疗词组的零点接地。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-08 DOI: 10.1109/JBHI.2024.3494246

Konstantinos Vilouras, Pedro Sanchez, Alison Q O'Neil, Sotirios A Tsaftaris

{"title":"Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models.","authors":"Konstantinos Vilouras, Pedro Sanchez, Alison Q O'Neil, Sotirios A Tsaftaris","doi":"10.1109/JBHI.2024.3494246","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3494246","url":null,"abstract":"Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task of performing localization with textual guidance is commonly referred to as phrase grounding. In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to perform this challenging task. This choice is supported by the fact that the Latent Diffusion Model, despite being generative in nature, contains cross-attention mechanisms that implicitly align visual and textual features, thus leading to intermediate representations that are suitable for the task at hand. In addition, we aim to perform this task in a zero-shot manner, i.e., without any training on the target task, meaning that the model's weights remain frozen. To this end, we devise strategies to select features and also refine them via post-processing without extra learnable parameters. We compare our proposed method with state-of-the-art approaches which explicitly enforce image-text alignment in a joint embedding space via contrastive learning. Results on a popular chest X-ray benchmark indicate that our method is competitive with SOTA on different types of pathology, and even outperforms them on average in terms of two metrics (mean IoU and AUC-ROC). Source code will be released upon acceptance at https://github.com/vios-s.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing In Silico Clinical Trials for Regulatory Adoption and Innovation. 推进硅学临床试验，促进法规采纳和创新。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-08 DOI: 10.1109/JBHI.2024.3486538

Georgia Karanasiou, Elazer Edelman, Francois-Henri Boissel, Robert Byrne, Luca Emili, Martin Fawdry, Nenad Filipovic, David Flynn, Liesbet Geris, Alfons Hoekstra, Maria Cristina Jori, Ali Kiapour, Dejan Krsmanovic, Thierry Marchal, Flora Musuamba, Francesco Pappalardo, Lorenza Petrini, Markus Reiterer, Marco Viceconti, Klaus Zeier, Lampros K Michalis, Dimitrios I Fotiadis

{"title":"Advancing In Silico Clinical Trials for Regulatory Adoption and Innovation.","authors":"Georgia Karanasiou, Elazer Edelman, Francois-Henri Boissel, Robert Byrne, Luca Emili, Martin Fawdry, Nenad Filipovic, David Flynn, Liesbet Geris, Alfons Hoekstra, Maria Cristina Jori, Ali Kiapour, Dejan Krsmanovic, Thierry Marchal, Flora Musuamba, Francesco Pappalardo, Lorenza Petrini, Markus Reiterer, Marco Viceconti, Klaus Zeier, Lampros K Michalis, Dimitrios I Fotiadis","doi":"10.1109/JBHI.2024.3486538","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3486538","url":null,"abstract":"The evolution of information and communication technologies has affected all fields of science, including health sciences. However, the rate of technological innovation adoption by the healthcare sector has been historically slow, compared to other industrial sectors. Innovation in computer modeling and simulation approaches has changed the landscape in biomedical applications and biomedicine, paving the way for their potential contribution in reducing, refining, and partially replacing animal and human clinical trials. In Silico Clinical Trials (ISCT) allow the development of virtual populations used in the safety and efficacy testing of new drugs and medical devices. This White Paper presents the current framework for ISCT, the role of in silico medicine research communities, the different perspectives (research, scientific, clinical, regulatory, standardization, data quality, legal and ethical), the barriers, challenges, and opportunities for ISCT adoption. In addition, an overview of successful ISCT projects, market-available platforms, and FDA- approved paradigms, along with their vision, mission and outcomes are presented.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PointCHD: A Point Cloud Benchmark for Congenital Heart Disease Classification and Segmentation. PointCHD：用于先天性心脏病分类和分割的点云基准。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-08 DOI: 10.1109/JBHI.2024.3495035

Dinghao Yang, Wei Gao

{"title":"PointCHD: A Point Cloud Benchmark for Congenital Heart Disease Classification and Segmentation.","authors":"Dinghao Yang, Wei Gao","doi":"10.1109/JBHI.2024.3495035","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495035","url":null,"abstract":"Congenital heart disease (CHD) is one of the most common birth defects. With the development of medical imaging analysis technology, medical image analysis for CHD has become an important research direction. Due to the lack of data and the difficulty of labeling, CHD datasets are scarce. Previous studies focused on CT and other medical image modes, while point cloud is still unstudied. As a representative type of 3D data, point cloud can intuitively model organ shapes, which has obvious advantages in medical analysis and can assist doctors in diagnosis. However, the production of a medical point cloud dataset is more complex than that of an image dataset, and the 3D modeling of internal organs needs to be reconstructed after scanning by high-precision instruments. We propose PointCHD, the first point cloud dataset for CHD diagnosis, with a large number of high precision-annotated and wide-categorized data. PointCHD includes different types of three-dimensional data with varying degrees of distortion, and supports multiple analysis tasks, i.e. classification, segmentation, reconstruction, etc. We also construct a benchmark on PointCHD with the goal of medical diagnosis, we design the analysis process and compare the performances of the mainstream point cloud analysis methods. In view of the complex internal and external structure of the heart point cloud, we propose a point cloud representation learning method based on manifold learning. By introducing normal lines to consider the continuity of the surface to construct a manifold learning method of the adaptive projection plane, fully extracted the structural features of the heart, and achieved the best performance on each task of the PointCHD benchmark. Finally, we summarize the existing problems in the analysis of the CHD point cloud and prospects for potential research directions in the future. The benchmark will be released soon.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0