IEEE Journal of Biomedical and Health Informatics最新文献

A Graph-based Multi-dimensional Interaction Network for Drug-Drug Interaction Prediction. 基于图的药物-药物相互作用预测多维相互作用网络。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-29 DOI: 10.1109/JBHI.2025.3575012

Lejun Gong, Xinyi Wei, Hongqin Ji, Hao Huang, Yimu Ji

引用次数: 0

Glaucoformer: Dual-domain Global Transformer Network for Generalized Glaucoma Stage Classification. 青光眼成形器：用于广义青光眼分期分类的双域全局变压器网络。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-29 DOI: 10.1109/JBHI.2025.3574997

Dipankar Das, Deepak Ranjan Nayak, Ram Bilas Pachori

{"title":"Glaucoformer: Dual-domain Global Transformer Network for Generalized Glaucoma Stage Classification.","authors":"Dipankar Das, Deepak Ranjan Nayak, Ram Bilas Pachori","doi":"10.1109/JBHI.2025.3574997","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574997","url":null,"abstract":"Classification of glaucoma stages remains challenging due to substantial inter-stage similarities, the presence of irrelevant features, and subtle lesion size, shape, and color variations in fundus images. For this purpose, few efforts have recently been made using traditional machine learning and deep learning models, specifically convolutional neural networks (CNN). While the conventional CNN models capture local contextual features within fixed receptive fields, they fail to exploit global contextual dependencies. Transformers, on the other hand, are capable of modeling global contextual information. However, they lack the ability to capture local contexts and merely focus on performing attention in the spatial domain, ignoring feature analysis in the frequency domain. To address these issues, we present a novel dual-domain global transformer network, Glaucoformer, to effectively classify glaucoma stages. Specifically, we propose a dual-domain global transformer layer (DGTL) consisting of dual-domain channel attention (DCA) and dual-domain spatial attention (DSA) with Fourier domain feature analyzer (FDFA) as the core component and integrated with a backbone. This helps in exploiting local and global contextual feature dependencies in both spatial and frequency domains, thereby learning prominent and discriminant feature representations. A shared key-query scheme is introduced to learn complementary features while reducing the parameters. In addition, the DGTL leverages the benefits of a deformable convolution to enable the model to handle complex lesion irregularities. We evaluate our method on a benchmark dataset, and the experimental results and extensive comparisons with existing CNN and vision transformer-based approaches indicate its effectiveness for glaucoma stage classification. Also, the results on an unseen dataset demonstrate the generalizability of the model.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181992","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

TTFNet: Temporal-Frequency Features Fusion Network for Speech based Automatic Depression Recognition and Assessment. 基于时间-频率特征融合网络的语音抑郁自动识别与评估。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-29 DOI: 10.1109/JBHI.2025.3574864

Xiyuan Chen, Zhuhong Shao, Yinan Jiang, Runsen Chen, Yunlong Wang, Bicao Li, Mingyue Niu, Hongguang Chen, Qiang Hu, Jiasong Wu, Chunfeng Yang, Yuanyuan Shang

{"title":"TTFNet: Temporal-Frequency Features Fusion Network for Speech based Automatic Depression Recognition and Assessment.","authors":"Xiyuan Chen, Zhuhong Shao, Yinan Jiang, Runsen Chen, Yunlong Wang, Bicao Li, Mingyue Niu, Hongguang Chen, Qiang Hu, Jiasong Wu, Chunfeng Yang, Yuanyuan Shang","doi":"10.1109/JBHI.2025.3574864","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574864","url":null,"abstract":"Related studies have revealed that the phonological features of depressed patients are different from those of healthy individuals. With the increasing prevalence of depression, objective and convenient early screening is necessary. To this end, we propose an automatic depression detection method based on hybrid speech features extracted by deep learning, dubbed as TTFNet. Firstly, to effectively excavate the intrinsic relationship among multidimensional dynamic features in the frequency domain, the Mel spectrogram of raw speech and its related derivatives are encoded into quaternion representation. Then, the innovatively designed quaternion VisionLSTM is utilized to capture their synergistic effects. Simultaneously, we integrate sLSTM with the pre-trained wav2vec 2.0 model to fully acquire the temporal features. In addition, to further exploit the complementarity between temporal and frequency features, we design an XConformer block for cross-sequence interactions, which ingeniously combines self-attention mechanisms and convolutional modules. The designed XCFF fusion module, based on the XConformer block, enables multi-level interactions between frequency-domain and temporal-domain, thereby enhancing generalization ability of the proposed model. Extensive experiments conducted on the AVEC 2013, AVEC 2014, DAIC-WOZ and E-DAIC datasets demonstrate that our method outperforms current state-of-the-art methods in both depression recognition and severity prediction tasks.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181789","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

Multi-scale Spatiotemporal Dynamic Graph Neural Network for Early Prediction of Mortality Risks in Heart Failure Patients. 多尺度时空动态图神经网络对心力衰竭患者死亡风险的早期预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-28 DOI: 10.1109/JBHI.2025.3574566

Longfei Liu, Rongqin Chen, Jifu Qu, Chunli Liu, Ye Li, Dan Wu

{"title":"Multi-scale Spatiotemporal Dynamic Graph Neural Network for Early Prediction of Mortality Risks in Heart Failure Patients.","authors":"Longfei Liu, Rongqin Chen, Jifu Qu, Chunli Liu, Ye Li, Dan Wu","doi":"10.1109/JBHI.2025.3574566","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574566","url":null,"abstract":"Heart Failure (HF) stands as a principal public health issue worldwide, imposing a significant burden on healthcare systems. While existing prognostic methods have achieved certain milestones in predicting the early mortality risk of HF patients, they have not fully considered the dynamic interdependencies among physiological parameters. This paper introduces a novel Multi-scale Spatiotemporal Dynamic Graph Neural Network, MSTD-GNN, which enhances the prediction capability for early mortality in HF patients by dynamically extracting spatio-temporal information of physiological parameters from ICU patient Electronic Health Records (EHRs). Our model constructs dynamic graphs to model multivariate time series data, revealing the implicit dependencies between physiological parameters and capturing the inherent dynamics of the data. We conducted experiments using the MIMIC-III and MIMIC-IV datasets. The experimental results show that, compared to existing methods, MSTD-GNN demonstrates superior performance in predicting the early mortality risk of HF patients. On the MIMIC-III and MIMIC-IV datasets, the AUC scores of MSTD-GNN reached 83.93% and 81.74%, respectively. Furthermore, through dynamic graphs, our model unveils the dynamic relationships between physiological variables across different time scales. Code is available at https://github.com/dragonlfy/MSTDGNN-Mortality-Prediction.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173239","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

Knowledge-Driven Graph Representation Learning for Myocardial Infarction Localization. 知识驱动图表示学习在心肌梗死定位中的应用。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-28 DOI: 10.1109/JBHI.2025.3574688

Fengyi Guo, Ying An, Hulin Kuang, Jianxin Wang

{"title":"Knowledge-Driven Graph Representation Learning for Myocardial Infarction Localization.","authors":"Fengyi Guo, Ying An, Hulin Kuang, Jianxin Wang","doi":"10.1109/JBHI.2025.3574688","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574688","url":null,"abstract":"The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in assisting physicians with MI localization. Traditional MI localization methods are purely data-driven, and the quality of the data significantly affects the model's performance, particularly in the localization of rare MI. We propose a knowledgedriven graph representation learning (KD-GRL) framework which is designed to guide deep learning models in identifying key features for MI localization using prior knowledge. The MI localization knowledge graph (KG) is constructed by integrating medical knowledge about MI localization, including ECG leads and morphological manifestations, the correlations between MI localization labels, diagnostic rules, and patient demographic information. KG effectively represents the relationships among various entities, which include ECG signal entities, morphological feature entities, and demographic feature entities. The embeddings of these entities are obtained using parallel patient multi-feature extractors. Additionally, a KG aggregation method based on edge relation projection (ERP) is proposed to aggregate the relational information in the MI localization KG. Ultimately, the MI localization task is transformed into a link prediction task between patient entity and localization label entities within the KG. We conduct experiments on two public datasets, PTB and PTBXL, achieving F1-scores of 48.90% and 46.06%, respectively, both surpassing the comparison methods. Additionally, due to the incorporation of diagnostic knowledge, our method outperforms the comparison methods in localizing rare MIs.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173290","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

Modeling Brain Aging with Explainable Triamese ViT: Towards Deeper Insights into Autism Disorder. 用可解释的Triamese ViT模拟大脑衰老：对自闭症障碍的更深入了解。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-27 DOI: 10.1109/JBHI.2025.3574366

Zhaonian Zhang, Vaneet Aggarwal, Plamen Angelov, Richard Jiang

{"title":"Modeling Brain Aging with Explainable Triamese ViT: Towards Deeper Insights into Autism Disorder.","authors":"Zhaonian Zhang, Vaneet Aggarwal, Plamen Angelov, Richard Jiang","doi":"10.1109/JBHI.2025.3574366","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574366","url":null,"abstract":"Machine learning, particularly through advanced imaging techniques such as three-dimensional Magnetic Resonance Imaging (MRI), has significantly improved medical diagnostics. This is especially critical for diagnosing complex conditions like Alzheimer's disease. Our study introduces Triamese-ViT, an innovative Tri-structure of Vision Transformers (ViTs) that incorporates a built-in interpretability function, it has structure-aware explainability that allows for the identification and visualization of key features or regions contributing to the prediction, integrates information from three perspectives to enhance brain age estimation. This method not only increases accuracy but also improves interoperability with existing techniques. When evaluated, Triamese-ViT demonstrated superior performance and produced insightful attention maps. We applied these attention maps to the analysis of natural aging and the diagnosis of Autism Spectrum Disorder (ASD). The results aligned with those from occlusion analysis, identifying the Cingulum, Rolandic Operculum, Thalamus, and Vermis as important regions in normal aging, and highlighting the Thalamus and Caudate Nucleus as key regions for ASD diagnosis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158347","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

Comparing Text-Based Clinical Risk Prediction in Critical Care: A Note-Specific Hierarchical Network and Large Language Models. 比较基于文本的临床风险预测在重症监护：一个笔记特定的层次网络和大型语言模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-27 DOI: 10.1109/JBHI.2025.3574254

Jinghui Liu, Anthony Nguyen, Daniel Capurro, Karin Verspoor

{"title":"Comparing Text-Based Clinical Risk Prediction in Critical Care: A Note-Specific Hierarchical Network and Large Language Models.","authors":"Jinghui Liu, Anthony Nguyen, Daniel Capurro, Karin Verspoor","doi":"10.1109/JBHI.2025.3574254","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574254","url":null,"abstract":"Clinical predictive analysis is a crucial task with numerous applications and has been extensively studied using machine learning approaches. Clinical notes, a vital data source, have been employed to develop natural language processing (NLP) models for risk prediction in healthcare with robust performance. However, clinical notes vary considerably in text composition-written by diverse healthcare providers for different purposes-and the impact of these variations on NLP modeling is also underexplored. It also remains uncertain whether the recent Large Language Models (LLMs) with instruction-following capabilities can effectively handle the risk prediction task out-of-the-box, especially when using routinely collected clinical notes instead of polished text. We address these two important research questions in the context of in-hospital mortality prediction within the critical care setting. Specifically, we propose a supervised hierarchical network with note-specific modules to account for variations across different note categories, and provide a detailed comparison with strong supervised baselines and LLMs. We benchmark 34 instruction-following LLMs based on zero-shot, few-shot, and chain-of-thought prompting with diverse prompt templates. Our results demonstrate that the note-specific network delivers improved risk prediction performance compared to established supervised baselines from both measurement-based and text-based modeling. In contrast, LLMs consistently underperform on this critical task, despite their remarkable performances in other domains. This highlights important limitations and raises caution regarding the use of LLMs for risk assessment in the critical setting. Additionally, we show that the proposed model can be leveraged to select informative clinical notes to enhance the training of other models.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158338","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

FE-DIC-Based Motion and Intensity Correction for Enhanced CEST-MRI Registration. 基于fe - dic的增强CEST-MRI配准运动和强度校正。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-27 DOI: 10.1109/JBHI.2025.3574356

Haizhou Liu, Yijia Zheng, Zhou Liu, Yuxi Jin, Zhihua Li, Jidong Han, Ziang Di, Hairong Zheng, Dong Liang, Yin Wu, Dehong Luo, Zhanli Hu

{"title":"FE-DIC-Based Motion and Intensity Correction for Enhanced CEST-MRI Registration.","authors":"Haizhou Liu, Yijia Zheng, Zhou Liu, Yuxi Jin, Zhihua Li, Jidong Han, Ziang Di, Hairong Zheng, Dong Liang, Yin Wu, Dehong Luo, Zhanli Hu","doi":"10.1109/JBHI.2025.3574356","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574356","url":null,"abstract":"Physiological and external motion cause inter-frame misalignment in chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI), thereby compromising quantitative accuracy. In CEST-MRI, saturation effects induce intensity variations, resulting in motion-intensity coupling that makes registration particularly challenging. To address this issue, we extend the finite element digital image correlation (FE-DIC) framework by introducing an alternating correction strategy that iteratively refines both motion and intensity estimation. Unlike conventional FE-DIC approaches that assume intensity constancy, the proposed method incorporates mechanical regularization to suppress non-physical deformations, alongside intensity correction to compensate for reference-target contrast discrepancies. This mutual reinforcement enables progressively improved registration across the CEST sequence. The robustness and effectiveness of the method were evaluated on three datasets. In simulated liver data, it maintains RMSE within 0.4 pixels, reducing error by 0.5 pixels compared to RPCA&PCA (a PCA-based synthetic reference generation method for CEST registration). On clinical brain and pig cardiac data, it achieves average SSIM of 0.83, outperforming RPCA&PCA by 0.03 and surpassing CNN-based registration (e.g., AirLab) by 0.10. The consistent results across datasets highlight its generalizability, making it a promising tool for metabolic quantification in clinical and research settings.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158345","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

SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation. 用于椎体分割形状一致性的结构低秩描述符。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-27 DOI: 10.1109/JBHI.2025.3574279

Xin You, Yixin Lou, Minghui Zhang, Jie Yang, Yun Gu

{"title":"SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation.","authors":"Xin You, Yixin Lou, Minghui Zhang, Jie Yang, Yun Gu","doi":"10.1109/JBHI.2025.3574279","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3574279","url":null,"abstract":"Automatic and precise multi-class vertebrae segmentation from CT images is crucial for various clinical applications. However, due to similar appearances between adjacent vertebrae and the existence of various pathologies, existing single-stage and multi-stage methods suffer from imprecise vertebrae segmentation. Essentially, these methods fail to explicitly impose both contour precision and intra-vertebrae voxel consistency constraints synchronously, resulting in the intra-vertebrae segmentation inconsistency, which refers to multiple label predictions inside a singular vertebra. In this work, we intend to label complete binary masks with sequential indices to address that challenge. Specifically, a contour generation network is proposed based on Structural Low-Rank Descriptors for shape consistency, termed SLoRD. For a structural representation of vertebral contours, we adopt the spherical coordinate system and devise the spherical centroid to calculate contour descriptors. Due to vertebrae's similar appearances, basic contour descriptors can be acquired offline to restore original contours. Therefore, SLoRD leverages these contour priors and explicit shape constraints to facilitate regressed contour points close to vertebral surfaces. Quantitative and qualitative evaluations on VerSe 2019 and 2020 demonstrate the superior performance of our framework over other single-stage and multi-stage state-of-the-art (SOTA) methods. Further, SLoRD is a plug-and-play framework to refine the segmentation inconsistency existing in coarse predictions from other approaches. Source codes are available https://github.com/AlexYouXin/SLoRD-VerSe.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158352","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

FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease. FROG：一种具有自监督指导的细粒度时空图神经网络用于阿尔茨海默病的早期诊断。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-05-26 DOI: 10.1109/JBHI.2025.3552638

Shuoyan Zhang, Qingmin Wang, Min Wei, Jiayi Zhong, Ying Zhang, Ziyan Song, Chenyang Li, Xiaochen Zhang, Ying Han, Yunxia Li, Han Lv, Jiehui Jiang

{"title":"FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease.","authors":"Shuoyan Zhang, Qingmin Wang, Min Wei, Jiayi Zhong, Ying Zhang, Ziyan Song, Chenyang Li, Xiaochen Zhang, Ying Han, Yunxia Li, Han Lv, Jiehui Jiang","doi":"10.1109/JBHI.2025.3552638","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3552638","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) has demonstrated significant potential in the early diagnosis and study of pathological mechanisms of Alzheimer's disease (AD). To fit subtle cross-spatiotemporal interactions and learn pathological features from fMRI, we proposed a fine-grained spatiotemporal graph neural network with self-supervised learning (SSL) for diagnosis and biomarker extraction of early AD. First, considering the spatiotemporal interaction of the brain, we designed two masks that leverage the spatial correlation and temporal repeatability of fMRI. Afterwards, temporal gated inception convolution and graph scalable inception convolution were proposed for the spatiotemporal autoencoder to enhance subtle cross-spatiotemporal variation and learn noise-suppressed signals. Furthermore, a spatiotemporal scalable cosine error with high selectivity for signal reconstruction was designed in SSL to guide the autoencoder to fit the fine-grained pathological features in an unsupervised manner. A total of 5,687 samples from four cross-population cohorts were involved. The accuracy of our model was 5.1% higher than the state-of-the-art models, which included four AD diagnostic models, four SSL strategies, and three multivariate time series models. The neuroimaging biomarkers were precisely localized to the abnormal brain regions, and correlated significantly with the cognitive scale and biomarkers (P$< $0.001). Moreover, the AD progression was reflected through the mask reconstruction error of our SSL strategy. The results demonstrate that our model can effectively capture spatiotemporal and pathological features, and providing a novel and relevant framework for the early diagnosis of AD based on fMRI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150315","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