... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics最新文献

{"title":"Source-Free Active Learning for Adapting Alzheimer's Diagnostic Deep Learning Models Across Neuroimaging Cohorts.","authors":"Theofanis Ganitidis, Maria Eleftheria Vlontzou, Maria Athanasiou, Konstantina S Nikita, Christos Davatzikos","doi":"10.1109/bhi67747.2025.11269546","DOIUrl":"10.1109/bhi67747.2025.11269546","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) classification across multiple neuroimaging sites faces significant challenges due to domain shifts arising from variations in data acquisition protocols, imaging devices, and population demographics. While large-scale multi-site datasets offer unprecedented opportunities for developing robust diagnostic models, the heterogeneity between sites often leads to poor model generalization. This work proposes an uncertainty-informed active learning framework for Source-Free (SF) Domain Adaptation (DA) to classify cognitively normal individuals and AD patients across different neuroimaging studies. The proposed approach leverages Monte Carlo dropout to estimate prediction uncertainty and guide the selection of the most informative samples from the target domain for model adaptation, eliminating the need for source domain data during deployment. The framework was evaluated on a large-scale dataset comprising 3,177 participants from five neuroimaging studies (ADNI-1, ADNI-2/3, PENN, AIBL, and OASIS) with 145 regional brain volume measurements. The uncertainty-based active learning approach achieved the highest median AUC of 91.4% across all source-target combinations, outperforming baseline models (89.7%) and demonstrating superior performance compared to other SF and Source-Aware (SA) DA methods. Additionally, the distribution shifts between studies were quantified using maximum mean discrepancy to evaluate the effectiveness under variable inter-site shift. The results demonstrate that SF methods can achieve comparable or superior performance to SA approaches while addressing privacy constraints inherent in medical imaging applications.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13325197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378254","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}

{"title":"Dual-Attention BiLSTM for Interpretable Forecasting of Treatment Toxicities.","authors":"Eric Ababio Anyimadu, Xinhua Zhang, Clifton David Fuller, G Elisabeta Marai, Guadalupe Canahuate","doi":"10.1109/bhi67747.2025.11269508","DOIUrl":"10.1109/bhi67747.2025.11269508","url":null,"abstract":"<p><p>Longitudinal patient-reported outcomes (PROs) provide crucial insights into symptom progression and treatment response in oncology, enabling more personalized and anticipatory care. Deep learning models such as Bidirectional Long Short-Term Memory (Bi-LSTM) networks have recently been applied to forecast symptom trajectories from PRO data. While these models offer improved predictive performance over traditional statistical methods, they often fall short of capturing the evolving clinical relevance of individual symptoms or the varying influence of specific time points in the patient journey and lack clinical interpretability. In this work, we propose an attention-enhanced Bi-LSTM model that incorporates dual attention mechanisms at the item and temporal levels to selectively emphasize the most informative symptom-time interactions. This architecture addresses the limitation of uniform input weighting, enhancing both forecasting accuracy and interpretability. Evaluated on a longitudinal PRO dataset collected at a major cancer center, our model outperforms conventional Bi-LSTM approaches in predicting 12-month symptom severity and offers clinically meaningful insights into symptom evolution. These findings highlight the potential of attention-based temporal modeling to support personalized, timely decision-making in oncology care.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13189339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990151","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}

{"title":"Bidirectional Translation Between ECG and PCG.","authors":"Sajjad Karimi, Amit J Shah, Gari D Clifford, Reza Sameni","doi":"10.1109/bhi67747.2025.11269464","DOIUrl":"10.1109/bhi67747.2025.11269464","url":null,"abstract":"<p><p>Simultaneous electrocardiography (ECG) and phonocardiogram (PCG) offer a multimodal view of cardiac function by capturing electrical and mechanical activity, respectively. However, their shared and unique information and potential for mutual reconstruction remain poorly understood across different physiological states and individuals. This study analyzes the EPHNOGRAM dataset of simultaneous ECG-PCG recordings during rest and exercise, using linear and nonlinear models-including a non-causal neural network-to reconstruct one modality from the other. Nonlinear models, especially non-causal neural network, outperform others, with ECG reconstruction from PCG proving more feasible. In the within-subject scenario, the non-causal neural network achieved a signal-to-noise ratio (SNR) of 6.5±5.2 dB and a cross-correlation of 0.78 ± 0.19 for PCG-based ECG reconstruction. These findings provide quantitative insight into the electromechanical relationship between cardiac signals and support the development of multimodal cardiac monitoring tools.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357637","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}

{"title":"Interrelation Among the Developmental Trajectories of Brain, Cognition and Behavior During Adolescence.","authors":"Uwasila Binte Munir, Dawn M Jensen, Aidan Troha, Jingyu Liu","doi":"10.1109/bhi67747.2025.11269548","DOIUrl":"10.1109/bhi67747.2025.11269548","url":null,"abstract":"<p><p>During adolescence, the dynamic change of brain structure and function underlies the rapid maturation of cognition and behavior. However, it remains underexplored how distinct regional brain growth patterns are related to the development curves of cognition and behavior in large, longitudinal samples. To investigate that, we employed data of 2553 children from Adolescent Brain Cognitive Developmental study, including brain morphological features, cognitive and behavioral measures from ages 9-10 to 13-14 years old. Linear mixed effects model and latent growth curve analysis were first used to estimate the development trajectories of 302 brain features, 5 cognitive and 20 behavior measures. We then applied a regularized canonical correlation analysis on the resultant trajectory characteristics of brain features and those of cognition and behavior. Two pairs of significant and robust intriguing associations were identified between brain and behavior. The first pair of components highlighted the association of the increasing rate of bilateral cerebellar volume as well as cerebral white matter volume with the decreasing rate of internalizing behavior problems. Whereas, the second pair of components emphasized the relationship between crystallized intelligence and cerebral white matter volume as well as left and right ventral diencephalon volume at baseline. These findings provide us a precise understanding of how brain maturation is associated with cognition and emotional regulation.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576587","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}

{"title":"Personalized Case- and Evidence-Based TBI Prognosis with Small Language Models.","authors":"Pranav Manjunath, Syed M Adil, Benjamin D Wissel, Daniel P Sexton, Brian Lerner, Timothy W Dunn","doi":"10.1109/bhi67747.2025.11269507","DOIUrl":"10.1109/bhi67747.2025.11269507","url":null,"abstract":"<p><p>Timely and accurate emergency department disposition for traumatic brain injury patients requires rapid synthesis of complex, multimodal data. Yet in practice, such decisions often rely on heuristics, resulting in variable outcomes. While large language models show promise for supporting evidence-based practice, their clinical deployment is limited by size, cost, and privacy concerns. We present a dual retrieval-augmented framework that leverages efficient, on-premise small language models and unifies evidence-based practice with case-based reasoning to enable personalized disposition prediction of patients with traumatic brain injury. Evidence-based practice is modeled by retrieving guideline passages tailored to each patient's presentation, while case-based reasoning retrieves similar patients as few-shot exemplars. This dual-retrieval strategy personalizes both clinical guidelines and case-based exemplars, enabling the language model to produce predictions that integrate guideline alignment with patient-specific context. We implemented this framework using two open-source language models under 4B parameters-Phi-4-mini and Qwen-2.5. Across both models, similar patient exemplars consistently improved classification performance, increasing sensitivity without sacrificing specificity. Clinical guidelines had less impact on performance, but when combined with exemplars, they shifted predictions toward more conservative, guideline-consistent behavior. Clinician evaluations suggest that while adding similar patient exemplars enhances accuracy, overreliance on exemplars may diminish reasoning quality, whereas guidelines improve the clinical relevance and justification of model outputs. These findings underscore how targeted retrieval can personalize both predictions and their rationale, enhancing the performance, interpretability, and trust-worthiness of AI-assisted clinical decision-making.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13150833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870807","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}

{"title":"Integrating Neuroimaging and Genetics via Contrastive Learning for Working Memory.","authors":"Pranav Nadigapu Suresh, Behnam Kazemivash, Dawn M Jensen, Vince Calhoun, Jingyu Liu","doi":"10.1109/bhi62660.2024.10913914","DOIUrl":"10.1109/bhi62660.2024.10913914","url":null,"abstract":"<p><p>Understanding working memory's genetic and neural bases is crucial for advancing cognitive neuroscience and identifying biomarkers for cognitive impairments, particularly in the older population. This study integrates SNP and neuroimaging data from the UK biobank to improve the classification of high vs. low working memory capacity and reveal genetic factors associated with brain structure. 1060 SNPs belonging to Protein-Protein Interaction networks of amyloid precursor protein and <math><mtext>A</mtext> <mi>β</mi></math> of Alzheimer's disease were integrated with latent features of whole brain gray matter density, extracted by a pre-trained CNN, via supervised contrastive learning. Our model effectively extracts latent representations of both modalities through enhancing genetic-imaging relation within individuals and within working memory groups, in contrast to across individuals and groups. Features derived from contrastive learning outperformed other baseline models in terms of classification. Sparse canonical correlation analysis was applied to the latent representations and uncovered significantly related genetic variants and brain regions. Genetic components highlight SNPs in genes FYN, RPL28, MAPT, enriched in the pathways of dendrite and synapse, among others. The linked brain regions support the cerebellum and striatum's role in cognitive functions. These findings provide new insights into the genetic and neural mechanisms underlying working memory, potentially guiding future research and therapeutic strategies for cognitive impairment.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635230","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}

{"title":"Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population.","authors":"Frederick Xu, Duy Duong-Tran, Yize Zhao, Li Shen","doi":"10.1109/bhi62660.2024.10913835","DOIUrl":"10.1109/bhi62660.2024.10913835","url":null,"abstract":"<p><p>Neuroimaging studies have demonstrated that Alzheimer's disease (AD) is closely related to changes in neuroanatomy in the form of damage to both grey matter and white matter. However, the exact nature of AD's relationship with white matter anatomical deterioration is not fully understood at a systemic level. To investigate this knowledge gap, we constructed structural brain networks from ADNI-GO/2 diffusion tensor imaging (DTI) images with brain regions of interest (ROIs) as nodes and white matter connections as edges weighted by fiber density. The cohort consists of healthy control (HC), mild cognitive impairment (MCI), and clinically diagnosed AD subjects. By optimizing consensus modularity of structural brain networks at a subpopulation level to investigate community structure throughout a range of resolution parameters (γ), we observed a split of the reward-based decision-making module in the AD group at γ = 1.3, thus finding a 7^th consensus community in the AD consensus brain network partition that was not present in that of MCI or HC populations. Upon further investigation, we found that thalamic and caudal regions were involved in the increased segregation of AD brain networks. These regions are implicated in regulation of decision-making processes, and their segregation from other decision-making regions is a novel finding in white matter biomarker studies of AD. Our study presents novel evidence that AD may be a disconnection syndrome at the mesoscopic structural level, with potential new avenues of exploration into the role of the thalamus and caudate that may reveal neural correlates of cognitive deficits in clinically diagnosed AD.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12345622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857162","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}

{"title":"Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data.","authors":"Aditi Jaiswal, Dennis P Wall, Peter Washington","doi":"10.1109/bhi62660.2024.10913850","DOIUrl":"https://doi.org/10.1109/bhi62660.2024.10913850","url":null,"abstract":"<p><p>Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024260","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}

{"title":"RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection.","authors":"Hao Wang, Wenhui Zhu, Jiayou Qin, Xin Li, Oana Dumitrascu, Xiwen Chen, Peijie Qiu, Abolfazl Razi, Yalin Wang","doi":"10.1109/bhi62660.2024.10913865","DOIUrl":"10.1109/bhi62660.2024.10913865","url":null,"abstract":"<p><p>Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for efficient annotation. To mitigate these issues, this paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. Additionally, we offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles. Our methodology for retinal branching angle detection and calculation is detailed, followed by a benchmark analysis comparing our method with previous approaches. The results indicate that our method is robust under various conditions with high accuracy and efficiency, which offers a valuable instrument for ophthalmic research and clinical applications. The dataset and source codes are available at https://github.com/Retinal-Research/RBAD.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047221","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}

{"title":"Stratification and Survival Prediction for Amyotrophic Lateral Sclerosis Patients","authors":"Yixiao Huang, Xiaoli Wu, Rosa H. M. Chan","doi":"10.1109/bhi56158.2022.9926946","DOIUrl":"https://doi.org/10.1109/bhi56158.2022.9926946","url":null,"abstract":"","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81022323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}