Computer methods and programs in biomedicine最新文献

{"title":"Exhaustive biclustering driven by self-learning evolutionary approach for biomedical data","authors":"Adrián Segura-Ortiz ,&nbsp;Adán José-García ,&nbsp;Laetitia Jourdan ,&nbsp;José García-Nieto","doi":"10.1016/j.cmpb.2025.108846","DOIUrl":"10.1016/j.cmpb.2025.108846","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Biclustering is a key data analysis technique that identifies submatrices with coherent patterns, widely applied in biomedical fields such as gene co-expression analysis. Despite its importance, in the context of evolutionary algorithms, traditional partial representations in biclustering algorithms face significant limitations, such as redundancy and limited adaptability to domain-specific objectives. This study aims to overcome these challenges by introducing MOEBA-BIO, a new evolutionary biclustering framework for biomedical data.</div></div><div><h3>Methods:</h3><div>MOEBA-BIO is designed as a flexible framework based on the evolutionary metaheuristics scheme. It includes a self-configurator that dynamically adjusts the algorithm’s objectives and parameters based on contextual domain knowledge. The framework employs a complete representation, enabling the integration of new domain-specific objectives and the self-determination of the number of biclusters, addressing the limitations of traditional representations. The source code is available through the following git repository: <span><span>https://github.com/AdrianSeguraOrtiz/MOEBA-BIO</span><svg><path></path></svg></span>.</div></div><div><h3>Results:</h3><div>Experimental results demonstrate that MOEBA-BIO overcomes the limitations of classical partial representations. Furthermore, its application to simulated and real-world gene expression datasets highlights its ability to specialize in specific biological domains, improving accuracy and functional enrichment of biclusters compared to other state-of-the-art techniques.</div></div><div><h3>Conclusions:</h3><div>MOEBA-BIO represents a significant advancement in biclustering applied to bioinformatics. Its innovative framework, combining adaptability, self-configuration, and integration of domain-specific objectives, addresses the main limitations of traditional methods and offers robust solutions for complex biomedical datasets.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108846"},"PeriodicalIF":4.9,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185509","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}

{"title":"A bidirectional reasoning approach for blood glucose control via invertible neural networks","authors":"Jingchi Jiang ,&nbsp;Rujia Shen ,&nbsp;Yang Yang ,&nbsp;Boran Wang ,&nbsp;Yi Guan","doi":"10.1016/j.cmpb.2025.108844","DOIUrl":"10.1016/j.cmpb.2025.108844","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Despite the profound advancements that deep learning models have achieved across a multitude of domains, their propensity to learn spurious correlations significantly impedes their applicability to tasks necessitating causal and counterfactual reasoning.</div></div><div><h3>Methods:</h3><div>In this paper, we propose a Bidirectional Neural Network, which innovatively consolidates forward causal reasoning with inverse counterfactual reasoning into a cohesive framework. This integration is facilitated through the implementation of multi-stacked affine coupling layers, which ensure the network’s invertibility, thereby enabling bidirectional reasoning capabilities within a singular architectural construct. To augment the network’s trainability and to ensure the bidirectional differentiability of the parameters, we introduce an orthogonal weight normalization technique. Additionally, the counterfactual reasoning capacity of the Bidirectional Neural Network is embedded within the policy function of reinforcement learning, thereby effectively addressing the challenges associated with reward sparsity in the blood glucose control scenario.</div></div><div><h3>Results:</h3><div>We evaluate our framework on two pivotal tasks: causal-based blood glucose forecasting and counterfactual-based blood glucose control. The empirical results affirm that our model not only exemplifies enhanced generalization in causal reasoning but also significantly surpasses comparative models in handling out-of-distribution data. Furthermore, in blood glucose control tasks, the integration of counterfactual reasoning markedly improves decision efficacy, sample efficiency, and convergence velocity.</div></div><div><h3>Conclusion:</h3><div>It is our expectation that the Bidirectional Neural Network will pave novel pathways in the exploration of causal and counterfactual reasoning, thus providing groundbreaking methods for complex decision-making processes. Code is available at <span><span>https://github.com/HITshenrj/BNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108844"},"PeriodicalIF":4.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154499","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}

{"title":"Projection-based reduced order modelling for unsteady parametrized optimal control problems in 3D cardiovascular flows","authors":"Surabhi Rathore ,&nbsp;Pasquale C. Africa ,&nbsp;Francesco Ballarin ,&nbsp;Federico Pichi ,&nbsp;Michele Girfoglio ,&nbsp;Gianluigi Rozza","doi":"10.1016/j.cmpb.2025.108813","DOIUrl":"10.1016/j.cmpb.2025.108813","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Accurately defining outflow boundary conditions in patient-specific models poses significant challenges due to complex vascular morphologies, physiological conditions, and high computational demands. These challenges hinder the computation of realistic and reliable cardiovascular (CV) haemodynamics by incorporating clinical data such as 4D magnetic resonance imaging. The objective is to control the outflow boundary conditions to optimize CV haemodynamics and minimize the discrepancy between target and computed flow velocity profiles. This paper presents a projection-based reduced order modelling (ROM) framework for unsteady parametrized optimal control problems (OCP<span><math><msub><mrow></mrow><mrow><mrow><mo>(</mo><mi>μ</mi><mo>)</mo></mrow></mrow></msub></math></span>s) arising from CV applications.</div></div><div><h3>Methods:</h3><div>Numerical solutions of OCP<span><math><msub><mrow></mrow><mrow><mrow><mo>(</mo><mi>μ</mi><mo>)</mo></mrow></mrow></msub></math></span>s require substantial computational resources, highlighting the need for robust and efficient ROMs to perform real-time and many-query simulations. We investigate the performance of a projection-based reduction technique that relies on the offline-online paradigm, enabling significant computational cost savings. In this study, the fluid flow is governed by unsteady Navier–Stokes equations with physical parametric dependence, <em>i.e.</em> the Reynolds number. The Galerkin finite element method is used to compute the high-fidelity solutions in the offline phase. We implemented a nested-proper orthogonal decomposition (<em>nested-POD</em>) for fast simulation of OCP<span><math><msub><mrow></mrow><mrow><mrow><mo>(</mo><mi>μ</mi><mo>)</mo></mrow></mrow></msub></math></span>s that encompasses two stages: temporal compression for reducing dimensionality in time, followed by parametric-space compression on the precomputed POD modes.</div></div><div><h3>Results:</h3><div>We tested the efficacy of the proposed methodology on vascular models, namely an idealized bifurcation geometry and a patient-specific coronary artery bypass graft, incorporating stress control at the outflow boundary and observing consistent speed-up with respect to high-fidelity strategies. We observed the inter-dependency between the state, adjoint, and control solutions and presented detailed flow field characteristics, providing valuable insights into factors such as atherosclerosis risk.</div></div><div><h3>Conclusion:</h3><div>The projection-based ROM framework provides an efficient and accurate approach for simulating parametrized CV flows. By enabling real-time, patient-specific modelling, this advancement supports personalized medical interventions and improves the predictions of disease progression in vascular regions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108813"},"PeriodicalIF":4.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154498","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}

{"title":"Cross-Fusion Adaptive Feature Enhancement Transformer: Efficient high-frequency integration and sparse attention enhancement for brain MRI super-resolution","authors":"Zhiying Yang ,&nbsp;Hanguang Xiao ,&nbsp;Xinyi Wang ,&nbsp;Feizhong Zhou ,&nbsp;Tianhao Deng ,&nbsp;Shihong Liu","doi":"10.1016/j.cmpb.2025.108815","DOIUrl":"10.1016/j.cmpb.2025.108815","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>High-resolution magnetic resonance imaging (MRI) is essential for diagnosing and treating brain diseases. Transformer-based approaches demonstrate strong potential in MRI super-resolution by capturing long-range dependencies effectively. However, existing Transformer-based super-resolution methods face several challenges: (1) they primarily focus on low-frequency information, neglecting the utilization of high-frequency information; (2) they lack effective mechanisms to integrate both low-frequency and high-frequency information; (3) they struggle to effectively eliminate redundant information during the reconstruction process. To address these issues, we propose the Cross-fusion Adaptive Feature Enhancement Transformer (CAFET).</div></div><div><h3>Methods:</h3><div>Our model maximizes the potential of both CNNs and Transformers. It consists of four key blocks: a high-frequency enhancement block for extracting high-frequency information; a hybrid attention block for capturing global information and local fitting, which includes channel attention and shifted rectangular window attention; a large-window fusion attention block for integrating local high-frequency features and global low-frequency features; and an adaptive sparse overlapping attention block for dynamically retaining key information and enhancing the aggregation of cross-window features.</div></div><div><h3>Results:</h3><div>Extensive experiments validate the effectiveness of the proposed method. On the BraTS and IXI datasets, with an upsampling factor of <span><math><mrow><mo>×</mo><mn>2</mn></mrow></math></span>, the proposed method achieves a maximum PSNR improvement of 2.4 dB and 1.3 dB compared to state-of-the-art methods, along with an SSIM improvement of up to 0.16% and 1.42%. Similarly, at an upsampling factor of <span><math><mrow><mo>×</mo><mn>4</mn></mrow></math></span>, the proposed method achieves a maximum PSNR improvement of 1.04 dB and 0.3 dB over the current leading methods, along with an SSIM improvement of up to 0.25% and 1.66%.</div></div><div><h3>Conclusions:</h3><div>Our method is capable of reconstructing high-quality super-resolution brain MRI images, demonstrating significant clinical potential.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"268 ","pages":"Article 108815"},"PeriodicalIF":4.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134793","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}

{"title":"Towards the use of multiple ROIs for radiomics-based survival modelling: Finding a strategy of aggregating lesions","authors":"Agata Małgorzata Wilk ,&nbsp;Andrzej Swierniak ,&nbsp;Andrea d’Amico ,&nbsp;Rafał Suwiński ,&nbsp;Krzysztof Fujarewicz ,&nbsp;Damian Borys","doi":"10.1016/j.cmpb.2025.108840","DOIUrl":"10.1016/j.cmpb.2025.108840","url":null,"abstract":"<div><h3>Background:</h3><div>Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients — such as the primary tumour and peritumoral area or subregions of the tumour. These can be straightforwardly incorporated into models as additional features. However, a more complex scenario arises, for example, in a regionally disseminated disease, when multiple distinct lesions are present.</div></div><div><h3>Aim:</h3><div>This study aims to evaluate the feasibility of integrating radiomic data from multiple lesions into survival models. We explore strategies for incorporating these ROIs and hypothesize that including all available lesions can improve model performance.</div></div><div><h3>Methods:</h3><div>While each lesion produces a feature vector, the desired result is a unified prediction. We propose methods to aggregate either the feature vectors to form a representative one or the modelling results to compute a consolidated risk score. As a proof of concept, we apply these strategies to predict distant metastasis risk in a cohort of 115 non-small cell lung cancer patients, 60% of whom exhibit regionally advanced disease. Two feature sets (radiomics extracted from PET and PET interpolated to CT resolution) are tested across various survival models using a Monte Carlo Cross-Validation framework.</div></div><div><h3>Results:</h3><div>Across both feature sets, incorporating all available lesions — rather than limiting analysis to the primary tumour — consistently improved the c-index, irrespective of the survival model used. The highest c-Index obtained by a primary tumour-only model was 0.611 for the PET dataset and 0.614 for the PET_CT dataset, while by using all lesions we were able to achieve c-Indices of 0.632 and 0.634.</div></div><div><h3>Conclusion:</h3><div>Lesions beyond the primary tumour carry information that should be utilized in radiomics-based models to enhance predictive ability.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108840"},"PeriodicalIF":4.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185510","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}

{"title":"Label knowledge guided transformer for automatic radiology report generation","authors":"Rui Wang,&nbsp;Jianguo Liang","doi":"10.1016/j.cmpb.2025.108877","DOIUrl":"10.1016/j.cmpb.2025.108877","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The task of automatically generating radiology reports is a key research area at the intersection of computer science and medicine, aiming to enable computers to generate corresponding reports on the basis of radiology images. This field currently faces a significant data bias issue, which causes words describing diseases to be overshadowed by words describing normal regions in the reports.</div></div><div><h3>Methods</h3><div>To address this, we propose the label knowledge guided transformer model for generating radiology reports. Specifically, our model incorporates a Multi Feature Extraction module and a Dual-branch Collaborative Attention module. The Multi Feature Extraction module leverages medical knowledge graphs and feature clustering algorithms to optimize the label feature extraction process from both the prediction and encoding of label information, making it the first module specifically designed to reduce redundant label features. The Dual-branch Collaborative Attention module uses two parallel attention mechanisms to simultaneously compute visual features and label features, and prevents the direct integration of label features into visual features, thereby effectively balancing the model's attention between label features and visual features.</div></div><div><h3>Results</h3><div>We conduct experimental tests using the IU X-Ray and MIMIC-CXR datasets under six natural language generation evaluation metrics and analyze the results. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance. Compared with the baseline models, the label knowledge guided transformer achieves an average improvement of 23.3% on the IU X-Ray dataset and 20.7% on the MIMIC-CXR dataset.</div></div><div><h3>Conclusion</h3><div>Our model has strong capabilities in capturing abnormal features, effectively mitigating the adverse effects caused by data bias, and demonstrates significant potential to enhance the quality and accuracy of automatically generated radiology reports.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108877"},"PeriodicalIF":4.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167103","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}

{"title":"Visual-language foundation models in medical imaging: A systematic review and meta-analysis of diagnostic and analytical applications","authors":"Yiyao Sun ,&nbsp;Xinran Wen ,&nbsp;Yan Zhang ,&nbsp;Lijun Jin ,&nbsp;Chunna Yang ,&nbsp;Qianhui Zhang ,&nbsp;Mingchen Jiang ,&nbsp;Zhaoyang Xu ,&nbsp;Wei Guo ,&nbsp;Juan Su ,&nbsp;Xiran Jiang","doi":"10.1016/j.cmpb.2025.108870","DOIUrl":"10.1016/j.cmpb.2025.108870","url":null,"abstract":"<div><h3>Background and objective</h3><div>Visual-language foundation models (VLMs) have garnered attention for their numerous advantages and significant potential in AI-aided diagnosis and treatment, driving widespread applications in medical tasks. This study analyzes and summarizes the value and prospects of VLMs, highlighting their groundbreaking opportunities in healthcare.</div></div><div><h3>Methods</h3><div>This systematic review and meta-analysis, registered with PROSPERO (CRD42024575746), included studies from PubMed, Embase, Web of Science, and IEEE from inception to December 31, 2024. The inclusion criteria covered state-of-the-art VLM developments and applications in medical imaging. Metrics such as AUC, Dice coefficient, BLEU score, and Accuracy were pooled for tasks like classification, segmentation, report generation, and Visual Question Answering (VQA). Reporting quality and bias were assessed using the QUADAS-AI checklist.</div></div><div><h3>Results</h3><div>A total of 106 eligible studies were identified for this systematic review, of which 94 were included for meta-analysis. The pooled AUC for downstream classification tasks was 0.86 (0.85–0.87); pooled Dice coefficient for segmentation tasks was 0.73 (0.68–0.78); pooled BLEU score for report generation tasks was 0.31 (0.20–0.43); and pooled Acc score for VQA was 0.76 (0.71–0.81). Subgroup analyses were stratified by imaging modalities (radiological, pathological and surface imaging) and publication year (before or after 2023) to explore the heterogeneity within VLM research and to analyze diagnostic performance of the VLMs under different conditions.</div></div><div><h3>Conclusions</h3><div>VLMs based on medical imaging have demonstrated strong performance and significant potential in computer-assisted clinical diagnosis. Stricter reporting standards addressing the unique challenges of VLM research could enhance study quality.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"268 ","pages":"Article 108870"},"PeriodicalIF":4.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139618","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}

{"title":"FOTELP-VOX-OA: Enhancing radiotherapy planning precision with particle transport simulations and Optimization Algorithms","authors":"Milena Zivkovic ,&nbsp;Filip Andric ,&nbsp;Marina Svicevic ,&nbsp;Dragana Krstic ,&nbsp;Lazar Krstic ,&nbsp;Bogdan Pirkovic ,&nbsp;Tatjana Miladinovic ,&nbsp;Mohamed El Amin Aichouche","doi":"10.1016/j.cmpb.2025.108838","DOIUrl":"10.1016/j.cmpb.2025.108838","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Accurate tumor targeting with minimal exposure to healthy tissue remains a significant challenge in radiotherapy. Modern techniques like Intensity-Modulated Radiation Therapy and stereotactic radiotherapy increasingly rely on detailed simulations and planning to achieve maximum treatment efficiency. Particle transport simulations play a crucial role in accurately modeling interactions between radiation and biological structures, providing a foundation for advancements in treatment planning. Building on this, FOTELP-VOX-OA is introduced as a novel framework designed to determine the optimal external-beam radiotherapy treatment plan. The primary aim of this study is to integrate the existing FOTELP-VOX framework with various Optimization Algorithms, focusing on estimating the parameters of interest that lead to the optimal radiation dose. While the framework itself is not pathology-specific, ocular melanoma is chosen as a test case due to its requirement for exceptionally precise dose delivery, given the small tumor volume and proximity of critical ocular structures.</div></div><div><h3>Methods:</h3><div>Particle transport simulations were conducted with FOTELP-VOX software, enabling detailed dose distribution analysis in tissues. Simulated conditions included a detailed biological model of eye melanoma to closely mimic clinical scenarios. The study integrates advanced optimization algorithms, such as Random Search, Tree-structured Parzen Estimator, and Genetic Algorithm, into the FOTELP-VOX framework, creating FOTELP-VOX-OA, to achieve the optimal treatment plan. Additionally, a specialized metric named Total Error was developed to determine the efficiency of the proposed treatment plan, focusing on both the desired tumor dose and minimizing exposure to surrounding tissues.</div></div><div><h3>Results:</h3><div>In the presented case-study, FOTELP-VOX-OA, utilizing the Genetic Algorithm, achieved a Total Error of 1701.52, significantly improving treatment planning compared to a human expert. However, this approach required the longest computation time among all methods. In contrast, the Tree-structured Parzen Estimator within the FOTELP-VOX-OA framework provided a balanced trade-off between speed and accuracy, while the Random Search-based solution was the fastest but also the least accurate.</div></div><div><h3>Conclusion:</h3><div>The FOTELP-VOX-OA framework improves radiotherapy precision, reduces risks to surrounding healthy tissues, and achieves better treatment outcomes. This approach demonstrates how particle transport simulations, coupled with optimization techniques, can address critical challenges in radiotherapy planning, paving the way for future applications in other tumor sites and clinical contexts.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"268 ","pages":"Article 108838"},"PeriodicalIF":4.9,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099306","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}

{"title":"Investigating the interpretability of ChatGPT in mental health counseling: An analysis of artificial intelligence generated content differentiation","authors":"Yang Liu,&nbsp;Fan Wang","doi":"10.1016/j.cmpb.2025.108864","DOIUrl":"10.1016/j.cmpb.2025.108864","url":null,"abstract":"<div><div>The global impact of COVID-19 has caused a significant rise in the demand for psychological counseling services, creating pressure on existing mental health professionals. Large language models (LLM), like ChatGPT, are considered a novel solution for delivering online psychological counseling. However, performance evaluation, emotional expression, high levels of anthropomorphism, ethical issues, transparency, and privacy breaches need to be addressed before LLM can be widely adopted.</div><div>This study aimed to evaluate ChatGPT’s effectiveness and emotional support capabilities in providing mental health counseling services from both macro and micro perspectives to examine whether it possesses psychological support abilities comparable to those of human experts. Building on the macro-level evaluation, we conducted a deeper comparison of the linguistic differences between ChatGPT and human experts at the micro-level. In addition, to respond to current policy requirements regarding the labeling, we further explored how to identify artificial intelligence generated content (AIGC) in counseling texts and which micro-level linguistic features can effectively distinguish AIGC from user-generated content (UGC). Finally, the study addressed transparency, privacy breaches, and ethical concerns.</div><div>We utilized ChatGPT for psychological interventions, applying LLM to address various mental health issues. The BERTopic algorithm evaluated the content across multiple mental health problems. Deep learning techniques were employed to differentiate between AIGC and UGC in psychological counseling responses. Furthermore, Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) evaluate interpretability, providing deeper insights into the decision-making process and enhancing transparency.</div><div>At the macro level, ChatGPT demonstrated performance comparable to human experts, exhibiting professionalism, diversity, empathy, and a high degree of human likeness, making it highly effective in counseling services. At the micro level, deep learning models achieved accuracy rates of 99.12 % and 96.13 % in distinguishing content generated by ChatGPT 3.5 and ChatGPT 4.0 from UGC, respectively. Interpretability analysis revealed that context, sentence structure, and emotional expression were key factors differentiating AIGC from UGC.</div><div>The findings highlight ChatGPT's potential to deliver effective online psychological counseling and demonstrate a reliable framework for distinguishing between artificial intelligence-generated and human-generated content. This study underscores the importance of leveraging large-scale language models to support mental health services while addressing high-level anthropomorphic issues and ethical and practical challenges.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"268 ","pages":"Article 108864"},"PeriodicalIF":4.9,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139531","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}

{"title":"Validation of CTA-based closed-loop coronary artery flow simulations against intravascular Doppler velocity and pressure measurements","authors":"Anahita Seresti ,&nbsp;Alison L. Marsden ,&nbsp;Andrew M. Kahn ,&nbsp;Ryan R. Reeves ,&nbsp;Ehtisham Mahmud ,&nbsp;Belal Al Khiami ,&nbsp;Lawrence Ang ,&nbsp;M․Owais Khan","doi":"10.1016/j.cmpb.2025.108868","DOIUrl":"10.1016/j.cmpb.2025.108868","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>The modeling assumptions involved in computational fluid dynamics (CFD) simulations of coronary arteries using coronary computed angiography (CTA) have not been thoroughly validated. These modeling assumptions can lead to uncertainties in simulated velocities and pressure, and consequently, other hemodynamic markers, such as wall shear stresses. In this study, we validated a state-of-the-art coronary CTA-CFD simulation strategy against intravascular Doppler velocity and pressure measurements.</div></div><div><h3>Methods</h3><div>3D coronary models were reconstructed using coronary CTA in 13 patients. Intravascular Doppler velocities and pressures were obtained in 18 arteries over 120 ± 55 cardiac cycles to validate CTA-CFD simulations. A lumped parameter network (LPN) was tuned to capture each patient’s heart and distal coronary circulation, and coupled to the CFD solver. For each patient, Murray’s Law coefficient was varied from 2.0 to 3.0 in increments of 0.2. The simulated velocities and pressures were compared to intravascular measurements.</div></div><div><h3>Results</h3><div>The correlation between intravascular and CTA-CFD parameters showed no statistically significant correlation for velocity (<em>r</em>=-0.13 and <em>p</em> = 0.60), while both flow rates (<em>r</em> = 0.77, <em>p</em> &lt; 0.01) and pressures (<em>r</em> = 0.88, <em>p</em> &lt; 0.01) demonstrated strong correlation and statistical significance. When considering the intra-patient cycle-to-cycle variabilities in invasive measurements, velocity in 11 of 18 and pressures in 7 of 18 coronary arteries were within one standard deviation of intravascular measurement variability.</div></div><div><h3>Conclusion</h3><div>CTA-CFD simulations showed statistically significant correlations for intravascular flows and pressures, whereas no meaningful correlation was observed for velocity. These findings highlight the influence of measurement variability and modeling assumptions. Future studies should consider the inherent uncertainties in CTA-CFD, especially when estimating absolute hemodynamic parameters such as velocity and pressure, and aim to refine boundary conditions and validation strategies to improve accuracy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"268 ","pages":"Article 108868"},"PeriodicalIF":4.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139619","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}