Computers in biology and medicine最新文献

{"title":"Influences of mitral valve shape on transmitral hemodynamics before and after edge-to-edge repair: development of a reduced-order model","authors":"Juliana Franz ,&nbsp;Fabian Barbieri ,&nbsp;Marco Barink ,&nbsp;Alexandra Groth ,&nbsp;Markus Reinthaler ,&nbsp;Mario Kasner ,&nbsp;Jan Brüning ,&nbsp;Valentina Lavezzo ,&nbsp;Irina Waechter-Stehle ,&nbsp;Ulf Landmesser ,&nbsp;Titus Kuehne ,&nbsp;Leonid Goubergrits ,&nbsp;Katharina Vellguth","doi":"10.1016/j.compbiomed.2025.110838","DOIUrl":"10.1016/j.compbiomed.2025.110838","url":null,"abstract":"<div><div>Transcatheter edge-to-edge repair (TEER) is an effective treatment for mitral valve regurgitation in patients with high surgical risk, but predicting the hemodynamic outcomes is challenging. Reduced-order models (ROMs) show promise for post-TEER hemodynamic outcome predictions, but personalization of the ROM equations is essential for accurate simulations of mitral valve blood flow rates and pressure gradients during both diastole and systolic regurgitation. While the mitral valve orifice area is a common parameter used for ROM personalization, other aspects of the mitral valve shape are usually not considered. In this in-silico study, we investigated the influences of mitral valve shape on transmitral hemodynamics using a combination of computational fluid dynamics (CFD) simulations and geometrical analyses. Mitral valves from ten TEER patients were analyzed at three valve states: early diastole (pre- and post-TEER) and systolic regurgitation (pre-TEER). The orifice-to-annulus area ratio and the orifice orientation were identified as key shape parameters impacting mitral valve hemodynamics. Based on these findings, we developed shape-based ROM equations that are personalized using routine echocardiographic data. The ROM estimates agreed well with CFD simulation results (mean relative differences &lt;1 % and limits of agreements &lt;13 % for both flow rates and pressure gradients). Application of the ROM equations to patient-specific data revealed distinct hemodynamic differences between the three valve states, aligning with expectations from both physiological and fluid dynamics perspectives. Our results suggest that incorporating mitral valve shape parameters into ROMs could improve the accuracy of patient-specific simulations, thus enhancing their potential for supporting TEER planning and predicting intervention outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110838"},"PeriodicalIF":6.3,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757840","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":"Multi-omics analysis of plasma and CSF in spontaneous diabetic cynomolgus monkeys: Unravelling and validating the key molecular markers that predict the preclinical pathological formation of Alzheimer's disease","authors":"Xinxin Huang ,&nbsp;Xu Zhu ,&nbsp;Fangyan Fu ,&nbsp;Junzhen Song ,&nbsp;Jiyu Zeng ,&nbsp;Shanshan Huang ,&nbsp;Feng Yue","doi":"10.1016/j.compbiomed.2025.110849","DOIUrl":"10.1016/j.compbiomed.2025.110849","url":null,"abstract":"<div><div>Alzheimer's disease (AD) biomarkers (Aβ42 or Tau 181) have high diagnostic performance. However, when they are altered, it indicates that irreversible pathology has developed in the brain. Therefore, there is a lack of early prediction or monitoring of AD biomarkers. Here, we used the spontaneous type 2 diabetes mellitus (T2DM) monkey as the preclinical stage of AD. Two different methods were used to screen molecules associated with AD biomarker changes and construct molecular interaction networks. And key molecules were screened using nodes in the molecular network as input for machine learning (ML). The results showed that our predictive models demonstrate satisfactory performance (AUC 0.69-1) in different molecular levels of cerebrospinal fluid (CSF) and peripheral blood (PB) . And the validity of the peripheral transcript level model was validated in an external cohort, resulting in the ability to distinguish between disease states and normal states (AUC 0.72–0.79). Importantly, we screened seven molecular markers at the overall body fluid level (CSF + PB) in T2DM monkeys. Compared with the single molecular level, the multi-omics level, especially CSF + PB, can reflect different levels of molecular characterization changes and provide more information for clarifying AD biomarker changes in vivo. This result was also verified at the histopathological level. These results provide high-performance molecular markers for early AD prediction.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110849"},"PeriodicalIF":6.3,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757841","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":"Photon-counting detector CT for bone defect repair: Unveiling the impact of pore size on osseointegration and the advantage over traditional imaging","authors":"Yuanbo Ma ,&nbsp;Danyang Su ,&nbsp;Shenyu Yang,&nbsp;Qiuju Miao,&nbsp;Zhen Bai,&nbsp;Yaman Li,&nbsp;Yufang Du,&nbsp;Jinlong Liu,&nbsp;Fei Li,&nbsp;Xiaopeng Yang","doi":"10.1016/j.compbiomed.2025.110881","DOIUrl":"10.1016/j.compbiomed.2025.110881","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;The purpose of this study was to evaluate the role of photon counting detector CT (PCD-CT) in revealing the osseointegration properties of porous polyether ether ketone (PEEK) scaffolds. It was also compared with energy-integrated detector CT (EID-CT) and micro-computed tomography (Micro-CT) to assess the potential and accuracy of PCD-CT in visualizing bone details and evaluating implant-mediated bone regeneration and repair processes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and methods&lt;/h3&gt;&lt;div&gt;PEEK scaffolds with different pore sizes (0, 200, and 400 μm) were implanted into a rabbit tibial defect model. The bone defect model was analyzed by three imaging techniques: EID-CT (SOMATOM Force), PCD-CT (NAEOTOM Alpha) and Micro-CT (ZKKS-MCT-Sharp). Bone structure parameters such as bone volume to tissue volume ratio (BV/TV) and bone surface area to tissue volume ratio (BS/TV) were evaluated. The bone structure parameters obtained using the different imaging techniques were compared to analyze consistency and correlation. In addition, both quantitative and qualitative evaluations of the clarity of the bone–implant interface were conducted.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The BV/TV and BS/TV values tended to increase, while the BS/BV values tended to decrease in all groups as the growth cycle lengthened. Additionally, we found that the location of new bone growth varied with different pore size scaffolds. In 3D reconstructed images, PCD-CT demonstrated superior visualization compared to EID-CT, providing more detailed images of osseointegration. Compared to Micro-CT, although PCD-CT does not provide more detail, it offers a substantially lower radiation dose (Mean dose: 3.306 mGy vs. 642 mGy, p &lt; 0.0001). For the quantitative analysis of bone structure parameters, both EID-CT and PCD-CT analyses showed lower results than Micro-CT. However, PCD-CT values were closer to those of Micro-CT and exhibited better agreement (Bias = 0.009 for BV/TV; Bias = 0.014 for BS/TV; Bias = −0.945 for BS/BV) and stronger correlation (R = 0.849 for BV/TV; R = 0.941 for BS/TV; R = 0.622 for BS/BV) with Micro-CT. Quantitative analysis of scaffold osseointegration edge sharpness showed that PCD-CT (308.4 ± 144.1 Gy value/mm) had higher edge sharpness, which was significantly different from EID-CT (115.4 ± 66.12 Gy value/mm; p &lt; 0.0001) and not significantly different from Micro-CT (286.1 ± 117.6 Gy value/mm; p &gt; 0.05). Observer scores also demonstrated that PCD-CT had better image quality than EID-CT and is comparable to Micro-CT.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;PCD-CT demonstrates significant advantages over conventional imaging techniques in the assessment of osseointegration properties, particularly in the visualization of osseointegration and the accurate measurement of bone structural parameters. The application of PCD-CT not only provides high imaging accuracy but also reduces scanning costs and radiation doses. This reduction in r","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110881"},"PeriodicalIF":6.3,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757844","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":"Segmentation of coronary calcifications with a domain knowledge-based lightweight 3D convolutional neural network","authors":"Rui Santos ,&nbsp;Rui Castro ,&nbsp;Rúben Baeza ,&nbsp;Fábio Nunes ,&nbsp;Vítor M. Filipe ,&nbsp;Francesco Renna ,&nbsp;Hugo Paredes ,&nbsp;Ricardo Fontes-Carvalho ,&nbsp;João Pedrosa","doi":"10.1016/j.compbiomed.2025.110798","DOIUrl":"10.1016/j.compbiomed.2025.110798","url":null,"abstract":"<div><div>Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast computed tomography is a widely accepted and heavily employed technique for risk assessment. Manual segmentation of these calcifications is a time-consuming task, subject to variability. State-of-the-art methods often employ convolutional neural networks for an automated approach. However, there is a lack of studies that perform these segmentations with 3D architectures that can gather important and necessary anatomical context to distinguish the different coronary arteries. This paper proposes a novel and automated approach that uses a lightweight three-dimensional convolutional neural network to perform efficient and accurate segmentations and calcium scoring. Results show that this method achieves Dice score coefficients of 0.93 ± 0.02, 0.93 ± 0.03, 0.84 ± 0.02, 0.63 ± 0.06 and 0.89 ± 0.03 for the foreground, left anterior descending artery (LAD), left circumflex artery (LCX), left main artery (LM) and right coronary artery (RCA) calcifications, respectively, outperforming other state-of-the-art architectures. An external cohort validation also showed the generalization of this method’s performance and how it can be applied in different clinical scenarios. In conclusion, the proposed lightweight 3D convolutional neural network demonstrates high efficiency and accuracy, outperforming state-of-the-art methods and showcasing robust generalization potential.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110798"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750031","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 Decision Support System Based on multi-head convolutional and Recurrent Neural Networks for assisting physicians in diagnosing ADHD","authors":"Javier Sanchis ,&nbsp;Miguel A. Teruel ,&nbsp;Juan Trujillo","doi":"10.1016/j.compbiomed.2025.110826","DOIUrl":"10.1016/j.compbiomed.2025.110826","url":null,"abstract":"<div><h3>Background:</h3><div>Attention-Deficit Hyperactivity Disorder (ADHD) is highly prevalent among children and adolescents. Traditional diagnostic methods are subjective and time-consuming, underscoring the need for more objective diagnostic tools. Electroencephalography (EEG) has emerged as a promising biomarker for detecting ADHD. This study proposes MCRNet, a Multi-head Convolutional and Recurrent Neural Network, for aiding in ADHD detection using EEG and Deep Learning (DL) techniques.</div></div><div><h3>Method:</h3><div>MCRNet integrates a parallel architecture of two modules, convolutional and recurrent neural networks. The convolutional module introduces an innovative two-stage multi-head approach for enhanced feature extraction. The model was evaluated using cross-subject validation ensuring its applicability to new, unseen patients.</div></div><div><h3>Results:</h3><div>The model achieved an accuracy of 94.87% and a recall of 98.33%, indicating high reliability in identifying ADHD cases. MCRNet outperforms existing methodologies that employ raw EEG signals as input with cross-subject validation, offering an objective and reliable tool for ADHD diagnosis.</div></div><div><h3>Conclusions:</h3><div>MCRNet shows potential in reliably aiding ADHD diagnosis. Its two-stage multi-head approach enhances feature extraction and classification from raw EEG signals. Future work should focus on MCRNet’s explainability and test its efficacy on additional EEG datasets.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110826"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750028","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":"MicroRNA regulation of PPARG Signalling: Therapeutic implications for pulmonary hypertension","authors":"Renzhi Su ,&nbsp;Armalya Pritazahra ,&nbsp;ShirinC.C. Saverimuttu ,&nbsp;RuthC. Lovering ,&nbsp;LucieH. Clapp ,&nbsp;JigishaA. Patel","doi":"10.1016/j.compbiomed.2025.110884","DOIUrl":"10.1016/j.compbiomed.2025.110884","url":null,"abstract":"<div><div>Gene Ontology (GO) is a tool which provides functional gene annotations and is an essential resource for knowledge discovery and the analysis of biological datasets. Although considerable research has quantified the functional similarity between gene products and physiological processes, their contribution to human pathophysiology requires further exploration. Previous studies have identified PPARG participating in multiple signalling pathways, particularly those networks involving TGFB1 and BMP2 in pulmonary arterial smooth muscle cells (PASMCs) and their role in pulmonary arterial hypertension (PAH) pathobiology. To enhance the description of PPARG in the GO resource, we systematically curated the proteins it interacts with and its physiological role in human cells and tissues. In addition, we curated the microRNAs that may play a role in PAH through their regulation of PPARG expression and downstream impact on cellular processes. We curated using robust experimental criteria 101 human miRNAs that regulate the expression of 17 PPARG signalling pathway-relevant proteins. Of these, 91 of our curated miRNAs were previously unannotated in terms of directly regulating the expression of these priority proteins. By submitting these annotations to the GO Consortium database, we have significantly enhanced the breadth and depth of the GO description of PPARG-associated signalling pathways relevant to the pulmonary vasculature.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110884"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750029","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 RF-based end-to-end Breast Cancer Prediction algorithm","authors":"Khin Nandar Win","doi":"10.1016/j.compbiomed.2025.110785","DOIUrl":"10.1016/j.compbiomed.2025.110785","url":null,"abstract":"<div><div>Breast cancer became the primary cause of cancer-related deaths among women year by year. Early detection and accurate prediction of breast cancer play a crucial role in strengthening the quality of human life. Many scientists have concentrated on analyzing and conducting the development of many algorithms and progressing computer-aided diagnosis applications. Whereas many research have been conducted, feature research on cancer diagnosis is rare, especially regarding predicting the desired features by providing and feeding breast cancer features into the system. In this regard, this paper proposed a Breast Cancer Prediction (RF-BCP) algorithm based on Random Forest by taking inputs to predict cancer. For the experiment of the proposed algorithm, two datasets were utilized namely Breast Cancer dataset and a curated mammography dataset, and also compared the accuracy of the proposed algorithm with SVM, Gaussian NB, and KNN algorithms. Experimental results show that the proposed algorithm can predict well and outperform other existing machine learning algorithms to support decision-making.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110785"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750027","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":"Unsupervised learning for labeling global glomerulosclerosis","authors":"Hrafn Weishaupt ,&nbsp;Justinas Besusparis ,&nbsp;Cleo-Aron Weis ,&nbsp;Stefan Porubsky ,&nbsp;Arvydas Laurinavičius ,&nbsp;Sabine Leh","doi":"10.1016/j.compbiomed.2025.110719","DOIUrl":"10.1016/j.compbiomed.2025.110719","url":null,"abstract":"<div><h3>Background:</h3><div>Labeling images for supervised learning in nephropathology is highly time-consuming and dependent on domain-expertise. Unsupervised strategies have been suggested for mitigating this bottleneck. For instance, previous work suggested that clustering/grouping of glomeruli based on image features might enable a more semi-automated labeling of morphological classes or even a completely unsupervised training. However, even for the most basic separation between globally sclerosed and non-globally sclerosed glomeruli, the performance of clustering approaches has not yet been fully elucidated. The current study sought to fill this gap by extensively evaluating the accuracy and limitations of capturing these two classes via clustering.</div></div><div><h3>Methods:</h3><div>Clustering was investigated across 10 labeled datasets with diverse compositions and histological stains and across the feature embeddings produced by 34 different pre-trained CNN models.</div></div><div><h3>Results:</h3><div>As demonstrated by the study, clustering of globally and non-globally sclerosed glomeruli is generally highly feasible, yielding accuracies of over 95% in most datasets.</div></div><div><h3>Conclusions:</h3><div>While further work will be required to expand these experiments towards the clustering of additional glomerular lesion categories, the study clearly demonstrates that clustering might serve as a highly accurate means of pre-labeling glomeruli. Importantly, these findings strongly support clustering as a solid basis for downstream interactive labeling approaches or unsupervised learning approaches. Together, these results might greatly improve the possibilities and lookout for the establishment of clinically applicable glomerular classification models in the community. Further improvements in this area might be achieved by exploring more domain-specific feature extractors through contrastive learning or established foundation models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110719"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750030","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":"The stereotactic radiosurgery-brain prognostic index (SRS-BPI): A novel prognostic index for SRS-elligible lung cancer patients with brain metastases","authors":"A. Koulouris ,&nbsp;M. Skribek ,&nbsp;C. Kamali ,&nbsp;O. Grundberg ,&nbsp;M. Gubanski ,&nbsp;K. Kalaitzidis ,&nbsp;E. Lampa ,&nbsp;P. Hydbring ,&nbsp;S. Ekman ,&nbsp;G. Tsakonas","doi":"10.1016/j.compbiomed.2025.110790","DOIUrl":"10.1016/j.compbiomed.2025.110790","url":null,"abstract":"<div><h3>Background</h3><div>Existing prognostic models for lung cancer patients with brain metastases (BM) have certain limitations and are not specifically designed for individuals eligible for BM stereotactic radiosurgery (SRS). This research seeks to assess the predictive accuracy of current indices and establish a new prognostic model tailored to this patient group.</div></div><div><h3>Methods</h3><div>This retrospective cohort study analyzed data from 673 cancer patients with BM, ultimately including 431 individuals with lung cancer who underwent SRS at Karolinska University Hospital, Sweden, between 2009 and 2020 (representing all-comers from the Stockholm region). Demographic and clinicopathological data were extracted from electronic medical records. Predictors of overall survival (OS) were identified and incorporated into a newly developed prognostic score using a penalized Cox regression model. Internal validation was conducted through Efron-Gong optimism bootstrap analysis. The predictive performance of previously established prognostic indices was evaluated and compared to that of the newly proposed prognostic model.</div></div><div><h3>Results</h3><div>A novel prognostic index, named the SRS-Brain Prognostic Index (SRS-BPI), was developed, incorporating nine key variables: age, performance status, histology, genetic alterations, BM volume, neurological symptoms, BM at diagnosis, presence of extracranial metastases, and extracranial disease control. The in-sample C-index for one-year survival was 0.689, while the optimism-corrected C-index was 0.67, suggesting minimal overfitting. The SRS-BPI exhibited near-optimal calibration, with a low mean absolute error of 0.04 for one-year OS. It enabled continuous risk prediction and demonstrated superior discriminatory ability compared to previously established prognostic models.</div></div><div><h3>Conclusion</h3><div>The SRS-BPI emerges as an internally validated, advanced prognostic tool, providing enhanced decision-making support for lung cancer patients with BM who are candidates for SRS. Although internally validated, external validation is essential and is currently in progress.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110790"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757970","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":"Prescribed finite-time backstepping control for blood glucose regulation in type 1 diabetes","authors":"Jitendra Singh ,&nbsp;Vijay Kumar Singh ,&nbsp;Debasmita Mondal ,&nbsp;Sahaj Saxena","doi":"10.1016/j.compbiomed.2025.110737","DOIUrl":"10.1016/j.compbiomed.2025.110737","url":null,"abstract":"<div><div>Automated blood glucose regulation in Type 1 diabetes (T1D) is challenging due to nonlinear glucose-insulin dynamics and meal-induced disturbances. Existing control strategies lack guaranteed finite-time convergence, risking prolonged hyperglycemia and hypoglycemia post-meals. We propose a novel prescribed finite-time backstepping controller derived from the Bergman Minimal Model (BMM), enforcing user-defined convergence bounds to ensure glucose stabilization within a pre-specified duration, regardless of meal variability. The control law guarantees finite-time convergence through Lyapunov stability. The efficiency and efficacy of the proposed approach are tested through case studies involving varying pre-specified time windows, meal variability, and physiological variability across patients. The proposed method outperforms existing conventional backstepping controllers and sliding mode control approaches, ensuring smooth, stable, and efficient regulation of blood glucose levels within defined time constraints while avoiding hypoglycemic events. The method’s prescribed finite-time convergence ensures safety-critical performance, addressing a key limitation of prior approaches.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"196 ","pages":"Article 110737"},"PeriodicalIF":6.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739552","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}