Computers in biology and medicine最新文献

{"title":"A mathematical model for activated platelet-dependent activation of coagulation factor X by factor IXa","authors":"Anastasia A. Bozhko ,&nbsp;Mikhail A. Panteleev","doi":"10.1016/j.compbiomed.2025.110263","DOIUrl":"10.1016/j.compbiomed.2025.110263","url":null,"abstract":"<div><div>Membrane-dependent enzymatic reactions are central in many signaling and regulatory biological networks. Activation of blood coagulation factor X by activated factor IXa is a classical example, which retains many mysteries and controversies. Here we developed a novel non-stationary two-compartment computational model of this reaction on the physiological membrane of activated platelets (rather than phospholipid vesicles) within a wide platelet concentration range up to the intra-thrombus conditions, which took into account novel essential revisions in the mechanisms on factor IXa interactions with platelets. The set of ordinary differential equations (ODEs) was based on the laws of mass action and included several possible pathways of the complex formation. Sensitivity analysis was employed to identify critical points in the regulation. The model was able to describe the available experimental data and suggested that the major pathways of the enzyme-substrate complex assembly were membrane-dependent and solution-dependent enzyme delivery, with comparable contributions. The dependence of factor Xa formation on the activated procoagulant platelet concentration was predicted to be bell-shaped with the peak at (1.5–2)·10⁶ platelets/μL, which is similar to the expected intra-thrombus concentration. The modeling of the kinetics of all model variables demonstrated two-phase kinetics. With increasing platelet concentration in the system, the transition time after which a stationary concentration is reached increases to approximately 5 min.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110263"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877319","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":"Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning","authors":"Balaji Iyer ,&nbsp;Smruti Deoghare ,&nbsp;Krish Ranjan ,&nbsp;Bruce J. Aronow ,&nbsp;V.B. Surya Prasath","doi":"10.1016/j.compbiomed.2025.110150","DOIUrl":"10.1016/j.compbiomed.2025.110150","url":null,"abstract":"<div><div>Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classification, significantly enhances overall performance when executed with precision. In this study, we conduct a systematic literature review of HEp-2 cell segmentation techniques, identifying 28 key papers utilizing traditional image processing, machine learning classifiers, deep convolutional neural networks (CNNs), and generative adversarial network (GAN) frameworks. Building on these insights, we benchmark 17 CNN models without pretraining and 8 CNN models pretrained on ImageNet using both Frozen Encoder and Tunable Encoder strategies on the I3A dataset. Cross-validation (CV) and Benjamini–Hochberg (BH) significance correction were employed to ensure statistical rigor in model comparisons. Domain-Specific Pretraining (DSPT) experiments demonstrated performance improvements, particularly for underrepresented classes, while Data Augmentation strategies (DA-1 and DA-2) revealed distinct impacts across model categories. GAN-based segmentation experiments using the top-performing CNN architectures as generators within a Pix2Pix framework revealed performance degradation due to data limitations and adversarial training instabilities. Nonetheless, GANs displayed class-specific improvements in visual alignment of segmentation masks. Results were evaluated comprehensively across eight performance metrics, including Dice, IOU, Accuracy, Precision, Sensitivity, Specificity, AU-ROC and AU-PR. This work offers a robust benchmarking of state-of-the-art CNN, GAN, and Transformer-based models for HEp-2 cell segmentation, providing valuable insights for future research directions, including ensemble approaches, dynamic patch sampling, and diffusion models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110150"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877238","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":"Eigenhearts: Cardiac diseases classification using eigenfaces approach","authors":"Nourelhouda Groun ,&nbsp;María Villalba-Orero ,&nbsp;Lucía Casado-Martín ,&nbsp;Enrique Lara-Pezzi ,&nbsp;Eusebio Valero ,&nbsp;Soledad Le Clainche ,&nbsp;Jesús Garicano-Mena","doi":"10.1016/j.compbiomed.2025.110167","DOIUrl":"10.1016/j.compbiomed.2025.110167","url":null,"abstract":"<div><div>In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, as it requires a large volume of images, while ethical constraints, high costs, and variability in imaging protocols limit data acquisition. As a consequence, it is necessary to investigate different avenues to overcome this challenge. In this contribution, we offer an innovative tool to conquer this limitation. In particular, we delve into the application of a well recognized method known as the <em>eigenfaces</em> approach to classify cardiac diseases. This approach was originally motivated for efficiently representing pictures of faces using principal component analysis, which provides a set of eigenvectors (aka <em>eigenfaces</em>), explaining the variation between face images. Given its effectiveness in face recognition, we sought to evaluate its applicability to more complex medical imaging datasets. In particular, we integrate this approach with convolutional neural networks to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). The results show a substantial and noteworthy enhancement when employing the singular value decomposition for pre-processing, with classification accuracy increasing by approximately 50%.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110167"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873295","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":"Detection of β-Thalassemia trait from a heterogeneous population with red cell indices and parameters","authors":"Subrata Saha ,&nbsp;Prashant Sharma ,&nbsp;Atul Kumar Jain ,&nbsp;Bapi Dutta ,&nbsp;Luis Martínez ,&nbsp;Sarkaft Saleh ,&nbsp;Tuphan Kanti Dolai ,&nbsp;Anilava Kaviraj ,&nbsp;Tanmay Sanyal ,&nbsp;Izabela Nielsen ,&nbsp;Reena Das","doi":"10.1016/j.compbiomed.2025.110151","DOIUrl":"10.1016/j.compbiomed.2025.110151","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background:&lt;/h3&gt;&lt;div&gt;India is home to about 42 million people with &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;-thalassemia trait (&lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT) necessitating screening of &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT to stop spread of the disease. Over the years, researchers developed discrimination formulae based on red blood cell (RBC) parameters to screen &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;-thalassemia trait from iron deficiency anemia (IDA). However, the screening programs often encounter normal subjects (NSs) with other hemoglobinopathy variants. Because the outcome of existing formulas is binary, they often club normal subjects (NS) or variants such as Hemoglobin E (HbE) traits with either &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT or IDA. Therefore, it is necessary to segregate &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT, IDA, HbE, and NS in mixed population data for rational screening.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;A test data of 2877 subjects with 1226 NS, 425 HbE, 223 IDA, and 1003 &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT were collected from the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India and NRS Medical College and Hospital, Kolkata, India. First, we evaluated the performance of 25 discrimination formulae and four machine learning algorithms (MLA), Multi-Layer Perceptron (MLP), Neighborhood Components Analysis (NCA), eXtreme Gradient Boosting Classifier (XGBC), and SKope-Rules (SKR) based on seven performance measures. Based on the performance measures, we selected four discrimination formulae and two MLAs for further evaluation. The SHapley Additive exPlanations (SHAP) model was employed to explore the interpretability of outcomes. We generated four rules using the SKR algorithm to discriminate variants of hemoglobinopathies. Finally, a step-wise implementation scheme for screening is proposed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;Results demonstrate that a single formula cannot ensure high performance for all the performance measures. When tested on data set containing &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;TT and IDA samples, the best-performing formulae appear as SCS&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; in terms of sensitivity (SE) and negative predictive value (NPV); Sirachainan in terms of specificity (SP) and positive predictive value (PPV); CRUISE in terms of Youden index (YI) and RF-4 in terms of Matthews correlation coefficient (MCC) and &lt;span&gt;&lt;math&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;-coefficient, respectively. Among MLAs, the best-performing algorithms are Skope-rule regarding SP, YI, PPV, and XGBC in the rest of the measures. When tested on a heterogeneous data set, MCC and &lt;span&gt;&lt;math&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;-coefficient for these four formulae are decreased, but the performance of the two MLAs remains steady. The proposed scheme demonstrates around 97.33–97.62% accuracy while applied to two validation data sets collect","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110151"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877239","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":"Methods and computational techniques for predicting adherence to treatment: A scoping review","authors":"Beatriz Merino-Barbancho ,&nbsp;Ana Cipric ,&nbsp;Peña Arroyo ,&nbsp;Miguel Rujas ,&nbsp;Rodrigo Martín Gómez del Moral Herranz ,&nbsp;Torben Barev ,&nbsp;Nicholas Ciccone ,&nbsp;Giuseppe Fico","doi":"10.1016/j.compbiomed.2025.110193","DOIUrl":"10.1016/j.compbiomed.2025.110193","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Treatment non-adherence of patients stands as a major barrier to effectively manage chronic conditions. However, non-adherent behavior is estimated to affect up to 50 % of patients with chronic conditions, leading to poorer health outcomes among patients, higher rates of hospitalization, and increased mortality.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This study offers a provision of a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A scoping review was conducted, and the following databases were searched to identify relevant publications: PubMed, IEEE and Web of Science. The screening of publications consisted of two steps. First, the hits obtained from the search were independently screened and selected using an open-source machine learning (ML)-aided pipeline applying active learning: ASReview, Active learning for Systematic Reviews. Publications selected for full-text review and data extraction were those highly prioritized by ASReview.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 45 papers were selected into the second round of full-text screening and 29 papers were considered in the final review. The findings suggest supervised learning (regression and classification) to be the most used analytical approach, being the generalized linear models (GEE) (21.67 %), logistic regressions (20 %) and random forest (18.33 %) the most frequently employed techniques. The family of GEE identified in the studies included some multiple, hierarchical or mixed-effect models, among other. The selection of these models often depended on data source and types (e.g., logistic regressions for dichotomous outcome measures). Furthermore, over 54 % of adherence topics being related to chronic metabolic conditions such as diabetes, hypertension, and hyperlipidemia. Most assessed predictors were both treatment and socio-demographic and economic-related factors followed by condition-related factors. The adherence to treatment variable was mostly dichotomous (12 out of 29) and computed using metrics as the Medical Possession Ratio with a 80 % threshold. A limitation of the reviewed studies is the lack of accountancy for interrelationships between different determinants of adherence behavior, denoting the need for future research regarding the use of more complex analytical techniques that better capture these connections (e.g., patient's socio-economic status and the ability to afford medication).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;The creation of systems to accurately predict treatment adherence can pave the way for improved therapeutic outcomes, reduced healthcare costs and enabling personalized treatment plans. This paper can support to understand the efforts made in the field of modeling adherence-related factors. In particular, the results provide a structured overview of the computational methods and techniqu","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110193"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873297","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":"SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data","authors":"R. Falach ,&nbsp;G. Belonosov ,&nbsp;J.F. Schmidig ,&nbsp;M. Aderka ,&nbsp;V. Zhelezniakov ,&nbsp;R. Shani-Hershkovich ,&nbsp;E. Bar ,&nbsp;Y. Nir","doi":"10.1016/j.compbiomed.2025.110232","DOIUrl":"10.1016/j.compbiomed.2025.110232","url":null,"abstract":"<div><div>Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is growing interest in advanced EEG analysis that requires extensive preprocessing to improve the signal-to-noise ratio and specialized analysis algorithms. While many EEG software packages exist, sleep research has unique needs (e.g., specific artifacts, event detection). Currently, sleep investigators use different libraries for specific tasks in a ‘fragmented’ configuration that is inefficient, prone to errors, and requires the learning of multiple software environments. This complexity creates a barrier for beginners. Here, we present SleepEEGpy, an open-source Python package that simplifies sleep EEG preprocessing and analysis. SleepEEGpy builds on MNE-Python, PyPREP, YASA, and SpecParam to offer an all-in-one, beginner-friendly package for comprehensive sleep EEG research, including (i) cleaning, (ii) independent component analysis, (iii) sleep event detection, (iv) spectral feature analysis, and visualization tools. A dedicated dashboard provides an overview to evaluate data and preprocessing, serving as an initial step prior to detailed analysis. We demonstrate SleepEEGpy's functionalities using overnight high-density EEG data from healthy participants, revealing characteristic activity signatures typical of each vigilance state: alpha oscillations in wakefulness, spindles and slow waves in NREM sleep, and theta activity in REM sleep. We hope that this software will be adopted and further developed by the sleep research community, and constitute a useful entry point tool for beginners in sleep EEG research.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110232"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877240","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":"GBDTSVM: Combined Support Vector Machine and Gradient Boosting Decision Tree Framework for efficient snoRNA-disease association prediction","authors":"Ummay Maria Muna ,&nbsp;Fahim Hafiz ,&nbsp;Shanta Biswas ,&nbsp;Riasat Azim","doi":"10.1016/j.compbiomed.2025.110219","DOIUrl":"10.1016/j.compbiomed.2025.110219","url":null,"abstract":"<div><div>Small nucleolar RNAs (snoRNAs) are increasingly recognized for their critical role in the pathogenesis and characterization of various human diseases. Consequently, the precise identification of snoRNA-disease associations (SDAs) is essential for the progression of diseases and the advancement of treatment strategies. However, conventional biological experimental approaches are costly, time-consuming, and resource-intensive; therefore, machine learning-based computational methods offer a promising solution to mitigate these limitations. This paper proposes a model called ‘GBDTSVM’, representing a novel and efficient machine learning approach for predicting snoRNA-disease associations by leveraging a Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM). ‘GBDTSVM’ effectively extracts integrated snoRNA-disease feature representations utilizing GBDT, and SVM is subsequently utilized to classify and identify potential associations. Furthermore, the method enhances the accuracy of these predictions by incorporating Gaussian integrated profile kernel similarity for both snoRNAs and diseases. Experimental evaluation of the GBDTSVM model demonstrates superior performance compared to state-of-the-art methods in the field, achieving an AUROC of 0.96 and an AUPRC of 0.95 on the ‘MDRF’ dataset. Moreover, our model shows superior performance on two more datasets named ‘LSGT’ and ‘PsnoD’. Additionally, a case study conducted on the predicted snoRNA-disease associations verified the top-ranked snoRNAs across twelve prevalent diseases, further validating the efficacy of the GBDTSVM approach. These results underscore the model’s potential as a robust tool for advancing snoRNA-related disease research. Source codes and datasets for our proposed framework can be obtained from: <span><span>https://github.com/mariamuna04/gbdtsvm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110219"},"PeriodicalIF":7.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873294","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":"MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images","authors":"Xiufeng Zhang,&nbsp;Shichen Zhang,&nbsp;Yunfei Jiang,&nbsp;Lingzhuo Tian","doi":"10.1016/j.compbiomed.2025.110245","DOIUrl":"10.1016/j.compbiomed.2025.110245","url":null,"abstract":"<div><div>Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels within the brain. Delayed and inaccurate diagnosis and treatment of ICH can lead to fatality or disability. Therefore, early and precise diagnosis of intracranial hemorrhage is crucial for protecting patients' lives. Automatic segmentation of hematomas in CT images can provide doctors with essential diagnostic support and improve diagnostic efficiency. CT images of intracranial hemorrhage exhibit characteristics such as multi-scale, multi-target, and blurred edges. This paper proposes a Multi-scale and Edge Feature Fusion Network (MEF-Net) to effectively extract multi-scale and edge features and fully fuse these features through a fusion mechanism. The network first extracts the multi-scale features and edge features of the image through the encoder and the edge detection module respectively, then fuses the deep information, and employs the multi-kernel attention module to process the shallow features, enhancing the multi-target recognition capability. Finally, the feature maps from each module are combined to produce the segmentation result. Experimental results indicate that this method has achieved average DICE scores of 0.7508 and 0.7443 in two public datasets respectively, surpassing those of several advanced methods in medical image segmentation currently available. The proposed MEF-Net significantly improves the accuracy of intracranial hemorrhage segmentation.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110245"},"PeriodicalIF":7.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870514","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":"Population-optimized electrode montage approximates individualized optimization in transcranial temporal interference stimulation","authors":"Kanata Yatsuda ,&nbsp;Mariano Fernández-Corazza ,&nbsp;Wenwei Yu ,&nbsp;Jose Gomez-Tames","doi":"10.1016/j.compbiomed.2025.110223","DOIUrl":"10.1016/j.compbiomed.2025.110223","url":null,"abstract":"<div><h3>Background</h3><div>Effective transcranial temporal interference stimulation (tTIS) requires an optimized electrode configuration to target deep brain structures accurately. While individualized electric field analysis using high-resolution structural MRI enables precise electrode placement, its clinical practicality is limited by significant costs associated with imaging, specialized software, and navigation systems. Alternatively, standardized electrode montages optimized through population-based electric field analysis might overcome these limitations, although it remains unclear how accurately this approach approximates individualized optimization.</div></div><div><h3>Aim</h3><div>This study evaluates the feasibility of using group-level electric field analysis to optimize the tTIS montage. Specifically, it seeks to maximize the intracranial electric field using a population-proxy approach and compare its efficacy to individualized electric field optimization.</div></div><div><h3>Method</h3><div>We optimize the montage across various populations, balancing the trade-off between focality and electric field strength at deep brain targets. The method is compared to conventional individualized electric field-based optimization. Factors such as population size and age were analyzed for their impact on montage selection and effectiveness.</div></div><div><h3>Results</h3><div>Population-based electric field optimization demonstrated comparable focality and targeting accuracy to individualized analysis, with a difference of up to 17 %. Age mismatch between the population proxy and the target individual reduced the focality of up to 8.3 % compared to an age-matched population proxy. Also, insufficient population size led to inconsistencies in montage optimization, although these were negligible for populations larger than 40 individuals.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the capability of population-based electric field analysis to achieve targeting effects comparable to individualized-level electric field analysis in terms of focality and intensity. By eliminating the need for patient-specific MRI scans, this approach significantly enhances the accessibility and practicality of tTIS in diverse research and clinical applications.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110223"},"PeriodicalIF":7.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869872","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":"Real-time health monitoring by examining the role of next-generation elements in a medical app","authors":"S. Jayaprakash,&nbsp;J.P. Keerthana","doi":"10.1016/j.compbiomed.2025.110201","DOIUrl":"10.1016/j.compbiomed.2025.110201","url":null,"abstract":"<div><div>The healthcare sector is undergoing a profound transformation driven by the rapid rise in healthcare applications (mHealth apps), which are becoming integral to how patients manage their health. This paper examines the role of next-generation technologies such as Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML) in enhancing healthcare applications, specifically in telemedicine, health tracking and medical delivery. The research is motivated by the dramatic surge in mHealth app usage, particularly following the COVID-19 pandemic and the growing demand for digital solutions to improve patient care. By conducting a comprehensive analysis of existing healthcare apps, this study evaluates their functionalities, user engagement and adoption rates. It finds that integrating advanced technologies significantly improves the user experience, enhances operational efficiency and increases adoption rates among patients and healthcare providers. These technologies facilitate appointment scheduling, health monitoring and access to medical records, ultimately enabling users to manage wellness goals and illnesses more effectively. Furthermore, they streamline healthcare operations, making them more efficient and cost-effective. The paper highlights the transformative potential of integrating these technologies into healthcare apps, which can greatly improve patient care outcomes and pave the way for future innovations in digital health solutions. Through qualitative and quantitative assessments, this study provides valuable insights for developers and healthcare professionals looking to optimize the effectiveness and adoption of digital health applications.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"192 ","pages":"Article 110201"},"PeriodicalIF":7.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873293","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}