Computer methods and programs in biomedicine最新文献

{"title":"Online calibration of focal spot drift for a high-resolution micro-CT system","authors":"Li Chen ,&nbsp;Qingxian Zhao ,&nbsp;Sinong Su ,&nbsp;Yuchen Lu ,&nbsp;Yikun Zhang ,&nbsp;Shouhua Luo ,&nbsp;Yang Chen ,&nbsp;Xu Ji","doi":"10.1016/j.cmpb.2025.109210","DOIUrl":"10.1016/j.cmpb.2025.109210","url":null,"abstract":"<div><h3>Background:</h3><div>Micro-CT (Micro-Computed Tomography) is a high-resolution, non-destructive three-dimensional imaging technology widely applied in biomedical research. However, the long scanning time of micro-CT and its higher sensitivity to small-scale perturbations make focal spot drift a more likely and non-negligible source of geometric artifacts. Focal spot drift is typically stochastic and unrepeatable, which may make offline calibration inaccurate, while conventional online calibration tends to be time-consuming due to iterative operations.</div></div><div><h3>Methods:</h3><div>This paper presents a fast, accurate, and convenient online calibration method that overcomes the limitations commonly associated with existing online calibration approaches. To the best of our knowledge, this work is the first to explicitly and quantitatively describe the relationship between 3D focal spot drift and the resulting 2D projection shifts in micro-CT systems. By leveraging the prior geometric information of a specific feature point on the marker, its true spatial location can be precisely determined, which enables the tracking of its ideal trajectory and corresponding ideal projection positions across all views. Consequently, artifacts induced by focal spot drift can be effectively corrected by compensating for the offsets between the measured and ideal projection positions of the point. The entire correction process requires only a single pass of forward and backward projection.</div></div><div><h3>Results:</h3><div>The effectiveness and applicability of the method were validated through numerical simulation, physical experiments, and supplementary experiments. In numerical simulations, the method remained effective with even fivefold the normal perturbation level. In physical experiments without ground truth, this method achieved the highest level score according to BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) and completed the correction in the shortest time, making it the most efficient among all methods. Finally, the supplementary experiments were conducted to verify the applicability of the algorithm under certain assumptions and errors, further demonstrating its practicality.</div></div><div><h3>Conclusions:</h3><div>The proposed method preserves the high correction accuracy and strong applicability of online calibrations while avoiding the need for frequent iterations or forward/backward projections, thereby achieving high computational efficiency. It is particularly well-suited for micro-CT systems with a large number of scan views and high projection resolution.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109210"},"PeriodicalIF":4.8,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771863","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":"Random forests for individual treatment effect estimation with the R package ITERF","authors":"Sami Tabib,&nbsp;Denis Larocque","doi":"10.1016/j.cmpb.2025.109187","DOIUrl":"10.1016/j.cmpb.2025.109187","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Treatment effects often vary across individuals within a population. In contexts such as personalized medicine, it is crucial to accurately estimate treatment effects at the individual level. Random forests are among the most popular, versatile, and efficient statistical learning methods. This article introduces the <span>R</span> package <span>ITERF</span>, designed to estimate individual treatment effects using random forests across various settings. In particular, new methods to estimate the maximum treatment effect are introduced.</div></div><div><h3>Methods and Results:</h3><div>The <span>ITERF</span> package provides methods for estimating treatment effects in two scenarios: (1) survival outcomes with right-censoring and a binary treatment, and (2) continuous outcomes with a continuous treatment. All methods are based on random forests. A simulation study demonstrates that the proposed methods for estimating the maximum treatment effect perform as expected and show considerable promise. An illustration, using real data, that explores the link between sleep duration and cognitive health in the elderly is given.</div></div><div><h3>Conclusion:</h3><div>The <span>ITERF</span> package offers a fast and user-friendly tool for estimating treatment effect measures using random forests, making it a valuable resource for researchers and practitioners in personalized treatment evaluation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109187"},"PeriodicalIF":4.8,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145827169","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 subject-based association network defines new pediatric sleep apnea phenotypes with different odds of recovery after treatment","authors":"Gonzalo C. Gutiérrez-Tobal ,&nbsp;Javier Gomez-Pilar ,&nbsp;Daniela Ferreira-Santos ,&nbsp;Pedro Pereira-Rodrigues ,&nbsp;Daniel Álvarez ,&nbsp;Félix del Campo ,&nbsp;David Gozal ,&nbsp;Roberto Hornero","doi":"10.1016/j.cmpb.2025.109209","DOIUrl":"10.1016/j.cmpb.2025.109209","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Timely treatment of pediatric obstructive sleep apnea (OSA) can prevent or reverse neurocognitive and cardiovascular morbidities. However, whether distinct phenotypes exist and account for divergent treatment effectiveness remains unknown. In this study, our goal is threefold: i) to define new data-driven pediatric OSA phenotypes, ii) to evaluate possible treatment effectiveness differences among them, and iii) to assess phenotypic information in predicting OSA resolution.</div></div><div><h3>Methods</h3><div>We involved 22 sociodemographic, anthropometric, and clinical data from 464 children (5–10 years old) from the Childhood Adenotonsillectomy Trial (CHAT) database. Baseline information was used to automatically define pediatric OSA phenotypes using a new unsupervised subject-based association network. Follow-up data (7 months later) were used to evaluate the effects of the therapeutic intervention in terms of changes in the obstructive apnea-hypopnea index (OAHI) and the resolution of OSA (OAHI &lt; 1 event per hour). An explainable artificial intelligence (XAI) approach was also developed to assess phenotypic information as OSA resolution predictor at baseline.</div></div><div><h3>Results</h3><div>Our approach identified three OSA phenotypes (PH_OSA1-PH_OSA3), with PH_OSA2 showing significantly lower odds of OSA recovery than PH_OSA1 and PH_OSA3 when treatment information was not considered (odds ratios, OR: 1.64 and 1.66, 95 % confidence intervals, CI: 1.03–2.62 and 1.01–2.69, respectively). The odds of OSA recovery were also significantly lower in PH_OSA2 than in PH_OSA3 when adenotonsillectomy was adopted as treatment (OR: 2.60, 95 % CI: 1.26–5.39). Our XAI approach identified 79.4 % (CI: 69.9–88.0 %) of children reaching OSA resolution after adenotonsillectomy, with a positive predictive value of 77.8 % (CI: 70.3 %-86.0 %).</div></div><div><h3>Conclusions</h3><div>Our new subject-based association network successfully identified three clinically useful pediatric OSA phenotypes with different odds of therapeutic intervention effectiveness. Specifically, we found that children of any sex, &gt;6 years old, overweight or obese, and with enlarged neck and waist circumference (PH_OSA2) have less odds of recovering from OSA. Similarly, younger female children with no enlarged neck (PH_OSA3) have higher odds of benefiting from adenotonsillectomy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109209"},"PeriodicalIF":4.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145780471","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":"FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis","authors":"Jingzhen Wang ,&nbsp;Yanling Xu ,&nbsp;Beini Sun,&nbsp;Zhiqiang Wei,&nbsp;Rumeng Qu,&nbsp;Fengting Wang,&nbsp;Zhengfei Zhuang,&nbsp;Min Hu,&nbsp;Tongsheng Chen","doi":"10.1016/j.cmpb.2025.109208","DOIUrl":"10.1016/j.cmpb.2025.109208","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The fluorescence resonance energy transfer (FRET) two-hybrid assay enables quantification of the stoichiometry and binding affinity of protein interactions directly in living cells, but its broader application remains constrained by labor-intensive manual image analysis and high computational complexity. This study leverages deep learning to accurately extract FRET two-hybrid image signals and automate the FRET two-hybrid analysis process, thereby eliminating subjective bias and enhancing the method’s efficiency and accuracy.</div></div><div><h3>Methods:</h3><div>Based on the Segment Anything Model (SAM), we developed FRET-SAM, an optimized analysis method adapting SAM_Med2D’s structure for automated regions of interest (ROI) selection and fluorescence signal extraction in FRET two-hybrid images. A comprehensive FRET image dataset was established, including six model plasmids (C4Y, C10Y, C40Y, C80Y, C32V and CVC) and three functional FRET pairs (Bcl-XL-CFP/Bak-YFP, EGFR-CFP/Grb2-YFP and RAF-CFP/RAS-YFP), for model training and validation. Model segmentation performance was assessed by comparing its mean pixel accuracy (MPA), mean intersection over union (MIoU), and Dice coefficient against the original SAM_Med2D model. To assess protein interaction results, FRET-SAM-derived values were compared to established literature values, using relative error as a key metric of consistency.</div></div><div><h3>Results:</h3><div>The FRET-SAM model exhibited enhanced segmentation accuracy, with MPA, MIoU, and Dice coefficient increasing by 2.88%, 2.36%, and 2.19%, respectively, compared to the original SAM_Med2D model. Validation experiments demonstrated high consistency between FRET-SAM-derived results and literature values, with all plasmid models exhibiting relative errors that were individually calculated and confirmed to be under 5%. Furthermore, FRET-SAM exhibited robust drug screening potential in three biomedical case studies: (1) EGFR-Grb2-targeted lung cancer intervention (gefitinib), (2) RAS-RAF-mediated hepatocellular carcinoma suppression (sorafenib), and (3) Bcl-XL inhibitors discovery (A-1331852). Mechanistic studies confirmed its ability to resolve drug-target interactions.</div></div><div><h3>Conclusions:</h3><div>By enabling automated analysis of FRET images, FRET-SAM significantly enhances the efficiency and accuracy of FRET two-hybrid assays, while eliminating subjective bias. The capability of FRET-SAM to resolve drug-target interactions establishes it as a promising tool for drug discovery.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109208"},"PeriodicalIF":4.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145767252","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 novel multimodal diagnostic framework integrating hyperspectral imaging and deep learning for predicting RET gene mutations in medullary thyroid carcinoma","authors":"Zhenpeng Yang ,&nbsp;Peng Su ,&nbsp;Yuyang Zhang ,&nbsp;Haitao Zheng ,&nbsp;Yupeng Deng ,&nbsp;Xiangfeng Lin ,&nbsp;Changyuan Ding ,&nbsp;Wei Li ,&nbsp;Weili Liang ,&nbsp;Bin Lv","doi":"10.1016/j.cmpb.2025.109207","DOIUrl":"10.1016/j.cmpb.2025.109207","url":null,"abstract":"<div><h3>Background and objective</h3><div>Medullary thyroid carcinoma (MTC) is an aggressive malignancy driven predominantly by activating mutations in the RET proto-oncogene. Conventional genotyping using polymerase chain reaction (PCR) or next-generation sequencing (NGS) is often hampered by burdensome costs and prolonged turnaround times, hindering timely clinical decision-making.</div></div><div><h3>Methods</h3><div>We developed a rapid, cost-effective, multimodal deep-learning framework to predict RET mutations from standard H&amp;E-stained slides. Our approach leverages hyperspectral imaging and integrates a 1D-CNN-LSTM network for spectral analysis with a Swin Transformer for spatial feature extraction. A cross-modal attention mechanism effectively fuses these representations. The model was trained and validated on 82 MTC cases from Qilu Hospital and externally tested on independent cohorts from two additional centers (<em>n</em> = 60).</div></div><div><h3>Results</h3><div>The proposed framework achieved an overall accuracy of 89.5 %, with a sensitivity of 90.2 % and specificity of 88.6 % for RET mutation classification. External validation confirmed robust generalizability, with performance surpassing single-modality benchmarks by 7.0–19.5 %.</div></div><div><h3>Conclusions</h3><div>This study presents a non-invasive and efficient alternative for predicting RET mutations in MTC, demonstrating the potential of hyperspectral imaging and integrated deep learning to advance precision oncology.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109207"},"PeriodicalIF":4.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145780510","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":"Development of a predictive model for Ki-67 index of meningiomas by integrating deep-learning, radiomics and clinical features utilizing fully automated segmentation results","authors":"Xin Ma ,&nbsp;Yajing Zhao ,&nbsp;Kaiyue Zhang ,&nbsp;Nan Mei ,&nbsp;Xuanxuan Li ,&nbsp;Jin Cui ,&nbsp;Jie Chen ,&nbsp;Yuxi Xie ,&nbsp;Yiping Lu ,&nbsp;Bo Yin","doi":"10.1016/j.cmpb.2025.109205","DOIUrl":"10.1016/j.cmpb.2025.109205","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the efficacy of clinical information, traditional radiological, radiomics and deep-learning features combinations for constructing a predictive model for the Ki-67 index of meningiomas.</div></div><div><h3>Material and methods</h3><div>This study acquired retrospective (198 cases) and prospective (22 cases) meningioma data between 2015 and 2020. Within the retrospective data, 160 cases were utilized for training, while 38 were allocated to an independent test. Ki-67 expression levels were dichotomized into low and high groups using a 4% threshold based on previous research. The study developed and evaluated five classifier models combining clinical information, radiomics and deep-learning features to predict Ki-67 expression levels. Model performance was evaluated via the receiver operating characteristic (ROC) curves and the area under the curve (AUC), obtaining a 95% confidence interval (CI) using DeLong testing. Subsequently, the most effective model was validated using prospective data from 22 cases.</div></div><div><h3>Results</h3><div>The eXtreme Gradient Boosting (XGBoost) classifier model showed optimal performance among the five classifier models. The AUC for the independent test dataset was 0.717 (CI: 0.575-0.858). After optimization, the AUC of the test dataset is 0.767 (CI: 0.631-0.903). The AUC for the prospective test data set was 0.773 (CI: 0.590-0.955). Decision curve analysis (DCA) showed that combining clinical information, radiomics, and deep-learning features resulted in the best predictive performance of the XGBoost classifier.</div></div><div><h3>Conclusion</h3><div>An integrated radiomics model enables Ki-67 prediction and has great potential to estimate the risk of tumor regrowth and recurrence non-invasively.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109205"},"PeriodicalIF":4.8,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787096","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":"Explainable feature selection combining particle swarm optimisation with adaptive LASSO for MRI radiogenomics: Predicting HPV status in oropharyngeal cancer","authors":"Milad Ahmadian ,&nbsp;Zuhir Bodalal ,&nbsp;Mary Adib ,&nbsp;Seyed Sahand Mohammadi Ziabari ,&nbsp;Paula Bos ,&nbsp;Roland M. Martens ,&nbsp;Georgios Agrotis ,&nbsp;Conchita Vens ,&nbsp;Luc Karssemakers ,&nbsp;Abrahim Al-Mamgani ,&nbsp;Pim de Graaf ,&nbsp;Bas Jasperse ,&nbsp;Ruud H Brakenhoff ,&nbsp;C René Leemans ,&nbsp;Regina G.H. Beets-Tan ,&nbsp;Michiel W.M. van den Brekel ,&nbsp;Jonas A. Castelijns","doi":"10.1016/j.cmpb.2025.109204","DOIUrl":"10.1016/j.cmpb.2025.109204","url":null,"abstract":"<div><h3>Background</h3><div>Radiogenomic modelling faces a significant challenge due to the high-dimensional nature of quantitative radiomic features and limited sample sizes. Feature selection is therefore essential to eliminate irrelevant features and mitigate overfitting. Particle swarm optimisation (PSO) has shown promise for effectively navigating large feature spaces, yet its effectiveness in radiogenomics remains unexplored. This study investigates the value of PSO-based methods, both independently and in combination with other advanced techniques, for MRI-based prediction of human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).</div></div><div><h3>Materials and methods</h3><div>Baseline contrast-enhanced T1-weighted MR scans from two centres were analysed: 153 patients in an internal cohort (randomly split into 80 % for training and 20 % for testing) and 157 patients in an external validation cohort. Radiomic features were extracted from manually segmented tumours and multiple feature selection methods, including PSO and its ensembles, filter-based methods, wrapper-based approaches, and shrinkage techniques were evaluated. Performance was measured and compared using the area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>PSO alone had a reasonable predictive power on the internal test set (AUC = 0.76, 95 % CI: 0.57–0.92, <em>p</em> = 0.092). When combined with adaptive LASSO using Shapley values, PSO’s performance improved (AUC = 0.81, 95 % CI: 0.61–0.94, <em>p</em> = 0.023). Recursive feature elimination (RFE) selected the most relevant features (AUC = 0.91, 95 % CI: 0.79–1.00, <em>p</em> &lt; 0.001). Despite this, RFE failed to generalise well to the external cohort (AUC = 0.52, 95 % CI: 0.42–0.60, <em>p</em> = 1). Meanwhile, the PSO–adaptive LASSO combination maintained a robust AUC = 0.78 (95 % CI: 0.70–0.85, <em>p</em> &lt; 0.001), indicating superior generalisability.</div></div><div><h3>Conclusions</h3><div>The explainable PSO–adaptive LASSO feature selection method provides generalisable radiogenomic signatures associated with HPV status in OPSCC, outperforming other feature selection approaches. This combination may serve as a robust strategy for developing transferable models in radiogenomics.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109204"},"PeriodicalIF":4.8,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145780469","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":"INTELLI-PVA: Informative sample annotation-based contrastive active learning for cross-domain patient-ventilator asynchrony detection","authors":"Lingwei Zhang ,&nbsp;Xue Feng ,&nbsp;Fei Lu ,&nbsp;Zepeng Ding ,&nbsp;Jiayi Yang ,&nbsp;Luping Fang ,&nbsp;Gangmin Ning ,&nbsp;Shuohui Yuan ,&nbsp;Huiqing Ge ,&nbsp;Qing Pan","doi":"10.1016/j.cmpb.2025.109203","DOIUrl":"10.1016/j.cmpb.2025.109203","url":null,"abstract":"<div><h3>Background and objective</h3><div>Patient-ventilator asynchrony (PVA) is prevalent in mechanically ventilated patients and adversely impacts clinical outcomes, but its real-time detection remains challenging. While artificial intelligence (AI) systems show promise for PVA detection, their cross-domain generalization faces two major limitations: variability in patient-ventilator interactions across different clinical settings, and morphological overlap between PVA types. These challenges necessitate specialized AI solutions rather than conventional re-annotation approaches.</div></div><div><h3>Methods</h3><div>We present the INTELLI-PVA framework for efficient cross-domain PVA detection on eight types. First, a hybrid two-stage PVA classifier was developed. A deep learning model, pre-trained on unannotated data using contrastive learning and fine-tuned using annotated data, identified four morphologically defined compound PVA types, each encompassing a reverse triggering (RT) and a non-RT type. A subsequent rule-based algorithm differentiated the subtypes within each compound type according to their triggering signatures. Then, the model was adapted to the target domain through an iterative active learning cycle, which selected the most informative samples for expert annotation and used them to fine-tune the model.</div></div><div><h3>Results</h3><div>Established and validated on data from two centers encompassing 1190 patients and 124.975 million respiratory cycles, INTELLI-PVA demonstrates superior detection performance (average F1-score: 0.849) in classifying the eight PVA classes using only 1000 annotated samples per target domain, and achieves respiratory therapist-level recognition ability (average Cohen's κ=0.850) across unseen ventilator configurations and patient demographics.</div></div><div><h3>Conclusions</h3><div>INTELLI-PVA achieves high-accuracy, cross-domain PVA detection with minimal annotation burden, establishing a practical and efficient pathway for deploying AI-assisted ventilation monitoring in diverse clinical settings.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109203"},"PeriodicalIF":4.8,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734016","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":"Cluster-Based Insights into Cardiovascular and Autonomic Responses to Head-Up Tilt in Hypertension","authors":"Jia Hui Ooi ,&nbsp;Choon-Hian Goh ,&nbsp;Ahmadreza Argha ,&nbsp;Maw Pin Tan ,&nbsp;Hooi Chin Beh ,&nbsp;Nor Ashikin Md Sari ,&nbsp;Nigel H. Lovell ,&nbsp;Einly Lim","doi":"10.1016/j.cmpb.2025.109202","DOIUrl":"10.1016/j.cmpb.2025.109202","url":null,"abstract":"<div><h3>Background</h3><div>Cardiovascular dysfunction including vascular and cardiac remodeling, and autonomic dysfunction, can emerge before the clinical diagnosis for hypertension, often involving subtle impairments in autonomic regulation and hemodynamic adaptation. This study applied a data-driven clustering approach to uncover latent cardiovascular response phenotypes from autonomic and hemodynamic adaptations during a 70° head-up tilt (HUT) test in hypertensive and normotensive participants. This study aimed to reveal hidden autonomic-structural mechanisms and to provide new physiological insight into early cardiovascular regulation.</div></div><div><h3>Methods</h3><div>Continuous hemodynamic measurements during a 10-min HUT test and baseline echocardiographic assessment were performed. After excluding poor-quality data, 40 hypertensive and 32 normotensive participants were analyzed using K-means, Fuzzy C-means and Hierarchical clustering, with dimensionality reduction via principal component analysis, uniform manifold approximation and projection (UMAP). The UMAP + <em>K</em>-means model showed the best clustering performance and was selected for further analysis.</div></div><div><h3>Results</h3><div>Three distinct compensatory mechanisms were identified. Cluster 1 exhibited sympathetic-mediated vasoconstriction resembling the normotensive profile. Cluster 2 showed an enhanced chronotropic response with impaired vasoconstriction, the lowest complexity index, and signs of cardiac remodeling. Cluster 3 demonstrated attenuated reflex-mediated adjustments but preserved complexity, accompanied by more advanced cardiac-vascular remodeling. Clusters 2 and 3, predominantly hypertensive participants, reflected differing combinations of autonomic and structural adaptation.</div></div><div><h3>Conclusions</h3><div>This physiology-driven approach uncovered hidden cardiovascular phenotypes beyond conventional diagnostic categories. These findings highlight the potential of functional profiling for advancing early understanding of altered cardiovascular regulation and guiding future studies toward targeted strategies for cardiovascular risk reduction and hypertension management.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109202"},"PeriodicalIF":4.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787103","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 comparative study of computer vision models for oral cancer detection from oral photographs","authors":"Jérôme de Chauveron ,&nbsp;Chenyu Zha ,&nbsp;Géraldine Lescaille ,&nbsp;Caroline Shaar-Chneker ,&nbsp;Isabelle Brocheriou ,&nbsp;Laurent Wendling ,&nbsp;Camille Kurtz ,&nbsp;Juliette Rochefort","doi":"10.1016/j.cmpb.2025.109198","DOIUrl":"10.1016/j.cmpb.2025.109198","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Early detection of oral cavity cancers is critical for improving patient survival rates and treatment efficacy. In this context, this study evaluates the potential of computer vision models as diagnostic tools for identifying cancerous lesions from oral cavity photographs.</div></div><div><h3>Methods:</h3><div>A comparative study was conducted using modern deep learning-based object detection models to detect lesions, such as squamous cell carcinoma, from a biopsy-proven dataset of oral lesion photography. A comprehensive workflow was developed to evaluate and compare these models, including fine-tuning, hyperparameters optimization, and performances assessment using precision, sensitivity, and specificity metrics. Additionally, the various parameters influencing the model were systematically measured and analyzed, distinguishing it from previous studies and providing a novel and comprehensive assessment of detection methodologies in the field.</div></div><div><h3>Results:</h3><div>The studied models demonstrated high performance in a single-class detection setting, effectively localizing oral lesions with promising precision. However, in a two-class detection setting, distinguishing between malignant and benign lesions proved challenging, indicating a need for further refinement.</div></div><div><h3>Conclusions:</h3><div>This study underscores the potential of artificial intelligence in aiding early detection of oral cancers while identifying areas for improvement (notably: small lesion detection and distinguishing between malignant and benign). These findings provide a foundation for advancing medical computer vision tools to support early diagnosis and improve patient outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109198"},"PeriodicalIF":4.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734017","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}