Computers in biology and medicine最新文献_第3页

Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-08 DOI: 10.1016/j.compbiomed.2025.109761

Chia-Hsiang Lin , Zi-Chao Leng , Chien-Hsin Yu , Lui Kirtan Deori Bharali , Cheng-Li Lin , Bin-Hsu Mao , Ting-Yuan Tu

{"title":"Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation","authors":"Chia-Hsiang Lin , Zi-Chao Leng , Chien-Hsin Yu , Lui Kirtan Deori Bharali , Cheng-Li Lin , Bin-Hsu Mao , Ting-Yuan Tu","doi":"10.1016/j.compbiomed.2025.109761","DOIUrl":"10.1016/j.compbiomed.2025.109761","url":null,"abstract":"<div><div>Multicellular spheroids (MCSs) are increasingly employed as 3D cell culture models in biomedical research due to their ability to effectively replicate <em>in vivo</em> cell interactions, making them suitable for high-throughput drug screening. Accurate cell counting is critical for data normalization, therapeutic evaluation, and exploration of culture conditions; however, affordable software solutions for 3D cell counting using microscopic images are limited. To fill this gap, we created SUNSHINE, an innovative on-chip analytical workflow that uniquely merges optical clearing, histogram matching (HM)-assisted fluorescence calibration, and simple linear iterative clustering (SLIC) supervoxel segmentation. This tool offers an efficient method for analyzing the characteristics and counts of fluorescence-labeled nuclei within MCSs. While optical clearing improves the penetration depth of microscopic imaging, deeper regions of thicker samples often yield faint fluorescence signals. SUNSHINE resolves this issue through the HM image post-processing algorithm. Moreover, SLIC is an effective alternative to traditional contour-wise segmentation, enabling the identification of irregularly shaped fluorescent nuclei. We found that SUNSHINE generated results comparable to commercial software like Imaris and machine learning (ML)-based tools, such as StarDist and Cellpose, in our analysis of the effects of seeding density and cell type on spheroid growth. We also used it to measure the volume and spatial distribution of nuclei, focusing on the hypoxic and peripheral regions of spheroids. Overall, this study finds that SUNSHINE serves as a valuable and economical approach for characterizing cellular activity and interactions in 3D, diminishing the reliance on costly proprietary software.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109761"},"PeriodicalIF":7.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348442","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}

引用次数: 0

Identifying novel therapeutic targets in cystic fibrosis through advanced single-cell transcriptomics analysis

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-08 DOI: 10.1016/j.compbiomed.2025.109748

George Sun , Yi-Hui Zhou

{"title":"Identifying novel therapeutic targets in cystic fibrosis through advanced single-cell transcriptomics analysis","authors":"George Sun , Yi-Hui Zhou","doi":"10.1016/j.compbiomed.2025.109748","DOIUrl":"10.1016/j.compbiomed.2025.109748","url":null,"abstract":"<div><h3>Background:</h3><div>Lung disease remains a leading cause of morbidity and mortality in individuals with cystic fibrosis (CF). Despite significant advances, the complex molecular mechanisms underlying CF-related airway pathology are not fully understood. Building upon previous single-cell transcriptomics studies in CF patients and healthy controls, this study employs enhanced analytical methodologies to deepen our understanding of CF-associated gene expression.</div></div><div><h3>Methods:</h3><div>We employed advanced single-cell transcriptomics techniques, integrating data from multiple sources and implementing rigorous normalization and mapping strategies using a comprehensive lung reference panel. These sophisticated methods were designed to enhance the accuracy and depth of our analysis, with a focus on elucidating differential gene expression and characterizing co-expression network dynamics associated with cystic fibrosis (CF).</div></div><div><h3>Results:</h3><div>Our analysis uncovered novel genes and regulatory networks that had not been previously associated with CF airway disease. These findings highlight new potential therapeutic targets that could be exploited to develop more effective interventions for managing CF-related lung conditions.</div></div><div><h3>Conclusion:</h3><div>This study provides critical insights into the molecular landscape of CF airway disease, offering new avenues for targeted therapeutic strategies. By identifying key genes and networks involved in CF pathogenesis, our research contributes to the broader efforts to improve the prognosis and quality of life for patients with CF. These discoveries pave the way for future studies aimed at translating these findings into clinical practice.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A numerical treatment through Bayesian regularization neural network for the chickenpox disease model

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-08 DOI: 10.1016/j.compbiomed.2025.109807

Zulqurnain Sabir , Muhammad Athar Mehmood , Muhammad Umar , Soheil Salahshour , Yener Altun , Adnène Arbi , Mohamed R. Ali

{"title":"A numerical treatment through Bayesian regularization neural network for the chickenpox disease model","authors":"Zulqurnain Sabir , Muhammad Athar Mehmood , Muhammad Umar , Soheil Salahshour , Yener Altun , Adnène Arbi , Mohamed R. Ali","doi":"10.1016/j.compbiomed.2025.109807","DOIUrl":"10.1016/j.compbiomed.2025.109807","url":null,"abstract":"<div><h3>Objectives</h3><div>The current research investigations designates the numerical solutions of the chickenpox disease model by applying a proficient optimization framework based on the artificial neural network. The mathematical form of the chickenpox disease model is divided into different categories of individuals, susceptible, vaccinated, infected, exposed, recovered, and infected with/without complications.</div></div><div><h3>Method</h3><div>The construction of neural network is performed by using the single hidden layer and the optimization of Bayesian regularization. A dataset is assembled using the explicit Runge-Kutta technique for reducing the mean square error using the training 76 %, while 12 %, 12 % for validation and testing. The whole stochastic procedure is based on logistic sigmoid fitness function, single hidden layer structure with thirty neurons, along with the optimization capability of Bayesian regularization.</div></div><div><h3>Finding</h3><div>The designed procedure's correctness and reliability is observed by results matching, negligible absolute error around 10<sup>−04</sup> to 10<sup>−06</sup>, regression, error histogram, and state transmission. Moreover, the best performance values based on the mean square error are performed as 10<sup>−09</sup> to 10<sup>−11</sup>.</div></div><div><h3>Novelty</h3><div>The current neural network framework using the construction of a single hidden layer and the optimization of Bayesian regularization is applied first time to solve the chickenpox disease model.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109807"},"PeriodicalIF":7.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348699","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}

引用次数: 0

Optimized Yolov8 feature fusion algorithm for dental disease detection

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-08 DOI: 10.1016/j.compbiomed.2025.109778

Qimeng Wang , Xingfei Zhu , Zhaofei Sun , Bufan Zhang , Jinghu Yu , Shanhua Qian

{"title":"Optimized Yolov8 feature fusion algorithm for dental disease detection","authors":"Qimeng Wang , Xingfei Zhu , Zhaofei Sun , Bufan Zhang , Jinghu Yu , Shanhua Qian","doi":"10.1016/j.compbiomed.2025.109778","DOIUrl":"10.1016/j.compbiomed.2025.109778","url":null,"abstract":"<div><div>In oral panoramic film dental disease detection, image magnification distortion and low contrast often result in unclear details and features of target regions, increasing the difficulty of accurate detection. Although mainstream object detection algorithms have shown excellent performance in various fields, their direct application to dental disease detection has been suboptimal. To address these challenges, this study proposes an improved YEM-SAFN model to enhance the recognition of dental conditions. The proposed model incorporates a novel small-target network structure to address the multi-scale and significant size differences of targets in dental disease detection. Additional detection heads for different scales were introduced to improve the model's ability to recognize various dental diseases effectively. To mitigate the issue of tissue offset or overlap in panoramic dental films, the HCSA attention mechanism was integrated, enabling the model to focus on feature extraction in disease-specific regions. Additionally, a redesigned weighted fusion module enhances the utilization of features across scales, improving the model's feature representation capability. The improved YEM-SAFN algorithm achieves a 3.2 % increase in mAP compared to the original YOLOv8s algorithm, attaining an mAP of 86.7 % and outperforming other mainstream algorithms. This model provides an efficient and accurate method for dental condition identification and diagnosis.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109778"},"PeriodicalIF":7.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348439","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}

引用次数: 0

New drug discovery and Hedonic Q: A new interpretation

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-07 DOI: 10.1016/j.compbiomed.2025.109738

Ömer Tuğsal Doruk

引用次数: 0

Integrative functional logistic regression model for genome-wide association studies

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-06 DOI: 10.1016/j.compbiomed.2025.109766

Wenyuan Sun

{"title":"Integrative functional logistic regression model for genome-wide association studies","authors":"Wenyuan Sun","doi":"10.1016/j.compbiomed.2025.109766","DOIUrl":"10.1016/j.compbiomed.2025.109766","url":null,"abstract":"<div><h3>Background:</h3><div>Progress in rapid genomic sequencing techniques have transformed the field of disease biomarker identification by offering vast genetic information. The complexity of traits is not only influenced by single genetic loci but also by interactions among multiple genetic loci. When the dimensionality of SNP data is large, identifying a significant number of genetic variants associated with diseases becomes extremely challenging. To address these high-dimensionality issues, we employed functional data analysis techniques.</div></div><div><h3>Methods:</h3><div>Because there are a lot of ordered genetic variants spread out across a small space, multiple gene variations are handled as a continuous data set rather than discrete variables in some areas. This paper introduces a novel approach for analyzing the association of multiple genes within a region, by employing an integrative functional logistic regression model.</div></div><div><h3>Results:</h3><div>The proposed technique has shown promising results in both simulation and real data analysis, indicating its ability to generate smooth signals and accurately estimate the coefficients of the function while recognizing the null regions.</div></div><div><h3>Conclusions:</h3><div>Integrative functional logistic regression method adopt functional data analysis and assume that high-dimensional genetic data follow a continuous process. It not only naturally accommodates correlations among adjacent SNPs but also avoids the unstable estimation of a large number of parameters. This is especially desirable with the rapidly increasing dimensions of SNP data but still limited sample size. In summary, the suggested approach offers a valuable new avenue for identifying disease-related genetic variants in GWAS.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109766"},"PeriodicalIF":7.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143317329","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}

引用次数: 0

Automated dysphagia characterization in head and neck cancer patients using videofluoroscopic swallowing studies

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-06 DOI: 10.1016/j.compbiomed.2025.109759

Lucía Cubero , Christophe Tessier , Joël Castelli , Kilian Robert , Renaud de Crevoisier , Franck Jégoux , Javier Pascau , Oscar Acosta

{"title":"Automated dysphagia characterization in head and neck cancer patients using videofluoroscopic swallowing studies","authors":"Lucía Cubero , Christophe Tessier , Joël Castelli , Kilian Robert , Renaud de Crevoisier , Franck Jégoux , Javier Pascau , Oscar Acosta","doi":"10.1016/j.compbiomed.2025.109759","DOIUrl":"10.1016/j.compbiomed.2025.109759","url":null,"abstract":"<div><h3>Background</h3><div>Dysphagia is one of the most common toxicities following head and neck cancer (HNC) radiotherapy (RT). Videofluoroscopic Swallowing Studies (VFSS) are the gold standard for diagnosing and assessing dysphagia, but current evaluation methods are manual, subjective, and time-consuming. This study introduces a novel framework for the automated analysis of VFSS to characterize dysphagia in HNC patients.</div></div><div><h3>Method</h3><div>The proposed methodology integrates three key steps: (i) a deep learning-based labeling framework, trained iteratively to identify ten regions of interest; (ii) extraction of 23 swallowing dynamic parameters, followed by comparison across diverse cohorts; and (iii) machine learning (ML) classification of the extracted parameters into four dysphagia-related impairments.</div></div><div><h3>Results</h3><div>The labeling framework achieved high accuracy, with a mean error of 1.6 pixels across the ten regions of interest in an independent test dataset. Analysis of the extracted parameters revealed significant differences in swallowing dynamics between healthy individuals, HNC patients before and after RT, and patients with non-HNC-related dysphagia. The ML classifiers achieved accuracies ranging from 0.60 to 0.87 for the four dysphagia-related impairments.</div></div><div><h3>Conclusions</h3><div>Despite challenges related to dataset size and VFSS variability, our framework demonstrates substantial potential for automatically identifying ten regions of interest and four dysphagia-related impairments from VFSS. This work sets the foundation for future research aimed at refining dysphagia analysis and characterization using VFSS, particularly in the context of HNC RT.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109759"},"PeriodicalIF":7.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143317357","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}

引用次数: 0

The influence of leaflet flutter of the aortic valve bioprostheses on leaflet calcification and endothelial activation

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-06 DOI: 10.1016/j.compbiomed.2025.109765

Matheus Carvalho Barbosa Costa , Saulo de Freitas Gonçalves , Mário Luis Ferreira da Silva , João Victor Curado Fleury , Rudolf Huebner , Artur Henrique de Freitas Avelar

{"title":"The influence of leaflet flutter of the aortic valve bioprostheses on leaflet calcification and endothelial activation","authors":"Matheus Carvalho Barbosa Costa , Saulo de Freitas Gonçalves , Mário Luis Ferreira da Silva , João Victor Curado Fleury , Rudolf Huebner , Artur Henrique de Freitas Avelar","doi":"10.1016/j.compbiomed.2025.109765","DOIUrl":"10.1016/j.compbiomed.2025.109765","url":null,"abstract":"<div><div>Biological heart valve (BHV) prostheses have a short lifespan, since they are also susceptible to processes that trigger calcification that are observed in native valves. Studies indicate that the development of leaflet flutter may be related to the acceleration of the calcification mechanisms of these prostheses, as well as to the increase in endothelial cell activation. This study aims to evaluate the impact of leaflet flutter on parameters based on wall shear stress, used to verify calcification progression and ”thrombogenic susceptibility”, by performing fluid-structural computational calculations. The Arbitrary Lagrangian–Eulerian method was used to perform the computational analysis in a simplified domain applying physiological boundary conditions. The blood was modeled as a Newtonian fluid and the valve as a hyperelastic and isotropic incompressible material. The time-averaged wall shear stress (TAWSS), the relative residence time (RRT), the oscillatory shear index (OSI), and the endothelial cell activation potential (ECAP) were calculated to verify the impact of flutter on calcification and thrombogenesis of the prostheses. The results indicate that the accentuated curvatures developed after the oscillations started in the belly regions and between the free edge and the commissure of the leaflets are connected to the increase in OSI, RRT, and ECAP. Therefore, leaflet flutter is responsible for increasing mineral accumulation and platelet adhesion. Furthermore, the distributions of these quantities were different for each of the leaflets. This work aims to improve the understanding of the mechanisms involved in BHV degradation and provides supports for the manufacture of more durable prostheses.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109765"},"PeriodicalIF":7.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143317331","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}

引用次数: 0

QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble-supervised segmentation of medical images

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-06 DOI: 10.1016/j.compbiomed.2025.109762

Thien B. Nguyen-Tat , Hoang-An Vo , Phuoc-Sang Dang

{"title":"QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble-supervised segmentation of medical images","authors":"Thien B. Nguyen-Tat , Hoang-An Vo , Phuoc-Sang Dang","doi":"10.1016/j.compbiomed.2025.109762","DOIUrl":"10.1016/j.compbiomed.2025.109762","url":null,"abstract":"<div><div>The deployment of advanced deep learning models for medical image segmentation is often constrained by the requirement for extensively annotated datasets. Weakly-supervised learning, which allows less precise labels, has become a promising solution to this challenge. Building on this approach, we propose QMaxViT-Unet+, a novel framework for scribble-supervised medical image segmentation. This framework is built on the U-Net architecture, with the encoder and decoder replaced by Multi-Axis Vision Transformer (MaxViT) blocks. These blocks enhance the model’s ability to learn local and global features efficiently. Additionally, our approach integrates a query-based Transformer decoder to refine features and an edge enhancement module to compensate for the limited boundary information in the scribble label. We evaluate the proposed QMaxViT-Unet+ on four public datasets focused on cardiac structures, colorectal polyps, and breast cancer: ACDC, MS-CMRSeg, SUN-SEG, and BUSI. Evaluation metrics include the Dice similarity coefficient (DSC) and the 95th percentile of Hausdorff distance (HD95). Experimental results show that QMaxViT-Unet+ achieves 89.1% DSC and 1.316 mm HD95 on ACDC, 88.4% DSC and 2.226 mm HD95 on MS-CMRSeg, 71.4% DSC and 4.996 mm HD95 on SUN-SEG, and 69.4% DSC and 50.122 mm HD95 on BUSI. These results demonstrate that our method outperforms existing approaches in terms of accuracy, robustness, and efficiency while remaining competitive with fully-supervised learning approaches. This makes it ideal for medical image analysis, where high-quality annotations are often scarce and require significant effort and expense. The code is available at <span><span>https://github.com/anpc849/QMaxViT-Unet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109762"},"PeriodicalIF":7.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143317328","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}

引用次数: 0

Critical methodological factors influencing the accuracy of intraoral scanners in digital dentistry research

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-02-06 DOI: 10.1016/j.compbiomed.2025.109780

Petros Mourouzis

{"title":"Critical methodological factors influencing the accuracy of intraoral scanners in digital dentistry research","authors":"Petros Mourouzis","doi":"10.1016/j.compbiomed.2025.109780","DOIUrl":"10.1016/j.compbiomed.2025.109780","url":null,"abstract":"<div><div>This in vitro study aimed to identify the key methodological factors influencing the accuracy of intraoral scanners (IOS). The primary factors analyzed included the length of the scanned area, the total number of alignment points, the software used for analysis, and the operator's expertise. Three IOS systems were assessed—CEREC Primescan, Trios 3, and Omnicam—along with a laboratory desktop scanner (inEos X5). Scans were performed on a mandibular typodont, with the Root Mean Square (RMS) error used to measure the discrepancies between reference and experimental scans. The results indicated that the length of the scanned area significantly affected the RMS values, with full-arch scans producing greater errors compared to those of quadrant scans. Additionally, the total number of alignment points in the standard tessellation language files positively influenced accuracy, although improvements plateaued beyond 20 points. The choice of processing software also impacted accuracy, with Geomagic Control X yielding significantly lower RMS values than those of MeshLab and CloudCompare. Finally, user expertise played a significant role in scanning accuracy, with the experience user achieving more precise results, especially when using the Trios 3 scanner.</div><div>Thus, the length of the scan, number of alignment points, software tools, and operator expertise significantly influence the accuracy of IOS, highlighting the importance of considering these methodological factors in both clinical and research settings for digital impressions.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109780"},"PeriodicalIF":7.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143317330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0