Computers in biology and medicine最新文献_第5页

Detection, identification and removing of artifacts from sEMG signals: Current studies and future challenges. 从表面肌电信号中检测、识别和去除伪影：当前研究和未来挑战。

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-10 DOI: 10.1016/j.compbiomed.2025.109651

Mohamed Ait Yous, Said Agounad, Siham Elbaz

{"title":"Detection, identification and removing of artifacts from sEMG signals: Current studies and future challenges.","authors":"Mohamed Ait Yous, Said Agounad, Siham Elbaz","doi":"10.1016/j.compbiomed.2025.109651","DOIUrl":"10.1016/j.compbiomed.2025.109651","url":null,"abstract":"<p><p>Surface electromyography (sEMG), a non-invasive technique, offers the ability to identify insights into the activities of muscles in the form of electrical pulses. During the process of recording, the sEMG signals frequently become contaminated by a multitude of different artifacts, the origin of which may be attributed to numerous sources. These artifacts affect the reliability and accuracy of the pure sEMG activity, and subsequently reduce the quality of analysis and interpretation. This can lead to a misinterpretation of sEMG signals, incorrect diagnostic, or a false decision in the case of human-machine interfaces (HMI), etc. Currently, several approaches have been developed to remove or reduce the effect of artifacts on the sEMG activity. In this paper, a comprehensive review of the current studies dealing with identification, detection, and removal of artifacts from sEMG signals is proposed. In addition, this study presents different features used to characterize the artifacts from that of the clean sEMG recordings. Finally, in order to improve the quality of denoising methods, the associated challenges of detection and artifact removal approaches are discussed to be addressed carefully in the future works.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109651"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964085","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

CA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction.

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-25 DOI: 10.1016/j.compbiomed.2025.109655

Ting Zhang, Changqing Yu, Shanwen Zhang

{"title":"CA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction.","authors":"Ting Zhang, Changqing Yu, Shanwen Zhang","doi":"10.1016/j.compbiomed.2025.109655","DOIUrl":"10.1016/j.compbiomed.2025.109655","url":null,"abstract":"<p><p>Accurate and efficient drug-drug interaction extraction (DDIE) from the medical corpus is essential for pharmacovigilance, drug therapy and drug development. To solve the problems of unbalance dataset and lack of accurate manual annotations in DDIE, a cross-attention guided Siamese quantum BiGRU (CA-SQBG) is constructed to improve feature representation learning ability for DDIE. It mainly consists of two quantum BiGRUs (QBiGRUs) and a cross-attention, where two QBiGRUs are Siamese implemented in a variational quantum environment to learn the contextual semantic feature representation of drug pairs, cross-attention is employed to learn mutual information from the Siamese QBiGRUs, which in turn allows the two modules to extract DDI more collaboratively. Unlike BiGRU, Siamese QBiGRUs uses internal and external dependencies in quaternion algebra to map DDI correlations within and between multidimensional features, whereas BiGRU can only capture dependencies within sequences. CA-SQBG is evaluated on the DDIExtraction2013 dataset, and the results demonstrate that it can effectively capture the inter- and intra-dependencies within multimodal features with few parameters, using a small number of training samples, and is superior to the most advanced DDIE methods. CA-SQBG offers potential applications for quantum computing and Siamese networks in the field of DDIE. Code is available on https://github.com/xaycq/CA-SQBG.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109655"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045913","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

Preoperative assessment of patients at risk of postoperative respiratory depression

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 DOI: 10.1016/j.compbiomed.2025.109805

Atousa Assadi , Frances Chung , Azadeh Yadollahi

{"title":"Preoperative assessment of patients at risk of postoperative respiratory depression","authors":"Atousa Assadi , Frances Chung , Azadeh Yadollahi","doi":"10.1016/j.compbiomed.2025.109805","DOIUrl":"10.1016/j.compbiomed.2025.109805","url":null,"abstract":"<div><div>Respiratory depression during sleep is a major health challenge after surgery. The main cause is reduction in breathing due to opioids, which are commonly used for management of postoperative pain. The consequences are hypoxemia and hypercapnia, which may increase the risk of cardiovascular complications, mortality, and healthcare utilization. Identifying individuals who are at risk of postoperative respiratory depression prior to the surgery can help guide the perioperative care to reduce adverse outcomes. In this project, we developed a risk assessment model to identify individuals at risk of postoperative respiratory depression prior to the surgery, based on the demographics and changes in preoperative overnight oxyhemoglobin saturation (SpO<sub>2</sub>) levels. To achieve this, we retrospectively analyzed SpO<sub>2</sub> signals of 159 patients, which were recorded continuously preoperatively and on the third night after surgery. Respiratory depression was defined as postoperative episodes where SpO<sub>2</sub> was ≤85% for more than 3 minutes. From preoperative SpO<sub>2</sub> signals, we extracted features to characterize overnight SpO<sub>2</sub> and desaturation episodes. We streamlined a systematic process for feature selection and model development using a nested cross-validation pipeline. Our results indicated that random forest, XGBoost, and Naïve bayes demonstrated the highest predictive performance, consistently surpassing the recent available PRODIGY model. These findings suggest that demographics and preoperative SpO<sub>2</sub> characteristics can preoperatively identify individuals at high-risk of postoperative respiratory depression, which offers a non-invasive and cost-effective method of monitoring respiratory health.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109805"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520923","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

Hemodynamics indicates differences between patients with and without a stroke outcome after left ventricular assist device implantation

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 DOI: 10.1016/j.compbiomed.2025.109877

Akshita Sahni , Sreeparna Majee , Jay D. Pal , Erin E. McIntyre , Kelly Cao , Debanjan Mukherjee

{"title":"Hemodynamics indicates differences between patients with and without a stroke outcome after left ventricular assist device implantation","authors":"Akshita Sahni , Sreeparna Majee , Jay D. Pal , Erin E. McIntyre , Kelly Cao , Debanjan Mukherjee","doi":"10.1016/j.compbiomed.2025.109877","DOIUrl":"10.1016/j.compbiomed.2025.109877","url":null,"abstract":"<div><div>Stroke remains a leading cause of complications and mortality in heart failure patients treated with a Left Ventricular Assist Device (LVAD). Hemodynamics plays a central role underlying post-LVAD stroke risk and etiology. Yet, detailed quantitative assessment of hemodynamic variables and their relation to stroke outcomes in patients on LVAD support remains a challenge. Modalities for pre-implantation assessment of post-implantation hemodynamics can help address this challenge. We present an <em>in silico</em> hemodynamics analysis for a digital twin cohort 12 patients on LVAD support; 6 with reported stroke outcomes and 6 without. For each patient we created a post-implant twin with the LVAD outflow graft reconstructed from cardiac-gated CT images; and a pre-implant twin of an estimated baseline flow by removing the LVAD outflow graft and driving flow from the aortic valve opening. Hemodynamics was characterized using descriptors for helical flow, vortex generation, and wall shear stress. We observed higher average values for descriptors of positive helical flow, vortex generation, and wall shear stress, across the 6 cases with stroke outcomes when compared with cases without stroke. When the descriptors for LVAD-driven flow were compared against estimated pre-implantation flow, extent of positive helicity was higher, and vorticity and wall shear were lower in cases with stroke compared to those without. Our study suggests that quantitative analysis of hemodynamics after LVAD implantation; and hemodynamic alterations from a pre-implant flow scenario, can potentially reveal hidden information linked to stroke outcomes during LVAD support. This has broad implications on understanding stroke etiology; and using patient digital twins for LVAD treatment planning, surgical optimization, and efficacy assessment.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109877"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520924","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

KNU-DTI: KNowledge United Drug-Target Interaction prediction

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 DOI: 10.1016/j.compbiomed.2025.109927

Ryong Heo , Dahyeon Lee , Byung Ju Kim , Sangmin Seo , Sanghyun Park , Chihyun Park

{"title":"KNU-DTI: KNowledge United Drug-Target Interaction prediction","authors":"Ryong Heo , Dahyeon Lee , Byung Ju Kim , Sangmin Seo , Sanghyun Park , Chihyun Park","doi":"10.1016/j.compbiomed.2025.109927","DOIUrl":"10.1016/j.compbiomed.2025.109927","url":null,"abstract":"<div><h3>Motivation</h3><div>Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks.</div></div><div><h3>Results</h3><div>We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109927"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520926","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

Association of Murray's law with atherosclerosis risk: Numerical validation of a general scaling law of arterial tree.

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI: 10.1016/j.compbiomed.2025.109741

Mohammad Shumal, Mohsen Saghafian, Ebrahim Shirani, Mahdi Nili-AhmadAbadi

{"title":"Association of Murray's law with atherosclerosis risk: Numerical validation of a general scaling law of arterial tree.","authors":"Mohammad Shumal, Mohsen Saghafian, Ebrahim Shirani, Mahdi Nili-AhmadAbadi","doi":"10.1016/j.compbiomed.2025.109741","DOIUrl":"10.1016/j.compbiomed.2025.109741","url":null,"abstract":"<p><p>Atherogenesis is prone in medium and large-sized vessels, such as the aorta and coronary arteries, where hemodynamic stress is critical. Low and oscillatory wall shear stress contributes significantly to endothelial dysfunction and inflammation. Murray's law minimizes energy expenditure in vascular networks and applies to small arteries. However, its assumptions fail to account for the pulsatile nature of blood flow in larger, atherosclerosis-prone arteries. This study aims to numerically validate a novel general scaling law that extends Murray's law to incorporate pulsatile flow effects and demonstrate its applications in vascular health and artificial graft design. The proposed scaling law establishes an optimal relationship between arterial bifurcation characteristics and pulsatile flow dynamics, applicable throughout the vascular system. This work examines the relationship between deviations from Murray's law and the development of atherosclerosis in both coronary arteries and abdominal aorta bifurcations, explaining observed deviations from Murray's law in these regions. A finite volume method is applied to evaluate flow patterns in coronary arteries and aortoiliac bifurcations, incorporating in vivo pulsatile inflow and average outlet pressure. The results indicate that the proposed scaling law enhances wall shear stress distribution compared to Murray's law, which is characterized by higher wall shear stress and reduced oscillatory shear index. These findings suggest that vessels adhering to this scaling law are less susceptible to atherosclerosis. Furthermore, the results are consistent with clinical morphometric data, underscoring the potential of the proposed scaling law to optimize vascular graft designs, promoting favorable hemodynamic patterns and minimizing the occlusion risk in clinical applications.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109741"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058371","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

A robust ensemble framework for anticancer peptide classification using multi-model voting approach

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 DOI: 10.1016/j.compbiomed.2025.109750

Zeeshan Abbas , Sunyeup Kim , Nangkyeong Lee , Syed Aadil Waheed Kazmi , Seung Won Lee

{"title":"A robust ensemble framework for anticancer peptide classification using multi-model voting approach","authors":"Zeeshan Abbas , Sunyeup Kim , Nangkyeong Lee , Syed Aadil Waheed Kazmi , Seung Won Lee","doi":"10.1016/j.compbiomed.2025.109750","DOIUrl":"10.1016/j.compbiomed.2025.109750","url":null,"abstract":"<div><div>Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a novel machine learning-based framework for ACP classification, integrating multiple feature sets, including sequence composition, physicochemical properties, and embedding features derived from pre-trained language models. We evaluate the performance of various classifiers on benchmark datasets and compare our model against state-of-the-art methods. The results demonstrate that our model outperforms existing methods such as UniDL4BioPep, ACPred-Fuse, and iACP with an accuracy of 75.58%, an AUC of 0.8272, and an MCC of 0.5119. Our approach provides a more balanced sensitivity of 0.7384 and specificity of 0.773, ensuring robust identification of both ACPs and non-ACPs. These findings suggest that incorporating diverse feature sets can significantly enhance ACP classification, potentially facilitating the discovery of novel anticancer peptides for therapeutic applications.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109750"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526724","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

Brain tumour histopathology through the lens of deep learning: A systematic review. 深度学习视角下的脑肿瘤组织病理学：系统综述。

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-08 DOI: 10.1016/j.compbiomed.2024.109642

Chun Kiet Vong, Alan Wang, Mike Dragunow, Thomas I-H Park, Vickie Shim

{"title":"Brain tumour histopathology through the lens of deep learning: A systematic review.","authors":"Chun Kiet Vong, Alan Wang, Mike Dragunow, Thomas I-H Park, Vickie Shim","doi":"10.1016/j.compbiomed.2024.109642","DOIUrl":"10.1016/j.compbiomed.2024.109642","url":null,"abstract":"<p><strong>Problem: </strong>Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's complex pathogenesis.</p><p><strong>Aim: </strong>Conduct a systematic review to investigate how ML/DL techniques have influenced the progression of brain tumour histopathological research, particularly in GBM.</p><p><strong>Methods: </strong>54 eligible studies were collected from the PubMed and ScienceDirect databases, and their information about the types of brain tumour/s used, types of -omics data used with histopathological data, origins of the data, types of ML/DL and its training and evaluation methodologies, and the ML/DL task it was set to perform in the study were extracted to inform us of trends in GBM-related ML/DL-based research.</p><p><strong>Results: </strong>Only 8 GBM-related studies in the eligible utilised ML/DL methodologies to gain deeper insights into GBM pathogenesis by contextualising histological data with -omics data. However, we report that these studies have been published more recently. The most popular ML/DL models used in GBM-related research are the SVM classifier and ResNet-based CNN architecture. Still, a considerable number of studies failed to state training and evaluative methodologies clearly.</p><p><strong>Conclusion: </strong>There is a growing trend towards using ML/DL approaches to uncover relationships between biological and histopathological data to bring new insights into GBM, thus pushing GBM research forward. Much work still needs to be done to properly report the ML/DL methodologies to showcase the models' robustness and generalizability and ensure the models are reproducible.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109642"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945645","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

OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-25 DOI: 10.1016/j.compbiomed.2025.109713

Jing Peng, Qiming He, Chen Wang, Zijun Wang, Siqi Zeng, Qiang Huang, Tian Guan, Yonghong He, Congrong Liu

{"title":"OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.","authors":"Jing Peng, Qiming He, Chen Wang, Zijun Wang, Siqi Zeng, Qiang Huang, Tian Guan, Yonghong He, Congrong Liu","doi":"10.1016/j.compbiomed.2025.109713","DOIUrl":"10.1016/j.compbiomed.2025.109713","url":null,"abstract":"<p><strong>Background: </strong>Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various ovarian cancer-related cells, based on morphological and immunohistochemical pathology images, is deemed an important step. Currently, the lack of a comprehensive deep learning framework for detecting various ovarian cells poses a performance bottleneck in ovarian cancer pathological diagnosis.</p><p><strong>Method: </strong>This paper presents OCDet, an object detection model with channel attention, which achieves comprehensive detection of CD3, CD8, and CD20 positive lymphocytes in immunohistochemical pathology slides, and neutrophils and polyploid giant cancer cells in H&E slides of ovarian cancer. OCDet, utilizing CSPDarkNet as its backbone, incorporates an Efficient Channel Attention module for Resolution-Specified Embedding Refinement and Multi-Resolution Embedding Fusion, enabling the efficient extraction of pathological features.</p><p><strong>Result: </strong>The experiment demonstrated that OCDet performed well in target detection of three types of positive lymphocytes in immunohistochemical images, as well as neutrophils and polyploid giant cancer cells in H&E images. The mAP@0.5 reached 98.82 %, 92.91 %, and 90.49 % respectively, all surpassing other compared models. The ablation experiment further highlighted the superiority of the introduced Efficient Channel Attention (ECA) mechanism.</p><p><strong>Conclusion: </strong>The proposed OCDet enables accurate detection of multiple types of cells in immunohistochemical and morphological pathology images of ovarian cancer, serving as an efficient application tool for pathological diagnosis thereof. The proposed framework has the potential to be further applied to other cancer types.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109713"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045902","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

An efficient heuristic for geometric analysis of cell deformations.

IF 7 2区医学

Computers in biology and medicine Pub Date : 2025-03-01 Epub Date: 2025-01-26 DOI: 10.1016/j.compbiomed.2025.109709

Yaima Paz Soto, Silena Herold Garcia, Ximo Gual-Arnau, Antoni Jaume-I-Capó, Manuel González-Hidalgo

{"title":"An efficient heuristic for geometric analysis of cell deformations.","authors":"Yaima Paz Soto, Silena Herold Garcia, Ximo Gual-Arnau, Antoni Jaume-I-Capó, Manuel González-Hidalgo","doi":"10.1016/j.compbiomed.2025.109709","DOIUrl":"10.1016/j.compbiomed.2025.109709","url":null,"abstract":"<p><p>Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis. Recent studies have proposed various erythrocyte representation and classification methods (Jennifer et al., 2023 [1]). Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space (Epifanio et al., 2020). This approach employs elastic distances between shapes, which are invariant under rotations, translations, scaling, and reparameterizations, ensuring consistent distance measurements regardless of the curves' position, starting point, or traversal speed. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. Aligning shapes to templates before distance computation, a concept successfully applied in areas such as molecular dynamics (Richmond et al., 2004 [2]), and using a fixed parameterization, instead of minimizing distances across all possible parameterizations, simplifies calculations. This strategy achieves 96.03% accuracy rate in both supervised classification and unsupervised clustering. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109709"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051717","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