Computers in biology and medicine最新文献

{"title":"Generating synthetic CT images from unpaired head and neck CBCT images and validating the importance of detailed nasal cavity acquisition through simulations.","authors":"Susie Ryu, Jun Hong Kim, Yoon Jeong Choi, Joon Sang Lee","doi":"10.1016/j.compbiomed.2024.109568","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109568","url":null,"abstract":"<p><strong>Background and objective: </strong>Computed tomography (CT) of the head and neck is crucial for diagnosing internal structures. The demand for substituting traditional CT with cone beam CT (CBCT) exists because of its cost-effectiveness and reduced radiation exposure. However, CBCT cannot accurately depict airway shapes owing to image noise. This study proposes a strategy utilizing a cycle-consistent generative adversarial network (cycleGAN) for denoising CBCT images with various loss functions and augmentation strategies, resulting in the generation of denoised synthetic CT (sCT) images. Furthermore, through a rule-based approach, we were able to automatically segment the upper airway in sCT images with high accuracy. Additionally, we conducted an analysis of the impact of finely segmented nasal cavities on airflow using computational fluid dynamics (CFD).</p><p><strong>Methods: </strong>We trained the cycleGAN model using various loss functions and compared the quality of the sCT images generated by each model. We improved the artifact removal performance by incorporating CT images with added Gaussian noise augmentation into the training dataset. We developed a rule-based automatic segmentation methodology using threshold and watershed algorithms to compare the accuracy of airway segmentation for noise-reduced sCT and original CBCT. Furthermore, we validated the significance of the nasal cavity by conducting CFD based on automatically segmented shapes obtained from sCT.</p><p><strong>Result: </strong>The generated sCT images exhibited improved quality, with the mean absolute error decreasing from 161.60 to 100.54, peak signal-to-noise ratio increasing from 22.33 to 28.65, and structural similarity index map increasing from 0.617 to 0.865. Furthermore, by comparing the airway segmentation performances of CBCT and sCT using our proposed automatic rule-based algorithm, the Dice score improved from 0.849 to 0.960. Airway segmentation performance is closely associated with the accuracy of fluid dynamics simulations. Detailed airway segmentation is crucial for altering flow dynamics and contributes significantly to diagnostics.</p><p><strong>Conclusion: </strong>Our deep learning methodology enhances the image quality of CBCT to provide anatomical information to medical professionals and enables precise and accurate biomechanical analysis. This allows clinicians to obtain precise quantitative metrics and facilitates accurate assessment.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109568"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863671","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":"Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.","authors":"Meng Zhou, Matthias W Wagner, Uri Tabori, Cynthia Hawkins, Birgit B Ertl-Wagner, Farzad Khalvati","doi":"10.1016/j.compbiomed.2024.109502","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109502","url":null,"abstract":"<p><p>Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes. In this work, we present a novel framework that uses vector-quantization GAN and a transformer incorporating masked token modeling to generate high-resolution and diverse 3D brain tumor ROIs that can be used as additional data for tumor ROI classification. We apply our method to two imbalanced datasets where we augment the minority class: (1) low-grade glioma (LGG) ROIs from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 dataset; (2) BRAF V600E Mutation genetic marker tumor ROIs from the internal pediatric LGG (pLGG) dataset. We show that the proposed method outperforms various baseline models qualitatively and quantitatively. The generated data was used to balance the data to classify brain tumor types. Our approach demonstrates superior performance, surpassing baseline models by 6.4% in the area under the ROC curve (AUC) on the BraTS 2019 dataset and 4.3% in the AUC on the internal pLGG dataset. The results indicate the generated tumor ROIs can effectively address the imbalanced data problem. Our proposed method has the potential to facilitate an accurate diagnosis of rare brain tumors using MRI scans.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109502"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863631","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":"Graph convolution networks model identifies and quantifies gene and cancer specific transcriptome signatures of cancer driver events.","authors":"Gil Ben Cohen, Adar Yaacov, Yishai Ben Zvi, Ranel Loutati, Natan Lishinsky, Jakob Landau, Tom Hope, Aron Popovzter, Shai Rosenberg","doi":"10.1016/j.compbiomed.2024.109491","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109491","url":null,"abstract":"<p><strong>Background: </strong>The identification and drug targeting of cancer causing (driver) genetic alterations has seen immense improvement in recent years, with many new targeted therapies developed. However, identifying, prioritizing, and treating genetic alterations is insufficient for most cancer patients. Current clinical practices rely mainly on DNA level mutational analyses, which in many cases fail to identify treatable driver events. Arguably, signal strength may determine cell fate more than the mutational status that initiated it. The use of transcriptomics, a complex and highly informative representation of cellular and tumor state, had been suggested to enhance diagnostics and treatment successes. A gene-expression based model trained over known genetic alterations could improve identification and quantification of cancer related biological aberrations' signal strength.</p><p><strong>Methods: </strong>We present STAMP (Signatures in Transcriptome Associated with Mutated Protein), a Graph Convolution Networks (GCN) based framework for the identification of gene expression signatures related to cancer driver events. STAMP was trained to identify the p53 dysfunction of cancer samples from gene expression, utilizing comprehensive curated graph structures of gene interactions. Predictions were modified for generating a quantitative score to rank the severity of a driver event in each sample. STAMP was then extended to almost 300 tumor type-specific predictive models for important cancer genes/pathways, by training to identify well-established driver events' annotations from the literature.</p><p><strong>Results: </strong>STAMP achieved very high AUC on unseen data across several tumor types and on an independent cohort. The framework was validated on p53 related genetic and clinical characteristics, including the effect of Variants of Unknown Significance, and showed strong correlation with protein function. For genes and tumor types where targeted therapy is available, STAMP showed correlation with drugs sensitivity (IC50) in an independent cell line database. It managed to stratify drug effect on samples with similar mutational profiles. STAMP was validated for drug-response prediction in clinical patients' cohorts, improving over a state-of-the-art method and suggesting potential biomarkers for cancer treatments.</p><p><strong>Conclusions: </strong>The STAMP models provide a learning framework that successfully identifies and quantifies driver events' signal strength, showing utility in portraying the molecular landscape of tumors based on transcriptomics. Importantly, STAMP manifested the ability to improve targeted therapy selection and hence can contribute to better treatment.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109491"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863673","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":"3d-printed sacral reconstruction prosthesis from multiscale topology optimization: A comprehensive numerical assessment of mechanical stability.","authors":"Naruporn Jitkla, Aingfa Pinyonitikasem, Piyatida Wiwatsuwan, Sutipat Pairojboriboon, Patcharapit Promoppatum","doi":"10.1016/j.compbiomed.2024.109562","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109562","url":null,"abstract":"<p><p>Sacral chordoma, an invasive tumor, necessitates surgical removal of the tumor and the affected region of the sacrum, disrupting the spinopelvic connection. Conventional reconstruction methods, relying on rod and screw systems, often face challenges such as rod failure, sub-optimal stability, and limited osseointegration. This study proposes a novel design for a porous-based sacral reconstruction prosthesis. The design framework involves a two-step topology optimization (TO) process. The first TO step is utilized to obtain the external shape of a patient-specific prosthesis, while the second TO step determines varied density fields. These fields are later integrated with graded Gyroid structures to generate the porous-based sacral prosthesis. Finite element simulations reveal several benefits of the newly developed device. Firstly, considering only solid-based TO tends to result in a highly rigid spinal movement, which may not be entirely favorable. However, the porous-based technique allows for a wider design space, enabling the sacral device's stiffness to be more comprehensively engineered. Secondly, with porous integration, the prosthesis shows potential for promoting bone integration over time, thereby providing further biological fixation and improving long-term structural stability. Thirdly, the porous-based prosthesis outperforms conventional methods such as four-rod reconstruction (FRR) and four-rod plus anterior column reconstruction (FRACR) by reducing maximum von Mises stress in the instruments by approximately 50-80 %. In summary, this study demonstrates how a two-step TO framework can create a superior sacral prosthesis, enhancing its mechanical performance and impact on spinopelvic stability. This suggests potential improvement for similar orthopedic devices in the future.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109562"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863683","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":"Comprehensive annotation of mutations in hallmark genes insights into structural and functional implications.","authors":"Ali F Alsulami","doi":"10.1016/j.compbiomed.2024.109588","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109588","url":null,"abstract":"<p><p>Understanding the multifaceted role of hallmark gene mutations in cancer progression is critical for developing targeted therapies. This study comprehensively analyses 344 hallmark gene mutations by mapping them to their three-dimensional protein structures using PDB data and AlphaFold models. Mutations were classified based on their locations, such as protein interfaces, ligand-binding sites, dimer interfaces, protein-DNA interfaces, and core regions. The results reveal that highly frequent mutations are located on the ligand-binding site and protein interface, highlighting their significant impact on protein function and interactions. This holistic approach bridges gaps in existing research, offering insights into the structural impacts of genetic alterations in hallmark genes, thereby informing more effective therapeutic strategies.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109588"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863612","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":"Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma.","authors":"Cleverson Vieira, Leonardo Rocha, Marcelo Guimarães, Diego Dias","doi":"10.1016/j.compbiomed.2024.109556","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109556","url":null,"abstract":"<p><p>Machine learning models are widely applied across diverse fields, including nearly all segments of human activity. In healthcare, artificial intelligence techniques have revolutionized disease diagnosis, particularly in image classification. Although these models have achieved significant results, their lack of explainability has limited widespread adoption in clinical practice. In medical environments, understanding AI model decisions is essential not only for healthcare professionals' trust but also for regulatory compliance, patient safety, and accountability in case of failures. Glaucoma, a neurodegenerative eye disease, can lead to irreversible blindness, making early detection crucial for preventing vision loss. Automated glaucoma detection has been a focus of intensive research in computer vision, with numerous studies proposing the use of convolutional neural networks (CNNs) to analyze retinal fundus images and diagnose the disease automatically. However, these models often lack the necessary explainability, which is essential for ophthalmologists to understand and justify their decisions to patients. This paper explores and applies explainable artificial intelligence (XAI) techniques to different CNN architectures for glaucoma classification, comparing which explanation technique offers the best interpretive resources for clinical diagnosis. We propose a new approach, SCIM (SHAP-CAM Interpretable Mapping), which has shown promising results. The experiments were conducted with an ophthalmology specialist who highlighted that CAM-based interpretability, applied to the VGG16 and VGG19 architectures, stands out as the most effective resource for promoting interpretability and supporting diagnosis.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109556"},"PeriodicalIF":7.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863613","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":"Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.","authors":"Priyanka Belwal, Surendra Singh","doi":"10.1016/j.compbiomed.2024.109530","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109530","url":null,"abstract":"<p><p>Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a systematic literature review (SLR) focused on deep learning methods for the detection and analysis of multiple sclerosis (MS) using magnetic resonance imaging (MRI). The initial search identified 401 articles, which were rigorously screened, a selection of 82 highly relevant studies. These selected studies primarily concentrate on key areas such as multiple sclerosis, deep learning, convolutional neural networks (CNN), lesion segmentation, and classification, reflecting their alignment with the current state of the art. This review comprehensively examines diverse deep-learning approaches for MS detection and analysis, offering a valuable resource for researchers. Additionally, it presents key insights by summarizing these DL techniques for MS detection and analysis using MRI in a structured tabular format.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109530"},"PeriodicalIF":7.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853353","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":"Exploring quinoline-type inhibitors of ergosterol biosynthesis: Binding mechanism investigation via molecular docking, pharmacophore mapping, and dynamics simulation approaches.","authors":"Gustavo A Barraza, Julio Román Maza, Vladimir V Kouznetsov, Carlos Mario Meléndez Gómez","doi":"10.1016/j.compbiomed.2024.109524","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109524","url":null,"abstract":"<p><p>Drug-resistant fungal infections pose a formidable challenge in healthcare, attributed to ergosterol production as a key mechanism of resistance. It is therefore imperative to target this pathway for effective therapeutic interventions. In this study, we have analyzed the binding mode of twelve quinoline derivatives known to be effective against various Candida species, Microsporum gypseum, and Cryptococcus neoformans. Employing molecular docking techniques, pharmacological modeling, and molecular dynamics, we have delved into interactions with Erg1, Erg11, and Erg24 proteins, crucial in ergosterol biosynthesis. Our analysis unveiled critical interactions that facilitate the docking and stabilization of C-2-substituted quinoline derivatives on these proteins, highlighting their potential as regulators of ergosterol synthesis. Furthermore, complexes formed with Erg1 ^… 8 (MIC = 125 μg/mL) and Erg24 ^… 4 (MIC = 62 μg/mL) showed higher affinity and stability during the docking process, pointing to their promising role as regulatory agents of these proteins. This in silico approach provides insights into potential pathways to combat drug-resistant fungal infections.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109524"},"PeriodicalIF":7.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853358","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":"Knock-knee diagnosis in Chinese adolescents: Expert evaluation and defensive strategies in image analysis - A population study.","authors":"Meiqi Wei, Zongnan Lv, Deyu Meng, Shichun He, Guang Yang, Ziheng Wang","doi":"10.1016/j.compbiomed.2024.109513","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109513","url":null,"abstract":"<p><strong>Background: </strong>Knock-knee, a prevalent postural deformity problem among adolescents, poses significant challenges to traditional diagnostic methods in terms of complexity, high cost, and radiation risk. Therefore, there is a demand for diagnostic techniques that are more accessible, safe, and non-invasive for knock-knee.</p><p><strong>Methods: </strong>We collected 1519 clear whole-body images from 1689 Chinese adolescents aged 10-19 years as image data, and obtained expert annotations on the presence or absence of knock-knee from three orthopedic surgeons. Utilizing Real-Time Multi-Person Pose Estimation (RTMpose), we manually extracted ten features related Knock-knee to construct the dataset. Regard to model, we employed a defense strategy called BitSqueezing.</p><p><strong>Results: </strong>The proposed model achieved an accuracy of 72.81%, a recall of 62.12%, and an AUC of 76.12%, outperforming the benchmark model that achieved an accuracy of 62.45%, a recall of 43.35%, and an AUC of 76.17%.</p><p><strong>Conclusion: </strong>The proposed model is a promising non-contact, high-performance knock-knee detection method that can overcome the limitations of traditional diagnostic methods. The proposed model can facilitate more accurate and efficient deformity detection and postural correction in adolescents. The proposed model also demonstrates the effectiveness of adversarial defense in improving the reliability and accuracy of pose estimation tasks. Future work should validate the proposed model in larger and more diverse populations, and explore other applications of pose estimation and adversarial defense in deformity detection.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109513"},"PeriodicalIF":7.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853364","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":"UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV.","authors":"Cherie C Y Au-Yeung, Yuen-Ting Cheung, Joshua Y T Cheng, Ken W H Ip, Sau-Dan Lee, Victor Y T Yang, Amy Y T Lau, Chit K C Lee, Peter K H Chong, King Wai Lau, Jurgen T J van Lunenburg, Damon F D Zheng, Brian H M Ho, Crystal Tik, Kingsley K K Ho, Ramesh Rajaby, Chun-Hang Au, Mullin H C Yu, Wing-Kin Sung","doi":"10.1016/j.compbiomed.2024.109560","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2024.109560","url":null,"abstract":"<p><strong>Background: </strong>Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs).</p><p><strong>Results: </strong>The Unified Variant Interpretation Platform (UniVar) is a free web server tool that offers an automated and comprehensive workflow on annotation, filtering and prioritization for SNV, INDEL, CNV and SV collectively to identify disease-causing variants for rare diseases in one interface, ensuring accessibility for users even without programming expertise. To filter common CNVs/SVs, a diverse SV catalogue has been generated, that enables robust filtering of common SVs based on population allele frequency. Through benchmarking our SV catalogue, we showed that it is more complete and accurate than the state-of-the-art SV catalogues. Furthermore, to cope with those patients without detailed clinical information, we have developed a novel computational method that enables variant prioritization from gene panels. Our analysis shows that our approach could prioritize pathogenic variants as effective as using HPO terms assigned by clinicians, which adds value for cases without specific clinically assigned HPO terms. Lastly, through a practical case study of disease-causing compound heterozygous variants across SNV and SV, we demonstrated the uniqueness and effectiveness in variant interpretation of UniVar, edging over any existing interpretation tools.</p><p><strong>Conclusions: </strong>UniVar is a unified and versatile platform that empowers researchers and clinicians to identify and interpret disease-causing variants in rare diseases efficiently through a single holistic interface and without a prerequisite for HPO terms. It is freely available without login and installation at https://univar.live/.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109560"},"PeriodicalIF":7.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863700","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}