Bioengineering最新文献

{"title":"A High-Resolution Digital Pathological Image Staining Style Transfer Model Based on Gradient Guidance.","authors":"Yutao Tang, Yuanpin Zhou, Siyu Zhang, Yao Lu","doi":"10.3390/bioengineering12020187","DOIUrl":"10.3390/bioengineering12020187","url":null,"abstract":"<p><p>Digital pathology images have long been regarded as the gold standard for cancer diagnosis in clinical medicine. A highly generalized digital pathological image diagnosis system can provide strong support for cancer diagnosis, help to improve the diagnostic efficiency and accuracy of doctors, and has important research value. The whole slide image of different centers can lead to very large staining differences due to different scanners and dyes, which pose a challenge to the generalization performance of the model application in multi-center data testing. In order to achieve the normalization of multi-center data, this paper proposes a style transfer algorithm based on an adversarial generative network for high-resolution images. The gradient-guided dye migration model proposed in this paper introduces a gradient-enhanced regularized term in the loss function design of the algorithm. A style transfer algorithm was applied to the source data, and the diagnostic performance of the multi-example learning model based on the domain data was significantly improved by validation in the pathological image datasets of two centers. The proposed method improved the AUC of the best classification model from 0.8856 to 0.9243, and another set of experiments improved the AUC from 0.8012 to 0.8313.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive AI Framework for Superior Diagnosis, Cranial Reconstruction, and Implant Generation for Diverse Cranial Defects.","authors":"Mamta Juneja, Ishaan Singla, Aditya Poddar, Nitin Pandey, Aparna Goel, Agrima Sudhir, Pankhuri Bhatia, Gurzafar Singh, Maanya Kharbanda, Amanpreet Kaur, Ira Bhatia, Vipin Gupta, Sukhdeep Singh Dhami, Yvonne Reinwald, Prashant Jindal, Philip Breedon","doi":"10.3390/bioengineering12020188","DOIUrl":"10.3390/bioengineering12020188","url":null,"abstract":"<p><p>Cranioplasty enables the restoration of cranial defects caused by traumatic injuries, brain tumour excisions, or decompressive craniectomies. Conventional methods rely on Computer-Aided Design (CAD) for implant design, which requires significant resources and expertise. Recent advancements in Artificial Intelligence (AI) have improved Computer-Aided Diagnostic systems for accurate and faster cranial reconstruction and implant generation procedures. However, these face inherent limitations, including the limited availability of diverse datasets covering different defect shapes spanning various locations, absence of a comprehensive pipeline integrating the preprocessing of medical images, cranial reconstruction, and implant generation, along with mechanical testing and validation. The proposed framework incorporates a robust preprocessing pipeline for easier processing of Computed Tomography (CT) images through data conversion, denoising, Connected Component Analysis (CCA), and image alignment. At its core is CRIGNet (Cranial Reconstruction and Implant Generation Network), a novel deep learning model rigorously trained on a diverse dataset of 2160 images, which was prepared by simulating cylindrical, cubical, spherical, and triangular prism-shaped defects across five skull regions, ensuring robustness in diagnosing a wide variety of defect patterns. CRIGNet achieved an exceptional reconstruction accuracy with a Dice Similarity Coefficient (DSC) of 0.99, Jaccard Similarity Coefficient (JSC) of 0.98, and Hausdorff distance (HD) of 4.63 mm. The generated implants showed superior geometric accuracy, load-bearing capacity, and gap-free fitment in the defected skull compared to CAD-generated implants. Also, this framework reduced the implant generation processing time from 40-45 min (CAD) to 25-30 s, suggesting its application for a faster turnaround time, enabling decisive clinical support systems.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic-Attention Enhanced DSC-Transformer for Lymph Node Ultrasound Classification and Remote Diagnostics.","authors":"Ying Fu, Shi Tan, Michel Kadoch, Jinghua Zhong, Lifeng Guo, Yangan Zhang, Xiaohong Huang, Xueguang Yuan","doi":"10.3390/bioengineering12020190","DOIUrl":"10.3390/bioengineering12020190","url":null,"abstract":"<p><p>This study presents a novel Semantic-Attention Enhanced Dynamic Swin Convolutional Block Attention Module(CBAM) Transformer (DSC-Transformer) for lymph node ultrasound image classification. The model integrates semantic feature extraction and multi-scale attention mechanisms with the Swin Transformer architecture, enabling efficient processing of diagnostically significant regions while suppressing noise. Key innovations include semantic-driven preprocessing for localized diagnostic focus, adaptive compression for bandwidth-limited scenarios, and multi-scale attention modules for capturing both global anatomical context and local texture details. The model's effectiveness is validated through comprehensive experiments on diverse datasets and Grad-Channel Attention Module (CAM) visualizations, demonstrating superior classification performance while maintaining high efficiency in remote diagnostic settings. This semantic-attention enhancement makes the DSC-Transformer particularly effective for telemedicine applications, representing a significant advancement in AI-driven medical image analysis with broad implications for telehealth deployment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gender Differences in Joint Biomechanics During Obstacle Crossing with Different Heights.","authors":"Chenyan Wang, Yuan Guo, Weijin Du, Zhiqiang Li, Weiyi Chen","doi":"10.3390/bioengineering12020189","DOIUrl":"10.3390/bioengineering12020189","url":null,"abstract":"<p><p>Identifying gender-related gait changes offers valuable insights into the role of gender in motor control. It is anticipated that more difficult gait tasks (obstacle crossing) may reveal gender-specific effects on gait parameters. The present study aimed to explore the gait adaptations of male and female participants when stepping over obstacles of 0 cm, 13 cm, 19 cm, and 26 cm in height. A total of 12 male and 12 female participants were recruited. The Vicon motion capture system and AMTI force plates were utilized to obtain the gait parameters. Moreover, spatiotemporal parameters were investigated. Two-way repeated ANOVA (gender × obstacle height) and three-way repeated ANOVA (gender × obstacle height × leg) were performed to compare gait parameters, respectively. Correlations between maximum joint angle and obstacle height were also evaluated. Significant interactions were observed for leading leg swing time, maximum hip extension angle, maximum knee flexion angle, and maximum ankle plantarflexion angle (gender × obstacle height). There were some differences in gait parameters between males and females in the unobstructed gait, and these changes became more evident as obstacle height increased. This study also identified significant differences in gait parameters between leading and trailing legs when stepping over the obstacle.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intracranial Aneurysm Segmentation with a Dual-Path Fusion Network.","authors":"Ke Wang, Yong Zhang, Bin Fang","doi":"10.3390/bioengineering12020185","DOIUrl":"10.3390/bioengineering12020185","url":null,"abstract":"<p><p>Intracranial aneurysms (IAs), a significant medical concern due to their prevalence and life-threatening nature, pose challenges regarding diagnosis owing to their diminutive and variable morphology. There are currently challenges surrounding automating the segmentation of IAs, which is essential for diagnostic precision. Existing deep learning methods in IAs segmentation tend to emphasize semantic features at the expense of detailed information, potentially compromising segmentation quality. Our research introduces the innovative Dual-Path Fusion Network (DPF-Net), an advanced deep learning architecture crafted to refine IAs segmentation by adeptly incorporating detailed information. DPF-Net, with its unique resolution-preserving detail branch, ensures minimal loss of detail during feature extraction, while its cross-fusion module effectively promotes the connection of semantic information and finer detail features, enhancing segmentation precision. The network also integrates a detail aggregation module for effective fusion of multi-scale detail features. A view fusion strategy is employed to address spatial disruptions in patch generation, thereby improving feature extraction efficiency. Evaluated on the CADA dataset, DPF-Net achieves a remarkable mean Dice similarity coefficient (DSC) of 0.8967, highlighting its potential in automated IAs diagnosis in clinical settings. Furthermore, DPF-Net's outstanding performance on the BraTS 2020 MRI dataset for brain tumor segmentation with a mean DSC of 0.8535 further confirms its robustness and generalizability.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes.","authors":"Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan","doi":"10.3390/bioengineering12020186","DOIUrl":"10.3390/bioengineering12020186","url":null,"abstract":"<p><p>Depression is a common mental health disorder and a leading contributor to mortality and morbidity. Despite several advancements, the current screening methods have limitations in enabling the robust and automated detection of depression, thereby hindering early diagnosis and timely intervention. This study aimed to develop explainable artificial intelligence (AI) models to predict depression using polysomnographic phenotype data, ensuring high predictive performance while providing clear insights into the importance of features influencing the risk of depression. Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. Phenotype data from subjective health questionnaires, clinical assessments, and demographic factors were analyzed. The explainable AI models identified the important features, and their performance was evaluated using cross-validation. The study population, comprising 114 control participants and 39 individuals with depression, was stratified based on validated depression-scoring methods. The proposed explainable AI models achieved an F1-score of 85%, verifying their high reliability in predicting depression. Key features influencing the risk of depression, such as anxiety disorders, sleep efficiency, and demographic factors, offer actionable insights for clinical practice, highlighting the transparency of these models. This study proposed and developed explainable AI models based on polysomnographic phenotype data for the automated detection of depression and verified that these models help improve mental health diagnostics, enabling timely interventions.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of 10 kV/m Electric Field Therapy in a Pressure Injury Model in Rats: An Innovative Preliminary Report.","authors":"Mustafa Soner Özcan, Halil Aşcı, Pınar Karabacak, Eyyüp Sabri Özden, Rümeysa Taner, Özlem Özmen, Muhammet Yusuf Tepebaşı, Selçuk Çömlekçi","doi":"10.3390/bioengineering12020183","DOIUrl":"10.3390/bioengineering12020183","url":null,"abstract":"<p><p><b>Background:</b> Pressure injuries are still an important health problem worldwide, although many therapies have been applied to date. This study aimed to determine the optimal duration of external application of a 10 kV/m direct current (DC, static) electric field in a pressure injury model in rats. <b>Methods:</b> Twelve male Wistar-Albino rats were divided into three groups: Grade-1, Grade-2, and Grade-3. Two round magnets were placed 4 h daily for one day in Grade-1, two days in Grade-2, and three days in Grade-3. Following wound formation, one rat from each group was designated the control, while the other rats were exposed to a 10 kV/m electric field for 15, 30, or 60 min. <b>Results:</b> Histopathological improvements were observed after 15 and 30 min of application, whereas a sharp decrease in the gene expression of growth factors at 30 min revealed that 15 min of application was optimal overall. <b>Conclusions:</b> According to the results of this study, 15 min applications of an external 10 kV/m electric field are promising for providing satisfactory results in wound healing. Further studies should examine in greater detail the effects of electric fields on growth factors and the mechanisms underlying these responses.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IDCC-SAM: A Zero-Shot Approach for Cell Counting in Immunocytochemistry Dataset Using the Segment Anything Model.","authors":"Samuel Fanijo, Ali Jannesari, Julie Dickerson","doi":"10.3390/bioengineering12020184","DOIUrl":"10.3390/bioengineering12020184","url":null,"abstract":"<p><p>Cell counting in immunocytochemistry is vital for biomedical research, supporting the diagnosis and treatment of diseases such as neurological disorders, autoimmune conditions, and cancer. However, traditional counting methods are manual, time-consuming, and error-prone, while deep learning solutions require costly labeled datasets, limiting scalability. We introduce the Immunocytochemistry Dataset Cell Counting with Segment Anything Model (IDCC-SAM), a novel application of the Segment Anything Model (SAM), designed to adapt the model for zero-shot-based cell counting in fluorescent microscopic immunocytochemistry datasets. IDCC-SAM leverages Meta AI's SAM, pre-trained on 11 million images, to eliminate the need for annotations, enhancing scalability and efficiency. Evaluated on three public datasets (IDCIA, ADC, and VGG), IDCC-SAM achieved the lowest Mean Absolute Error (26, 28, 52) on VGG and ADC and the highest Acceptable Absolute Error (28%, 26%, 33%) across all datasets, outperforming state-of-the-art supervised models like U-Net and Mask R-CNN, as well as zero-shot benchmarks like NP-SAM and SAM4Organoid. These results demonstrate IDCC-SAM's potential to improve cell-counting accuracy while reducing reliance on specialized models and manual annotations.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice.","authors":"Debesh Jha, Gorkem Durak, Vanshali Sharma, Elif Keles, Vedat Cicek, Zheyuan Zhang, Abhishek Srivastava, Ashish Rauniyar, Desta Haileselassie Hagos, Nikhil Kumar Tomar, Frank H Miller, Ahmet Topcu, Anis Yazidi, Jan Erik Håkegård, Ulas Bagci","doi":"10.3390/bioengineering12020180","DOIUrl":"10.3390/bioengineering12020180","url":null,"abstract":"<p><p>Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study on Staging Cystic Echinococcosis Using Machine Learning Methods.","authors":"Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee, Dolgorsuren Batjargal","doi":"10.3390/bioengineering12020181","DOIUrl":"10.3390/bioengineering12020181","url":null,"abstract":"<p><p>Cystic echinococcosis (CE) is a chronic parasitic disease characterized by slow progression and non-specific clinical symptoms, often leading to delayed diagnosis and treatment. Early and precise diagnosis is crucial for effective treatment, particularly considering the five stages of CE as outlined by the World Health Organization (WHO). This study explores the development of an advanced system that leverages artificial intelligence (AI) and machine learning (ML) techniques to classify CE cysts into stages using various imaging modalities, including computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI). A total of ten ML algorithms were evaluated across these datasets, using performance metrics such as accuracy, precision, recall (sensitivity), specificity, and F1 score. These metrics offer diverse criteria for assessing model performance. To address this, we propose a normalization and scoring technique that consolidates all metrics into a final score, allowing for the identification of the best model that meets the desired criteria for CE cyst classification. The experimental results demonstrate that hybrid models, such as CNN+ResNet and Inception+ResNet, consistently outperformed other models across all three datasets. Specifically, CNN+ResNet, selected as the best model, achieved 97.55% accuracy on CT images, 93.99% accuracy on US images, and 100% accuracy on MRI images. This research underscores the potential of hybrid and pre-trained models in advancing medical image classification, providing a promising approach to improving the differential diagnosis of CE disease.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}