BioMedical Engineering OnLine最新文献

{"title":"Osteoblast dysfunction associated with mitophagy suppression under simulated microgravity.","authors":"Jindong Xue, Min Wang, Songsong Liu, Congncong Xu, Haoyang Yu, Yong Guo, Biao Han","doi":"10.1186/s12938-025-01454-w","DOIUrl":"https://doi.org/10.1186/s12938-025-01454-w","url":null,"abstract":"<p><strong>Background: </strong>Bone loss is a significant health concern during spaceflight and mechanical unloading. Simulated microgravity (SMG) disrupts bone homeostasis by inhibiting osteoblast proliferation and differentiation while promoting apoptosis. Although these functional effects have been reported, the underlying mechanisms remain unclear. Mitochondrial quality control, particularly mitophagy involving the PINK1/Parkin pathway, may play a key role. This study aimed to investigate the relationship between osteogenic dysfunction and mitochondrial damage under SMG conditions and preliminarily validate the potential link using the small molecule probe icariin (ICA).</p><p><strong>Methods: </strong>An SMG model was established using a rotary cell culture system. Cell proliferation was assessed by CCK-8 assay, apoptosis was analyzed via flow cytometry, and osteogenic differentiation was evaluated by alkaline phosphatase (ALP) and Alizarin Red staining. Expression levels of relevant genes and proteins were measured by qPCR and Western blot. Mitochondrial function was assessed through ATP content, reactive oxygen species (ROS) levels, JC-1 staining for mitochondrial membrane potential, and transmission electron microscopy (TEM) for ultrastructural observation. Additionally, cells were treated with the mitochondrial function-related small molecule icariin (ICA) to observe its regulatory effects on mitophagy markers (PINK1, Parkin, p62, LC3B) expression and osteogenic function.</p><p><strong>Results: </strong>SMG significantly inhibited osteoblast proliferation and differentiation and induced apoptosis. These changes were accompanied by impaired mitochondrial function and downregulated expression of mitophagy-related genes. TEM revealed mitochondrial swelling and disrupted cristae structure. Treatment with ICA partially restored mitochondrial function and mitophagy marker expression, along with improved expression of osteogenic markers and cell viability.</p><p><strong>Conclusions: </strong>SMG induces osteogenic dysfunction, mitochondrial damage, and downregulation of mitophagy-related gene expression. The results suggest that impaired mitophagy may be a key mechanism underlying unloading-induced bone loss, and ICA, as a small molecule modulator, holds potential as a therapeutic intervention.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"113"},"PeriodicalIF":2.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of recombinant type III collagen in tissue engineering.","authors":"Yuchen Shan, Ting Wang, Haiyan Lin","doi":"10.1186/s12938-025-01447-9","DOIUrl":"https://doi.org/10.1186/s12938-025-01447-9","url":null,"abstract":"<p><p>Recombinant human type III collagen (rhCol III) is a promising biomaterial for tissue engineering due to its excellent biocompatibility and bioactivity. This review summarizes the structure, development, and applications of rhCol III in bone and soft tissue engineering. Advances in genetic engineering have enabled the production of rhCol III with customizable properties. It plays a key role in promoting tissue repair and regeneration, with significant potential in wound healing, skin repair, and anti-inflammatory responses. However, challenges remain in optimizing its mechanical properties for specific applications. Future research should focus on improving production efficiency and clinical translation while addressing potential host responses.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"114"},"PeriodicalIF":2.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating cell viability assessment techniques: a comparative study of flow cytometry and fluorescence microscopy in response to bioactive glass exposure.","authors":"Bolaji J Samuel, Zhaorui Jin, Delia S Brauer, Georg Matziolis, Victoria Horbert","doi":"10.1186/s12938-025-01452-y","DOIUrl":"10.1186/s12938-025-01452-y","url":null,"abstract":"<p><p>Reliable in vitro cytotoxicity assessment of biomaterials is essential for preclinical evaluation. Fluorescence microscopy (FM) and flow cytometry (FCM) are widely used methods, yet their comparative performance in particulate systems remains underexplored. This study compares FM and FCM in evaluating the cytotoxicity of Bioglass 45S5 (BG) on SAOS-2 osteoblast-like cells across different particle sizes and concentrations. The cells were treated with BG particles in three size ranges (< 38 µm, 63-125 µm, and 315-500 µm) at concentrations of 25, 50, and 100 mg/mL for 3 and 72 h. FM employs FDA/PI staining to distinguish viable and nonviable cells, whereas FCM utilizes multiparametric staining (Hoechst, DiIC1, Annexin V-FITC, and PI) to classify viable, apoptotic, and necrotic populations. Both techniques confirmed a clear trend: smaller particles and higher concentrations caused greater cytotoxicity. The most pronounced effect was observed for < 38 µm particles at 100 mg/mL, which reduced FM-assessed viability to 9% at 3 h and 10% at 72 h; FCM measurements under the same conditions revealed 0.2% and 0.7% viability, respectively. The controls retained > 97% viability, indicating size-based cytotoxicity. A strong correlation between FM and FCM data (r = 0.94, R² = 0.8879, p < 0.0001) was observed. FCM further distinguished early and late apoptosis from necrosis and demonstrated superior precision, particularly under high cytotoxic stress. These findings highlight the size- and dose-dependent cytotoxic potential of BG and support the use of FCM as a robust, quantitative tool for cytocompatibility evaluation in particulate biomaterial research.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"112"},"PeriodicalIF":2.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lesion detection in age-related macular degeneration with a multi-modal imaging and machine learning approach.","authors":"Chun Lin Yap, Ting Fang Tan, Anna C S Tan, Leopold Schmetterer, Damon Wong","doi":"10.1186/s12938-025-01439-9","DOIUrl":"10.1186/s12938-025-01439-9","url":null,"abstract":"<p><strong>Background: </strong>Age-related macular degeneration is a leading cause of central vision loss, and assessing visual function with microperimetry can be time-consuming and tiring for patients. Targeting regions corresponding to worsening acute-stage retinal lesions may reduce test durations and patient fatigue.</p><p><strong>Results: </strong>We developed a machine-learning approach using multi-modal imaging data to differentiate lesional regions from healthy retinal areas. Our dataset included 344,003 regions extracted from color fundus photographs, infrared fundus images, optical coherence tomography, and optical coherence tomography angiography images. A gradient-boosted tree-ensemble model was trained on this data and achieved an area under the receiver operating characteristic curve of 0.95 in detecting end-stage lesions in chronic age-related macular degeneration.</p><p><strong>Conclusions: </strong>The proposed method effectively detects lesions associated with age-related macular degeneration using multi-modal imaging and machine learning. This approach offers a potential solution for creating targeted microperimetry test patterns, which can reduce testing time and patient fatigue, thereby enhancing the clinical assessment of visual function in affected patients.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"111"},"PeriodicalIF":2.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.","authors":"Blanca Vazquez, Jorge Perez-Gonzalez, Nidiyare Hevia-Montiel","doi":"10.1186/s12938-025-01446-w","DOIUrl":"10.1186/s12938-025-01446-w","url":null,"abstract":"<p><strong>Background: </strong>Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects millions, mainly in Latin America, but is spreading globally due to migration and climate change. Early identification of infection is vital for preventing chronic complications, and analyzing multimodal cardiac function data may help detect T. cruzi infection early. This study presents a hybrid method based on late multimodal fusion for integrating machine learning (ML) and deep learning (DL) algorithms using cardiac biomarkers and echocardiography (ECHO) video to classify individuals with T. cruzi infection.</p><p><strong>Methods: </strong>An experimental cohort of 96 ICR mice was utilized to study cardiac functionality in infected individuals. Ensemble feature selection (EFS) and weighted multiple kernel learning (MKL) methods were proposed to classify unimodal and multimodal cardiac biomarkers using an ML approach. In addition, two DL-based architectures were implemented for ECHO video classification. Finally, we integrated the ML and DL algorithms in a hybrid method based on late multimodal fusion.</p><p><strong>Results: </strong>From 64 biomarkers, we identified 17 biomarkers as the most relevant using EFS. For ML, we trained algorithms with these selected biomarkers and obtained 73% accuracy (ACC), 84% area under the ROC curve (AUC), and an F1 score (F1) of 69% using unweighted MKL, and we noted that these results improved with weighted MKL, achieving ACC, AUC, and F1 of 80% on the test set. For the DL approach, we used ECHO for video classification, obtaining 65% ACC, 60% AUC, and F1 of 58%. Then, we integrated the ML and DL algorithms using the proposed hybrid method, which achieved 84% AUC, and 80% in ACC and F1.</p><p><strong>Conclusions: </strong>We presented a hybrid method for fusion cardiac biomarkers and ECHO video using late multimodal fusion (ML + DL). This work has the potential to assist in the diagnosis and monitoring of T. cruzi infection by providing an automated tool capable of accurately identifying patients with CD.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"110"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analysis of the effects of compressive stress on osteoblasts and osteoclasts growth.","authors":"Tingting Miao, Chengli Ni, Qianjiao Meng, Huixin Cheng, Yuan Wei","doi":"10.1186/s12938-025-01444-y","DOIUrl":"10.1186/s12938-025-01444-y","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to systematically assess the impact of compressive stress on the growth of osteoblasts and osteoclasts through a meta-analysis of existing literature. The focus was on understanding how compressive stress affects cell proliferation, differentiation, and overall bone metabolism.</p><p><strong>Methods: </strong>A comprehensive Literature search was conducted in multiple databases, including PubMed, Web of Science, CNKI, and ScienceDirect, to identify studies published between January 2000 and April 2025. The selection criteria focused on experimental studies examining the effects of compressive stress on osteoblasts and osteoclasts. Data from 16 high-quality studies were extracted and analyzed using RevMan 5.4 and R 4.1.4, with subgroup analyses based on study type, stress type, and cell response.</p><p><strong>Results: </strong>The meta-analysis found a significant positive effect of compressive stress on the growth of both osteoblasts and osteoclasts. In vitro studies demonstrated a stronger and more consistent effect compared to animal studies. Osteoblasts responded more significantly to compressive stress than osteoclasts. Different stress types, including compression stress and fluid shear stress, showed varying levels of impact, with compression stress having the most pronounced effects on cell growth.</p><p><strong>Discussion: </strong>Compressive stress plays a critical role in promoting osteoblast and osteoclast growth, which has implications for bone health and metabolism. These findings highlight the potential of compressive stress as a therapeutic tool for bone-related conditions. Further research is needed to explore the molecular mechanisms and clinical applications of compressive stress in treating bone diseases.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"109"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based cardiac computed tomography angiography left atrial segmentation and quantification in atrial fibrillation patients: a multi-model comparative study.","authors":"Lijun Feng, Wei Lu, Jiayi Liu, Zining Chen, Junyan Jin, Ningjing Qian, Jingnan Pan, Lijuan Wang, Jianping Xiang, Jun Jiang, Yaping Wang","doi":"10.1186/s12938-025-01442-0","DOIUrl":"10.1186/s12938-025-01442-0","url":null,"abstract":"<p><strong>Background and purpose: </strong>Quantitative assessment of left atrial volume (LAV) is an important factor in the study of the pathogenesis of atrial fibrillation. However, automated left atrial segmentation with quantitative assessment usually faces many challenges. The main objective of this study was to find the optimal left atrial segmentation model based on cardiac computed tomography angiography (CTA) and to perform quantitative LAV measurement.</p><p><strong>Method: </strong>A multi-center left atrial study cohort containing 182 cardiac CTAs with atrial fibrillation was created, each case accompanied by expert image annotation by a cardiologist. Then, based on this left atrium dataset, five recent states-of-the-art (SOTA) models in the field of medical image segmentation were used to train and validate the left atrium segmentation model, including DAResUNet, nnFormer, xLSTM-UNet, UNETR, and VNet, respectively. Further, the optimal segmentation model was used to assess the consistency validation of the LAV.</p><p><strong>Results: </strong>DAResUNet achieved the best performance in DSC (0.924 ± 0.023) and JI (0.859 ± 0.065) among all models, while VNet is the best performer in HD (12.457 ± 6.831) and ASD (1.034 ± 0.178). The Bland-Altman plot demonstrated the extremely strong agreement (mean bias - 5.69 mL, 95% LoA - 19-7.6 mL) between the model's automatic prediction and manual measurements.</p><p><strong>Conclusion: </strong>Deep learning models based on a study cohort of 182 CTA left atrial images were capable of achieving competitive results in left atrium segmentation. LAV assessment based on deep learning models may be useful for biomarkers of the onset of atrial fibrillation.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"106"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of colon polyps with malignant potential using statistical analysis of features extracted from ex vivo optical coherence tomography images.","authors":"Christos Photiou, Andrew D Thrapp, Guillermo J Tearney, Costas Pitris","doi":"10.1186/s12938-025-01443-z","DOIUrl":"10.1186/s12938-025-01443-z","url":null,"abstract":"<p><p>To reduce the burden of screening of colorectal polyps, *leave-in-situ* management of diminutive (≤ 5 mm) polyps is being considered. However, such an approach requires increased diagnostic efficacy (PIVI-1 criterion). The aim of this study was to use Optical Coherence Tomography (OCT) for the classification of pre-malignant polyps as benign (i.e., normal or hyperplastic) vs. those with a malignant potential (i.e., adenomas and sessile serrated adenomas) at an accuracy that would enable clinical screening of colorectal cancer. The OCT raw data, from volume imaging of resected polyps, were used to extract features that can serve as biomarkers of disease. In addition to intensity and textural measures, novel biomarkers, such as scatterer size, group velocity dispersion, and spectral information, were also estimated. Their statistical properties were combined to produce scores which, in turn, were used to classify the images or polyps. This approach yielded 79.6% accuracy (72.3% NPV) for the classification of individual images and 97.3% accuracy (95.5% NPV) when combining the feature values to classify whole polyp sections. The results of this study confirm the potential of OCT imaging of colorectal polyps as a viable adjunct to colonoscopy that could enable leave-in-situ management strategies.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"108"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathomics-based machine learning models for optimizing LungPro navigational bronchoscopy in peripheral lung lesion diagnosis: a retrospective study.","authors":"Feng Ying, Ya Bao, Xiaoyu Ma, Yiwen Tan, Shengjin Li","doi":"10.1186/s12938-025-01440-2","DOIUrl":"10.1186/s12938-025-01440-2","url":null,"abstract":"<p><strong>Objectives: </strong>To construct a pathomics-based machine learning model to enhance the diagnostic efficacy of LungPro navigational bronchoscopy for peripheral pulmonary lesions and to optimize the management strategy for LungPro-diagnosed negative lesions.</p><p><strong>Methods: </strong>Clinical data and hematoxylin and eosin (H&E)-stained whole slide images (WSIs) were collected from 144 consecutive patients undergoing LungPro virtual bronchoscopy at a single institution between January 2022 and December 2023. Patients were stratified into diagnosis-positive and diagnosis-negative cohorts based on histopathological or etiological confirmation. An artificial intelligence (AI) model was developed and validated using 94 diagnosis-positive cases. Logistic regression (LR) identified associations between clinical/imaging characteristics and malignant pulmonary lesion risk factors. We implemented a convolutional neural network (CNN) with weakly supervised learning to extract image-level features, followed by multiple instance learning (MIL) for patient-level feature aggregation. Multiple machine learning (ML) algorithms were applied to model the extracted features. A multimodal diagnostic framework integrating clinical, imaging, and pathomics data were subsequently developed and evaluated on 50 LungPro-negative patients to assess the framework's diagnostic performance and predictive validity.</p><p><strong>Results: </strong>Univariable and multivariable logistic regression analyses identified that age, lesion boundary and mean computed tomography (CT) attenuation were independent risk factors for malignant peripheral pulmonary lesions (P < 0.05). A histopathological model using a MIL fusion strategy showed strong diagnostic performance for lung cancer, with area under the curve (AUC) values of 0.792 (95% CI 0.680-0.903) in the training cohort and 0.777 (95% CI 0.531-1.000) in the test cohort. Combining predictive clinical features with pathological characteristics enhanced diagnostic yield for peripheral pulmonary lesions to 0.848 (95% CI 0.6945-1.0000). In patients with initially negative LungPro biopsy results, the model identified 20 of 28 malignant lesions (sensitivity: 71.43%) and 15 of 22 benign lesions (specificity: 68.18%). Class activation mapping (CAM) validated the model by highlighting key malignant features, including conspicuous nucleoli and nuclear atypia.</p><p><strong>Conclusions: </strong>The fusion diagnostic model that incorporates clinical and pathomic features markedly enhances the diagnostic accuracy of LungPro in this retrospective cohort. This model aids in the detection of subtle malignant characteristics, thereby offering evidence to support precise and targeted therapeutic interventions for lesions that LungPro classifies as negative in clinical settings.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"107"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic position monitoring of endotracheal breathing tubes using a magnetic sensor array.","authors":"Till Riemschneider, Thorben Schüthe, Robert Werdehausen, Thomas Schilling, Thomas Hachenberg","doi":"10.1186/s12938-025-01441-1","DOIUrl":"10.1186/s12938-025-01441-1","url":null,"abstract":"<p><strong>Background: </strong>Dislocation of an endotracheal tube (ETT) during invasive ventilation can lead to serious events such as unilateral ventilation or unintentional extubation. The correct position of the endotracheal tube is determined visually. X-ray imaging or invasive procedures such as bronchoscopy are established for repeated position verification. However, these measures are time-consuming and only provide a limited number of snapshots. A new monitoring method can recognize dislocations of the ETT. The proposed system operates automatically without the need for continuous staff awareness or interaction.</p><p><strong>Materials and methods: </strong>A ring-shaped permanent magnet is attached to the ETT. A small device is placed extracorporeally on the patient to detect the magnetic field. This device uses 64 magnetic sensors arranged as a sensor array in an 8x8 matrix. The sensor signals are digitally converted, enabling the position of the ETT with the attached magnet to be determined by software. Two processing methods (image similarity and localization) are tested for monitoring. The prototype system detects displacements with millimeter scale positioning deviations in our tests.</p><p><strong>Results: </strong>Our system triggers an alarm upon detecting an impermissible dislocation, complete extubation, or unintended bronchial intubation. The proposed methods were validated on a sensor array prototype and assessed through a dedicated experimental setup. The results are promising and could lead to further development towards clinical usability.</p><p><strong>Conclusion: </strong>Early warnings would be particularly advantageous, even for minor or beginning dislocations of the ETT. An automated continuous tube monitoring process could help reduce the workload of the staff and improve patient safety.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"105"},"PeriodicalIF":2.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}