BioMedical Engineering OnLine最新文献

{"title":"Preclinical evaluation of a hydraulic actuation system with guide tube for robotic cochlear implant electrode insertion.","authors":"Jakob Cramer, Rolf Salcher, Max Fröhlich, Georg Böttcher-Rebmann, Eralp Artukarslan, Thomas Lenarz, Thomas S Rau","doi":"10.1186/s12938-025-01338-z","DOIUrl":"10.1186/s12938-025-01338-z","url":null,"abstract":"<p><strong>Background: </strong>Automated insertion of the cochlear implant electrode array can reduce the risk of intracochlear trauma. To address this, our group previously developed a hydraulic electrode insertion device, the Cochlea Hydrodrive (CHD), which automates the process using a syringe piston driven by an infusion pump. This study aims to characterize the hydraulic actuation process of the CHD and to preclinically evaluate its design.</p><p><strong>Methods: </strong>A camera-based motion tracking test setup was developed to obtain hydraulic motion profiles. Various syringes were evaluated for their actuation properties and the optimal syringe was selected. The CHD design was adapted based on the selected syringe, incorporating a slotted stainless steel guide tube to surround the electrode during insertion. This enhanced design was tested in ex vivo insertion trials into human head specimens.</p><p><strong>Results: </strong>The final design of the CHD demonstrated smooth and steady motion profiles at all tested velocities (0.4 mm/s, 0.1 mm/s, 0.03 mm/s). Ex vivo insertion trials confirmed these findings, with the guide tube facilitating easy alignment of the CHD in front of the round window and preventing electrode buckling.</p><p><strong>Conclusion: </strong>Our study validates that the CHD provides reliably smooth actuation properties despite its low complexity. The use of a guide tube appears promising and could further enhance the standardization of automated electrode insertion.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"19"},"PeriodicalIF":2.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424942","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":"Comparative analysis of electrical signals in facial expression muscles.","authors":"Luna Adamov, Bojan Petrović, Lazar Milić, Vojin Štrbac, Sanja Kojić, Karunan Joseph, Goran M Stojanović","doi":"10.1186/s12938-025-01350-3","DOIUrl":"10.1186/s12938-025-01350-3","url":null,"abstract":"<p><strong>Background: </strong>Facial expression muscles serve a fundamental role in the orofacial system, significantly influencing the overall health and well-being of an individual. They are essential for performing basic functions such as speech, chewing, and swallowing. The purpose of this study was to determine whether surface electromyography could be used to evaluate the health, function, or dysfunction of three facial muscles by measuring their electrical activity in healthy people. Additionally, to ascertain whether pattern recognition and artificial intelligence may be used for tasks that differ from one another.</p><p><strong>Results: </strong>The study included 24 participants and examined three muscles (m. Orbicularis Oris, m. Zygomaticus Major, and m. Mentalis) during five different facial expressions. Prior to thorough statistical analysis, features were extracted from the acquired electromyographs. Finally, classification was done with the use of logistic regression, random forest classifier and linear discriminant analysis. A statistically significant difference in muscle activity amplitudes was demonstrated between muscles, enabling the tracking of individual muscle activity for diagnostic and therapeutic purposes. Additionally other time domain and frequency domain features were analyzed, showing statistical significance in differentiation between muscles as well. Examples of pattern recognition showed promising avenues for further research and development.</p><p><strong>Conclusion: </strong>Surface electromyography is a useful method for assessing the function of facial expression muscles, significantly contributing to the diagnosis and treatment of oral motor function disorders. Results of this study show potential for further research and development in this field of research.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"17"},"PeriodicalIF":2.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143405363","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":"Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.","authors":"Xiang Yu, Daoyan Hu, Qiong Yao, Yu Fu, Yan Zhong, Jing Wang, Mei Tian, Hong Zhang","doi":"10.1186/s12938-025-01348-x","DOIUrl":"10.1186/s12938-025-01348-x","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study is to convert low-dose PET (L-PET) images to full-dose PET (F-PET) images based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) to offer a potential balance between reducing radiation exposure and maintaining diagnostic performance.</p><p><strong>Methods: </strong>The proposed method includes two modules: the diffusion generator and the u-net discriminator. The goal of the first module is to get different information from different levels, enhancing the generalization ability of the generator to the image and improving the stability of the training. Generated images are inputted into the u-net discriminator, extracting details from both overall and specific perspectives to enhance the quality of the generated F-PET images. We conducted evaluations encompassing both qualitative assessments and quantitative measures. In terms of quantitative comparisons, we employed two metrics, structure similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) to evaluate the performance of diverse methods.</p><p><strong>Results: </strong>Our proposed method achieved the highest PSNR and SSIM scores among the compared methods, which improved PSNR by at least 6.2% compared to the other methods. Compared to other methods, the synthesized full-dose PET image generated by our method exhibits a more accurate voxel-wise metabolic intensity distribution, resulting in a clearer depiction of the epilepsy focus.</p><p><strong>Conclusions: </strong>The proposed method demonstrates improved restoration of original details from low-dose PET images compared to other models trained on the same datasets. This method offers a potential balance between minimizing radiation exposure and preserving diagnostic performance.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"16"},"PeriodicalIF":2.9,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11807330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381658","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":"The impact of a 5G-based smart nursing information system and associated mobile hardware on clinical nurses' work stress: a randomized controlled study in a Chinese hospital.","authors":"Xuejiao Ruan, Yuying Lou, Xinhua Zhang, Zhulin Wu, Hongzhi Yuan","doi":"10.1186/s12938-025-01344-1","DOIUrl":"10.1186/s12938-025-01344-1","url":null,"abstract":"<p><strong>Background: </strong>Clinical nurses frequently endure substantial work-related stress, adversely affecting their well-being and potentially compromising patient care quality and safety. The integration of a 5G-based medical private network into smart nursing systems and mobile devices offers a promising solution to reduce this stress. This study evaluates the impact of a Smart Nursing Information System based on a 5G Medical Private Network and its Supporting Mobile Hardware (SNIS-SMH) on mitigating work-related stress among clinical nurses. The goal is to provide a scientific basis for nursing management, reduce error incidents, advance nursing procedures, and enhance productivity.</p><p><strong>Results: </strong>A total of 226 nurses completed the study. The SNIS-SMH group showed significantly lower total work stress scores (66.16 ± 9.82) compared to the control group (70.65 ± 11.32, P = 0.002). In specific dimensions, the SNIS-SMH group had lower scores for nursing profession and work (14.17 ± 2.37 vs. 15.00 ± 3.06, P = 0.023), workload and time distribution (10.56 ± 2.45 vs. 12.42 ± 2.55, P < 0.001), and patient care (22.55 ± 3.34 vs. 23.70 ± 4.06, P = 0.021). No significant differences were found in the work environment and resource, and management and interpersonal relationships dimensions.</p><p><strong>Conclusions: </strong>The SNIS-SMH system significantly alleviated work-related stress among clinical nurses, particularly in nursing duties, workload and time distribution, and patient care.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"15"},"PeriodicalIF":2.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373613","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":"The effect of depth data and upper limb impairment on lightweight monocular RGB human pose estimation models.","authors":"Gloria-Edith Boudreault-Morales, Cesar Marquez-Chin, Xilin Liu, José Zariffa","doi":"10.1186/s12938-025-01347-y","DOIUrl":"10.1186/s12938-025-01347-y","url":null,"abstract":"<p><strong>Background and objectives: </strong>Markerless vision-based human pose estimation (HPE) is a promising avenue towards scalable data collection in rehabilitation. Deploying this technology will require self-contained systems able to process data efficiently and accurately. The aims of this work are to (1) Determine how depth data affects lightweight monocular red-green-blue (RGB) HPE performance (accuracy and speed), to inform sensor selection and (2) Validate HPE models using data from individuals with physical impairments.</p><p><strong>Methods: </strong>Two HPE models were investigated: Dite-HRNet and MobileHumanPose (capable of 2D and 3D HPE, respectively). The models were modified to include depth data as an input using three different fusion techniques: an early fusion method, a simple intermediate fusion method (using concatenation), and a complex intermediate fusion method (using specific fusion blocks, additional convolutional layers, and concatenation). All fusion techniques used RGB-D data, in contrast to the original models which only used RGB data. The models were trained, validated and tested using the CMU Panoptic and Human3.6 M data sets as well as a custom data set. The custom data set includes RGB-D and optical motion capture data of 15 uninjured and 12 post-stroke individuals, while they performed movements involving their upper limbs. HPE model performances were monitored through accuracy and computational efficiency. Evaluation metrics include Mean per Joint Position Error (MPJPE), Floating Point Operations (FLOPs) and frame rates (frames per second).</p><p><strong>Results: </strong>The early fusion architecture consistently delivered the lowest MPJPE in both 2D and 3D HPE cases while achieving similar FLOPs and frame rates to its RGB counterpart. These results were consistent regardless of the data used for training and testing the HPE models. Comparisons between the uninjured and stroke groups did not reveal a significant effect (all p values > 0.36) of motor impairment on the accuracy of any model.</p><p><strong>Conclusions: </strong>Including depth data using an early fusion architecture improves the accuracy-efficiency trade-off of the HPE model. HPE accuracy is not affected by the presence of physical impairments. These results suggest that using depth data with RGB data is beneficial to HPE, and that models trained with data collected from uninjured individuals can generalize to persons with physical impairments.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"12"},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370373","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":"Advances in growth factor-containing 3D printed scaffolds in orthopedics.","authors":"Longwen Zhan, Yigui Zhou, Ruitang Liu, Ruilong Sun, Yunfei Li, Yongzheng Tian, Bo Fan","doi":"10.1186/s12938-025-01346-z","DOIUrl":"10.1186/s12938-025-01346-z","url":null,"abstract":"<p><p>Currently, bone tissue engineering is a research hotspot in the treatment of orthopedic diseases, and many problems in orthopedics can be solved through bone tissue engineering, which can be used to treat fractures, bone defects, arthritis, etc. More importantly, it can provide an alternative to traditional bone grafting and solve the problems of insufficient autologous bone grafting, poor histocompatibility of grafts, and insufficient induced bone regeneration. Growth factors are key factors in bone tissue engineering by promoting osteoblast proliferation and differentiation, which in turn increases the efficiency of osteogenesis and bone regeneration. 3D printing technology can provide carriers with better pore structure for growth factors to improve the stability of growth factors and precisely control their release. Studies have shown that 3D-printed scaffolds containing growth factors provide a better choice for personalized treatment, bone defect repair, and bone regeneration in orthopedics, which are important for the treatment of orthopedic diseases and have potential research value in orthopedic applications. This paper aims to summarize the research progress of 3D printed scaffolds containing growth factors in orthopedics in recent years and summarize the use of different growth factors in 3D scaffolds, including bone morphogenetic proteins, platelet-derived growth factors, transforming growth factors, vascular endothelial growth factors, etc. Optimization of material selection and the way of combining growth factors with scaffolds are also discussed.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"14"},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370371","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":"Ultramodern natural and synthetic polymer hydrogel scaffolds for articular cartilage repair and regeneration.","authors":"Chun-Sheng Li, Yan Xu, Juan Li, Shu-Hao Qin, Shao-Wen Huang, Xue-Mei Chen, Yi Luo, Cheng-Tao Gao, Jian-Hui Xiao","doi":"10.1186/s12938-025-01342-3","DOIUrl":"10.1186/s12938-025-01342-3","url":null,"abstract":"<p><p>Articular cartilage injury is a serious bone disease that can result in disabilities. With the rapid increase in the aging population, this disorder has become an increasingly important public health issue. Recently, stem cell-based cartilage tissue engineering has emerged as a promising therapeutic option for treating articular cartilage damage. Cellular scaffolds, which are among three key elements of tissue engineering, play significant roles in the repair of damaged articular cartilage by regulating cellular responses and promoting cartilage tissue regeneration. Biological macromolecules are commonly used as scaffold materials owing to their unique properties. For example, natural and synthetic polymer hydrogel scaffolds can effectively mimic the microenvironment of the natural extracellular matrix; exhibit high cytocompatibility, biocompatibility, and biodegradability; and have attracted increasing attention in bone and cartilage tissue engineering and regeneration medicine. Several types of hydrogel scaffolds have been fabricated to treat articular cartilage abnormalities. This article outlines the recent progress in the field of hydrogel scaffolds manufactured from various biomaterials for repairing damaged articular cartilage, discusses their advantages and disadvantages, and proposes directions for their future development.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"13"},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370375","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":"WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation.","authors":"Ichiro Nakamoto, Hua Chen, Rui Wang, Yan Guo, Wei Chen, Jie Feng, Jianfeng Wu","doi":"10.1186/s12938-025-01341-4","DOIUrl":"10.1186/s12938-025-01341-4","url":null,"abstract":"<p><p>The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single-(e.g., disc herniation, prolapse, or bulge) and comorbidity-type degeneration (e.g., simultaneous presence of two or more conditions), respectively. A sample of lumbar magnetic resonance imaging (MRI) images from multiple clinical hospitals in China was collected and used in the proposal assessment. We devised a weighted transfer learning framework WDRIV-Net by ensembling four pre-trained models including Densenet169, ResNet101, InceptionV3, and VGG19. The proposed approach was applied to the clinical data and achieved 96.25% accuracy, surpassing the benchmark ResNet101 (87.5%), DenseNet169 (82.5%), VGG19 (88.75%), InceptionV3 (93.75%), and other state-of-the-art (SOTA) ensemble deep learning models. Furthermore, improved performance was observed as well for the metric of the area under the curve (AUC), producing a ≥ 7% increase versus other SOTA ensemble learning, a ≥ 6% increase versus most-studied models, and a ≥ 2% increase versus the baselines. WDRIV-Net can serve as a guide in the initial and efficient type screening of complex degeneration of lumbar intervertebral discs (LID) and assist in the early-stage selection of clinically differentiated treatment options.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"11"},"PeriodicalIF":2.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363551","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":"Relationship between RSV-hospitalized children and meteorological factors: a time series analysis from 2017 to 2023.","authors":"Shuying Wang, Yifan Wang, Yingxue Zou, Cheng-Liang Yin","doi":"10.1186/s12938-025-01339-y","DOIUrl":"10.1186/s12938-025-01339-y","url":null,"abstract":"<p><strong>Objectives: </strong>Respiratory syncytial virus (RSV) is a leading cause of hospitalization for lower respiratory tract infections amongst infants under 1 year, posing a significant global health challenge. The incidence of RSV exhibits marked seasonality and is influenced by various meteorological factors, which vary across regions and climates. This study aimed to analyze seasonal trends in RSV-related hospitalization in Tianjin, a region with a semi-arid and semi-humid monsoon climate, and to explore the relationship between these trends and meteorological factors. This research intends to inform RSV prevention strategies, optimize public health policies and medical resource allocation while also promoting vaccine and therapeutic drug development.</p><p><strong>Methods: </strong>This study analyzed data from a cohort of 6222 children hospitalized with RSV-related infections. Meteorological data were collected from the Tianjin Binhai International Airport meteorological station, encompassing temperature (℃), air pressure (mmHg), wind speed (m/s), humidity (%), and precipitation (mm). We employed seasonal ARIMA and GAM models to investigate the association between meteorological factors and RSV-related hospitalizations.</p><p><strong>Results: </strong>The SARIMA (1,0,0) (0,1,2)12 model effectively predicted RSV-related hospital admissions. Spearman correlation and GAM analysis revealed a significant negative association between the monthly average temperature and RSV hospitalizations.</p><p><strong>Conclusions: </strong>Our findings indicated that meteorological factors influence RSV infection-related hospital admissions, with higher monthly average temperatures associated with fewer hospitalizations. The predictive capabilities of the SARIMA model bolster the formulation of targeted RSV prevention strategies, enhancing public health policy and medical resource allocation. Furthermore, continued research into vaccines and therapeutic drugs remains indispensable for augmenting public health outcomes.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"10"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11796253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254256","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":"Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.","authors":"Qiao Chang, Yuxing Bai, Shaofeng Wang, Fan Wang, Shuang Liang, Xianju Xie","doi":"10.1186/s12938-025-01345-0","DOIUrl":"10.1186/s12938-025-01345-0","url":null,"abstract":"<p><strong>Background: </strong>Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics.</p><p><strong>Methods: </strong>This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance.</p><p><strong>Results: </strong>Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms.</p><p><strong>Conclusions: </strong>This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"9"},"PeriodicalIF":2.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143188028","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}