Medical & Biological Engineering & Computing最新文献_第8页

MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems. MSFHNet：一种用于脑机接口-虚拟现实系统空间认知脑电信号多尺度时空特征提取的混合深度学习网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-05 DOI: 10.1007/s11517-025-03386-y

Xulong Liu, Ziwei Jia, Meng Xun, Xianglong Wan, Huibin Lu, Yanhong Zhou

{"title":"MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.","authors":"Xulong Liu, Ziwei Jia, Meng Xun, Xianglong Wan, Huibin Lu, Yanhong Zhou","doi":"10.1007/s11517-025-03386-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03386-y","url":null,"abstract":"The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227392","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}

引用次数: 0

Stacked ensemble-based mutagenicity prediction model using multiple modalities with graph attention network. 基于多模态、图注意网络的堆叠集成突变性预测模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-05 DOI: 10.1007/s11517-025-03392-0

Tanya Liyaqat, Tanvir Ahmad, Mohammad Kashif, Chandni Saxena

{"title":"Stacked ensemble-based mutagenicity prediction model using multiple modalities with graph attention network.","authors":"Tanya Liyaqat, Tanvir Ahmad, Mohammad Kashif, Chandni Saxena","doi":"10.1007/s11517-025-03392-0","DOIUrl":"https://doi.org/10.1007/s11517-025-03392-0","url":null,"abstract":"Mutagenicity is concerning due to its link to genetic mutations, which can lead to cancer and other adverse effects. Early identification of mutagenic compounds in drug development is crucial to prevent unsafe candidates and reduce costs. While computational techniques, especially machine learning (ML) models, have become prevalent for mutagenicity prediction, they typically rely on a single modality. Our work introduces a novel stacked ensemble mutagenicity prediction model that integrates multiple modalities, including SMILES and molecular graphs. These modalities capture diverse molecular information such as substructural, physicochemical, geometrical, and topological features. We use SMILES for deriving substructural, geometrical, and physicochemical data, while a graph attention network (GAT) extracts topological information from molecular graphs. Our model employs a stacked ensemble of ML classifiers and SHAP (Shapley Additive Explanations) to identify the significance of classifiers and key features. Our method outperforms state-of-the-art techniques on two standard datasets, achieving an area under the curve of 95.21% on the Hansen benchmark dataset. This research is expected to interest clinicians and computational biologists in translational research.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227393","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}

引用次数: 0

A novel approach to exercise heart rate estimation combining PPG quality assessment with DNN modeling. 一种结合PPG质量评估和DNN建模的运动心率估计新方法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-04 DOI: 10.1007/s11517-025-03379-x

Mengshan Wu, Xiang Chen

{"title":"A novel approach to exercise heart rate estimation combining PPG quality assessment with DNN modeling.","authors":"Mengshan Wu, Xiang Chen","doi":"10.1007/s11517-025-03379-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03379-x","url":null,"abstract":"This paper proposes a novel approach for exercise heart rate (HR) estimation by integrating PPG quality assessment with deep neural network (DNN) modeling. A frequency-domain kurtosis (kurtF) metric is introduced to identify high-quality PPG samples, optimizing DNN training data and mitigating motion artifacts. An E-K scatter plot is used to visualize sample quality distribution, aiding dataset variability analysis. The proposed DNN model, designed for single-channel PPG input, demonstrates strong HR estimation performance on public datasets, achieving a mean absolute error (MAE) values of 3.76 bpm (PPG_DaLiA) and 3.18 bpm (IEEE-Training). Theoretical analysis and experimental validation confirm that prioritizing high-quality samples enhances model stability, accuracy, and generalizability. Additionally, a dataset quality analysis method is introduced to facilitate comparative assessments. The kurtF metric and quality-driven sample selection strategy provide a robust framework for improving HR estimation, even in data-limited scenarios. This study underscores the importance of integrating sample quality assessment into HR estimation workflows, paving the way for more accurate and reliable PPG-based HR monitoring during exercise.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217399","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}

引用次数: 0

Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis. 基于数字乳腺断层合成的乳腺癌分子亚型预测的自我知识精馏。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-03 DOI: 10.1007/s11517-025-03383-1

Wei Guo, Jiayi Bo, Shilin Chen, Zhaoxuan Gong, Guodong Zhang, Hanxun Zhou, Zekun Wang, Peng Zhao, Wenyan Jiang

{"title":"Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis.","authors":"Wei Guo, Jiayi Bo, Shilin Chen, Zhaoxuan Gong, Guodong Zhang, Hanxun Zhou, Zekun Wang, Peng Zhao, Wenyan Jiang","doi":"10.1007/s11517-025-03383-1","DOIUrl":"https://doi.org/10.1007/s11517-025-03383-1","url":null,"abstract":"This study aims to investigate the effectiveness of self-knowledge distillation (self-KD) with progressive refinement in the early prediction of molecular subtypes of breast cancer (BC) using digital breast tomosynthesis (DBT) images. This study conducted a retrospective analysis of 368 patients who underwent breast DBT and/or magnetic resonance imaging (MRI) scans at our hospital. Among these patients, 303 underwent DBT scans and 119 underwent MRI scans. Of the DBT patients, 137 had images with molecular subtypes labels, while the remaining 166 did not have molecular subtype annotations. None of the MRI patients had the corresponding molecular subtype labels. To address the issue of insufficient labeled DBT images, we proposed a self-knowledge distillation (self-KD) framework with progressive refinement to more effectively utilize the unlabeled MRI and DBT image. Initially, the teacher network was pre-trained using unlabeled MRI images to capture the essential characteristics of BC. Subsequently, the teacher network was progressively refined to generate more accurate soft labels for the unlabeled DBT images, which improved the performance of the student network through KD. Additionally, a noise-adaptive layer was integrated to adjust the soft labels for more accurate learning. The performance of our method was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) values. The proposed self-KD method achieved an AUC of 0.834, ACC of 0.732, SEN of 0.930, and SPE of 0.734, which surpassed the existing methods for BC molecular subtype prediction. Specifically, compared to the baseline KD, our self-KD improved AUC by 9%, ACC by 6%, SEN by 26%, and SPE by 9%. The proposed self-KD framework effectively refines the network using both labeled and unlabeled images, which enables more accurate BC molecular subtype prediction.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210078","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}

引用次数: 0

Slim UNETR++: A lightweight 3D medical image segmentation network for medical image analysis. Slim UNETR++：用于医学图像分析的轻量级3D医学图像分割网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-02 DOI: 10.1007/s11517-025-03390-2

Jiawei Jin, Sen Yang, Jigang Tong, Kai Zhang, Zenghui Wang

{"title":"Slim UNETR++: A lightweight 3D medical image segmentation network for medical image analysis.","authors":"Jiawei Jin, Sen Yang, Jigang Tong, Kai Zhang, Zenghui Wang","doi":"10.1007/s11517-025-03390-2","DOIUrl":"https://doi.org/10.1007/s11517-025-03390-2","url":null,"abstract":"Convolutional neural network (CNN) models, such as U-Net, V-Net, and DeepLab, have achieved remarkable results across various medical imaging modalities, and ultrasound. Additionally, hybrid Transformer-based segmentation methods have shown great potential in medical image analysis. Despite the breakthroughs in feature extraction through self-attention mechanisms, these methods are computationally intensive, especially for three-dimensional medical imaging, posing significant challenges to graphics processing unit (GPU) hardware. Consequently, the demand for lightweight models is increasing. To address this issue, we designed a high-accuracy yet lightweight model that combines the strengths of CNNs and Transformers. We introduce Slim UNEt TRansformers++ (Slim UNETR++), which builds upon Slim UNETR by incorporating Medical ConvNeXt (MedNeXt), Spatial-Channel Attention (SCA), and Efficient Paired-Attention (EPA) modules. This integration leverages the advantages of both CNN and Transformer architectures to enhance model accuracy. The core component of Slim UNETR++ is the Slim UNETR++ block, which facilitates efficient information exchange through a sparse self-attention mechanism and low-cost representation aggregation. We also introduced throughput as a performance metric to quantify data processing speed. Experimental results demonstrate that Slim UNETR++ outperforms other models in terms of accuracy and model size. On the BraTS2021 dataset, Slim UNETR++ achieved a Dice accuracy of 93.12% and a 95% Hausdorff distance (HD95) of 4.23mm, significantly surpassing mainstream relevant methods such as Swin UNETR.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200646","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}

引用次数: 0

Towards understanding the functional connectivity patterns in visual brain network. 探讨视觉脑网络的功能连接模式。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-02 DOI: 10.1007/s11517-025-03389-9

Debanjali Bhattacharya, Neelam Sinha

{"title":"Towards understanding the functional connectivity patterns in visual brain network.","authors":"Debanjali Bhattacharya, Neelam Sinha","doi":"10.1007/s11517-025-03389-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03389-9","url":null,"abstract":"Recent advances in neuroimaging have enabled studies in functional connectivity (FC) of human brain, alongside investigation of the neuronal basis of cognition. One important FC study is the representation of vision in human brain. The release of publicly available dataset \"BOLD5000\" has made it possible to study the brain dynamics during visual tasks in greater detail. In this paper, a comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN). The novelty of this work lies in (1) constructing VBN with consistently significant direct connectivity using both marginal and partial correlation, which is further analyzed using graph theoretic measures, and (2) classification of VBNs as formed by image complexity-specific TS, using graphical features. In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5 to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation. This result not only reflects the distinguishing graphical characteristics of each image complexity-specific VBN, but also highlights the importance of studying both correlated and anti-correlated VBN to understand how differently brain functions while viewing different complexities of real-world images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200647","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}

引用次数: 0

Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition. 基于脑电图的情感识别的最大子域差异辅助分类器对抗网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-02 DOI: 10.1007/s11517-025-03384-0

Zhaowen Xiao, Qingshan She, Feng Fang, Ming Meng, Yingchun Zhang

{"title":"Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.","authors":"Zhaowen Xiao, Qingshan She, Feng Fang, Ming Meng, Yingchun Zhang","doi":"10.1007/s11517-025-03384-0","DOIUrl":"https://doi.org/10.1007/s11517-025-03384-0","url":null,"abstract":"Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210068","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}

引用次数: 0

Electrophysiological biomarkers based on CISANET characterize illness severity and suicidal ideation among patients with major depressive disorder. 基于CISANET的电生理生物标志物表征重性抑郁症患者的疾病严重程度和自杀意念。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-01 Epub Date: 2025-01-24 DOI: 10.1007/s11517-024-03279-6

Yuchen Liang, Xuelin Gu, Yifan Shi, Yiru Fang, Zhiguo Wu, Xiaoou Li

{"title":"Electrophysiological biomarkers based on CISANET characterize illness severity and suicidal ideation among patients with major depressive disorder.","authors":"Yuchen Liang, Xuelin Gu, Yifan Shi, Yiru Fang, Zhiguo Wu, Xiaoou Li","doi":"10.1007/s11517-024-03279-6","DOIUrl":"10.1007/s11517-024-03279-6","url":null,"abstract":"Major depressive disorder (MDD) is a significant neurological disorder that imposes a substantial burden on society, characterized by its high recurrence rate and associated suicide risk. Clinical diagnosis, which relies on interviews with psychiatrists and questionnaires used as auxiliary diagnostic tools, lacks precision and objectivity in diagnosing MDD. To address these challenges, this study proposes an assessment method based on EEG. It involves calculating the phase lag index (PLI) in alpha and gamma bands to construct functional brain connectivity. This method aims to find biomarkers to assess the severity of MDD and suicidal ideation. The convolutional inception with shuffled attention network (CISANET) was introduced for this purpose. The study included 61 patients with MDD, who were classified into mild, moderate, and severe levels based on depression scales, and the presence of suicidal ideation was evaluated. Two paradigms were designed for the study, with EEG analysis focusing on 32 selected electrodes to extract alpha and gamma bands. In the gamma band, the classification accuracy reached 77.37% in the visual paradigm and 80.12% in the auditory paradigm. The average accuracy in classifying suicidal ideation was 93.60%. The findings suggest that gamma bands can be used as potential biomarkers differentiating illness severity and identifying suicidal ideation of MDD, and that objective assessment methods can effectively assess MDD The objective assessment method can effectively assess the severity of MDD and identify suicidal ideation of MDD patients, which provides a valuable theoretical basis for understanding the biological characteristics of MDD.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1731-1748"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030238","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}

引用次数: 0

Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data. 利用有限视场光声断层成像数据展开乳腺癌检测的深度学习。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-01 Epub Date: 2025-01-25 DOI: 10.1007/s11517-025-03302-4

Mary John, Imad Barhumi

{"title":"Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.","authors":"Mary John, Imad Barhumi","doi":"10.1007/s11517-025-03302-4","DOIUrl":"10.1007/s11517-025-03302-4","url":null,"abstract":"Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings present significant challenges for image reconstruction quality and computational efficiency. This paper introduces novel unrolled deep learning networks based on split Bregman total variation (SBTV) and relaxed basis pursuit alternating direction method of multipliers (rBP-ADMM) algorithms to address these challenges. Our approach combines transfer learning from full-view to limited-view scenarios with U-Net denoiser integration, achieving state-of-the-art reconstruction quality (MS-SSIM> 0.95) while reducing reconstruction time by 92% compared to traditional methods. The effectiveness of different sensor configurations is analyzed through restricted isometry property (RIP) analysis and coherence values, demonstrating that semicircular arrays achieve a RIP constant of 0.76 and coherence of 0.77, closely approximating full-view performance (RIP: 0.75, coherence: 0.78). These metrics validate the theoretical foundation for accurate sparse signal recovery in limited-view scenarios. Comprehensive evaluations across semicircular, concave, and convex sensor arrangements show that the proposed U-SBTV network consistently outperforms existing methods, particularly when combined with the U-Net denoiser. This advancement in limited-view PAT reconstruction brings the technology closer to practical clinical application, potentially improving early breast cancer detection capabilities.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1777-1795"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042513","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}

引用次数: 0

Automatic skeletal maturity grading from pelvis radiographs by deep learning for adolescent idiopathic scoliosis. 通过深度学习对青少年特发性脊柱侧凸骨盆x线片进行骨骼成熟度自动分级。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-06-01 Epub Date: 2025-01-22 DOI: 10.1007/s11517-025-03283-4

Yang Zhao, Junhua Zhang, Hongjian Li, Qiyang Wang, Yungui Li, Zetong Wang

{"title":"Automatic skeletal maturity grading from pelvis radiographs by deep learning for adolescent idiopathic scoliosis.","authors":"Yang Zhao, Junhua Zhang, Hongjian Li, Qiyang Wang, Yungui Li, Zetong Wang","doi":"10.1007/s11517-025-03283-4","DOIUrl":"10.1007/s11517-025-03283-4","url":null,"abstract":"Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed of the spine. A child's Risser stage of skeletal maturity must be carefully considered for AIS evaluation and treatment. However, there are intra-observer and inter-observer inaccuracies in the Risser stage manual assessment. A multi-task learning approach is proposed to address the low precision issue of manual assessment. With our developed multi-task learning approach, the iliac area is extracted and forwarded to the improved Swin Transformer for Risser stage assessment. The spatial and channel reconstruction convolutional Swin block is adapted to each stage of the Swin Transformer to achieve better performance. The Risser stage assessment based on iliac region extraction had an overall accuracy of 81.53%. The accuracy increased in comparison to ResNet50, ResNet101, Uni-former, Next-ViT, ConvNeXt, and the original Swin Transformer by 5.85%, 4.6%, 3.6%, 2.7%, 2.25%, and 1.8%, respectively. The Grad-CAM visualization is used to understand the interpretability of our proposed model. The results show that the proposed multi-task learning strategy performs well on the Risser stage assessment. Our proposed automatic Risser stage assessment method benefits the clinical evaluation of AIS. Project address: https://github.com/xyz911015/Risser-stage-assessment.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1571-1583"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015124","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}

引用次数: 0