Medical & Biological Engineering & Computing最新文献

{"title":"Diagnosis classification in EMR data using latent representations and SNOMED-CT mapping for improved medical data integration.","authors":"Sungsu Oh, In Ho Han, Jae Il Lee, Byung Kwan Choi, Hyunsu Lee","doi":"10.1007/s11517-026-03654-5","DOIUrl":"10.1007/s11517-026-03654-5","url":null,"abstract":"<p><p>Integrating medical data across hospitals has become a critical challenge in medical informatics, largely due to the heterogeneity of electronic medical record (EMR) systems. This study aims to address this issue by developing a diagnosis classification model that automatically maps diagnosis spans in EMR data to the standardized clinical ontology Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). We trained an embedding-based model, ClinicalBERT, on EMR data to obtain latent representations of diagnosis spans. These representations were aligned with SNOMED-CT fully specified names (FSNs) through mean squared error (MSE)-based fine-tuning and subsequently used to construct a downstream classification model. In addition, we analyzed the latent representations to investigate semantic alignment and structural characteristics in the latent space. The proposed model achieved an accuracy of 0.934, a weighted F1-score of 0.923, and a macro-averaged F1-score of 0.823 on 273 SNOMED-CT classes. Despite improved semantic alignment, the fine-tuned model demonstrated performance comparable to the base ClinicalBERT model, while remaining competitive with state-of-the-art approaches such as SapBERT (accuracy: 0.944) and BioSyn (accuracy: 0.931). Representation analysis further revealed improved clustering coherence, as evidenced by reduced similarity distances and more distinct class separation. This approach demonstrates strong potential for scalable and privacy-preserving medical concept normalization in real-world clinical environments. Fine-tuning improved semantic alignment, reducing the average Manhattan and cosine distances by 36.2% and 53.1%, respectively. Moreover, the integration of classification and representation analysis provides insights into the trade-off between semantic alignment and discriminative performance.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3529-3542"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702682","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":"Development and implementation of an automated micrometric bone distraction system based in a fuzzy logic controller for an Aybar-type external fixator.","authors":"Joao Marcelo Valdivia De La Gala, Andrea Sophia Valladares Chavez, Carlos Hernan Inga Espinoza","doi":"10.1007/s11517-026-03665-2","DOIUrl":"https://doi.org/10.1007/s11517-026-03665-2","url":null,"abstract":"<p><p>Bone distraction is a widely applied orthopedic procedure based on Ilizarov's tension-stress principle, which requires high precision and accuracy to guarantee successful bone regeneration. In many low resource locations, the Aybar-type external fixator is the only viable treatment option due to its low cost and accessibility. However, its manual procedure introduces human error, reduces precision, and compromises treatment outcomes. To address this problem, this study develops and implements an automated micrometric distraction system for an Aybar-type fixator, aiming to provide a low cost yet highly accurate alternative, particularly focused on pediatric patients, for whom precision is critical to ensure proper callus formation and bone growth. Four control strategies were designed and evaluated: an ON-OFF controller, a Proportional-Integral (PI) controller, a Linear Quadratic Integrator (LQI), and a Fuzzy Logic Controller (FLC). The mechanical platform integrates a Direct Current (DC) motor, encoder, and worm gear with 1.25 mm pitch, while simulations and experimental tests were performed under two distraction regimens: 1 mm/day in four increments and 1 mm/day in sixty increments. Results indicate that the FLC achieves the best performance in coarse step elongation, while the LQI provides superior results in high-resolution protocols. These findings demonstrate that it is possible to enhance the precision and reproducibility of the Aybar external fixator through low cost automation. The proposed solution contributes to improving treatment quality for pediatric patients in contexts where advanced imported systems are unavailable, filling a critical gap between affordability and clinical efficacy.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867567","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":"Classification of intracardiac heart sounds for ventricular and atrial fibrillation based on feature fusion and voting ensemble learning.","authors":"Yiming Xin, Rongfeng Zhang, Yiheng Yang, Yunlong Xia, Zhe Chen, Fuliang Yin","doi":"10.1007/s11517-026-03671-4","DOIUrl":"https://doi.org/10.1007/s11517-026-03671-4","url":null,"abstract":"<p><p>Arrhythmias, a significant category of cardiovascular diseases, including atrial fibrillation (AF) and ventricular fibrillation (VF), can result in severe complications and fatal outcomes, necessitating timely detection and intervention. This study proposes an intracardiac heart sound classification method based on feature fusion and voting ensemble learning for accurate discrimination between AF and VF. Using high-fidelity sonocardiogram dataset, the signals are preprocessed using a window function and a pre-emphasis filter. Then, the four types of features, including Mel-frequency cepstral coefficients, envelope autocorrelation, Hilbert-Huang transform, and wavelet scattering transform are extracted. Next, feature fusion and dimensionality reduction are performed using the maximum relevance minimum redundancy algorithm to streamline inputs for machine learning. Finally, a voting ensemble approach incorporating K-nearest neighbors, support vector machines, and artificial neural networks as base classifiers is employed to reliably distinguish between AF and VF. Experimental results demonstrate that the proposed method achieves an accuracy of 96.7%, with precision, recall, and F1 score of 96.5%, 96.8%, and 96.6%, respectively. By leveraging the complementary strengths of each base classifier, the proposed framework effectively classifies AF and VF from intracardiac heart sounds, thereby offering a reliable tool for abnormal intracardiac heart sound monitoring systems.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857800","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":"A low-cost imaging platform for quantitative characterisation of scaffold surface macrotopography with potential application in craniofacial tissue engineering.","authors":"X Marimon, E Saman-Sakkal, R Rodriguez, A Portela, M Cerrolaza, M A Mateos, R Pérez","doi":"10.1007/s11517-026-03661-6","DOIUrl":"https://doi.org/10.1007/s11517-026-03661-6","url":null,"abstract":"<p><p>Craniofacial bone regeneration is a demanding application of tissue engineering (TE) in which scaffold architecture-pore size, shape and spatial distribution-is a determinant of regenerative success, so that reliable geometric quality control is a prerequisite for translation. Conventional assessment of printed scaffold geometry is largely manual, time-consuming and subject to operator variability. Here we present a low-cost, semi-automated imaging platform that combines custom 3D-printed hardware-a 12.3 MP Raspberry Pi High Quality Camera with a 6 mm CS-mount lens, an adjustable monopod and a ring illuminator-with a dedicated image-processing pipeline. The system acquires a single zenithal image of a scaffold and segments it to quantify the number, area, perimeter and compactness of the pores of the uppermost printed layer; it therefore characterises two-dimensional surface macrotopography and does not resolve internal three-dimensional architecture or pore interconnectivity. On a printed reference grid the platform was highly repeatable (coefficient of variation, CV = 1.33%) but systematically underestimated pore area by 11.6%, a reproducible bias that can be removed by calibration. On a 3D-printed PLA scaffold, repeated acquisitions agreed to within CV = 0.01-2.48% (N = 5), whereas manual measurement of the same specimen by three experienced operators gave an inter-user CV of 11.5-22.8% and an intra-user CV of 0-12.7%. Feasibility was further demonstrated on two silica-gelatin hybrid bioink scaffolds and on a silica-based scaffold with non-linear pore boundaries. The total hardware cost is approximately €170. By reducing user-dependent variability under the conditions tested, and at a cost accessible to standard laboratories, the platform provides a practical quality-control tool for scaffold fabrication, with potential application to craniofacial tissue engineering.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851756","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":"Impact of EMG-based interfaces for hand motor rehabilitation in stroke: A systematic review with evidence gap map and meta-analysis.","authors":"Cristian D Guerrero-Mendez, Natanael Pereira Batista, José Osmar Alves Filho, Carina Marconi Germer, Leonardo Abdala Elias","doi":"10.1007/s11517-026-03660-7","DOIUrl":"https://doi.org/10.1007/s11517-026-03660-7","url":null,"abstract":"<p><p>Electromyography (EMG)-based interventions have been proposed to improve hand/wrist motor recovery after stroke; however, their clinical value remains uncertain, as evidence for their effectiveness is inconsistent. This systematic review with meta-analysis evaluated the impact of different EMG-based rehabilitation approaches compared with non-EMG-based interventions in individuals with stroke. Seven databases were searched from inception to November 17, 2025, in accordance with PRISMA guidelines. Randomized controlled trials comparing EMG-based and non-EMG-based interventions for post-stroke hand or wrist rehabilitation were eligible. Interventions were classified into four categories: EMG-triggered electrical stimulation (EMG-ES), EMG-ES combined with other interventions, EMG-based robotic platforms, and EMG biofeedback combined with other interventions. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Meta-analyses were conducted for clinical outcomes classified according to the International Classification of Functioning, Disability and Health (ICF) domains at post-intervention, follow-up <3 months, and follow-up ≥3 months. Thirty-eight studies involving 1,132 participants were included, of which 31 contributed to the meta-analyses. Eight studies were judged to have a low risk of bias, 13 to have some concerns, and 17 to have a high risk of bias. EMG-ES alone was not superior to conventional therapy. In contrast, EMG-ES combined with other interventions produced greater improvements across all ICF domains. EMG-based robotic platforms reduced wrist and finger spasticity, with effects sustained at long-term follow-up. EMG biofeedback primarily improved outcomes within the ICF body functions and structures domain. The effectiveness of EMG-based interventions depends on the rehabilitation strategy. EMG-ES, combined with other therapies, may be prioritized, whereas EMG-based robotic platforms may be useful in reducing spasticity. Further high-quality studies are needed to establish the clinical effectiveness of EMG-biofeedback approaches.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851735","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":"GDLS-D: two-stage medical surface reconstruction via geometric diffusion least squares and iterative deformation optimization.","authors":"Jing Shi, Xianyue He, Sichun Chen, Yuan Xing, Jisi Tang, Fei Wang, Xiangyun Ren","doi":"10.1007/s11517-026-03664-3","DOIUrl":"https://doi.org/10.1007/s11517-026-03664-3","url":null,"abstract":"<p><p>Surface representations of anatomical structures are crucial for medical applications including preoperative planning and biomechanical simulations, yet current methods face challenges from limited datasets and staircase artifacts. This study proposes a training-free two-stage optimization framework for accurate 3D reconstruction. First, a method named geometric diffusion least squares is proposed to generate the template which is homeomorphic to the anatomical structure. Second, the template mesh is iteratively refined through continuous mesh deformation to obtain the final optimal result. Evaluations on liver, ilium, and vertebrae achieved bi-directional chamfer distances (L1 norm) of 3.675 mm, 2.654 mm, and 2.508 mm, respectively. Compared to state-of-the-art deep learning methods (Voxel2Mesh and MeshDeformNet), our approach improves average accuracy by 33.11% across three metrics, effectively suppressed staircase artifacts, and demonstrated promising generalization across anatomical structures. While traditional methods yield better accuracy metrics due to overfitting to mask boundaries, our method produces watertight meshes with significantly lower aspect ratios and no staircase artifacts. The proposed framework overcomes current limitations, offering a precise, generalizable solution for surface reconstruction of volumetric medical images.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148834546","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":"Automated classification of musculoskeletal abnormalities using a dynamic ensemble of deep vision models.","authors":"Omid Mirzaei, Ahmet Ilhan, Serife Kaba, Boran Sekeroglu","doi":"10.1007/s11517-026-03663-4","DOIUrl":"https://doi.org/10.1007/s11517-026-03663-4","url":null,"abstract":"<p><p>Detecting and classifying musculoskeletal disorders is vital to preventing long-term disability and limited mobility; however, this procedure is challenging due to the shortage of radiologists. Existing artificial intelligence technologies, particularly deep vision models, aim to support specialists in rapid, accurate diagnosis. In this study, a dynamic ensemble model is developed using a Probability-based Dynamic Ensemble (PDE) approach with two vision models, ConvNeXt Base and Swin Transformer. Unlike conventional ensemble methods that employ fixed averaging or voting strategies, the proposed PDE framework learns both the contributions of individual backbone models and the optimal decision threshold directly from the training data, thereby exploiting the strengths of backbone models. Regional and image-based experiments are performed to detect abnormality in X-ray images of patients for seven anatomical regions of the MURA dataset. The radiographs are preprocessed and enhanced, and the models are trained individually. Dynamically determined probabilities of each model are used to construct and train an ensemble model with learnable weights and a threshold to mitigate the limitations of the backbone models. The proposed model obtained superior results in the majority of region-based experiments, with accuracies ranging from 0.826 to 0.920, and 0.843 in image-based experiments. Comparative statistical analyses are performed, and explainable AI tools are implemented to analyze clinical deployment. The obtained results demonstrate that the dynamically determined probabilities enable the ensemble model to optimize the balance between sensitivity and specificity by enhancing the individual recognition abilities of pre-trained models.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800767","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":"Mel-DEPTHS: a benchmark dataset for epidermis and tumor segmentation for melanoma staging.","authors":"Yasemin Topuz, M Taha Gökcan, A Mine Önenerk Men, Serdar Yıldız, İpek Sertbudak, Serhat Kaymaz, Özge Gökbaşı, Nil Urgancı, Nurullah Çalık, Övgü Aydın Ülgen, Songül Varlı","doi":"10.1007/s11517-026-03656-3","DOIUrl":"10.1007/s11517-026-03656-3","url":null,"abstract":"<p><p>Accurate delineation of epidermis and tumor boundaries is central to melanoma staging, yet pixel-level annotation on whole-slide images (WSIs) is labor-intensive and inconsistent across observers. Advancing this field requires standardized, publicly available benchmarks with expert-validated labels. We introduce Mel-DEPTHS, a new benchmark dataset for epidermis and tumor segmentation, designed to accelerate and standardize research for automated melanoma staging. Mel-DEPTHS comprises 50 anonymized melanoma WSIs (40x, 0.25[Formula: see text]m/pixel) with pixel-level masks for epidermis and tumor regions. Clinical variables such as invasion depth, ulceration, and pT stage are provided alongside fixed train/test partitions to ensure reproducibility. To mitigate annotation burden, we developed an Expert-Supervised Iterative Self-Training (ESIST) protocol: a pretrained model generates pseudo-labels, which dermatopathologists iteratively refine for retraining. We benchmarked six state-of-the-art segmentation models (UNet, UNet++, UNet3+, UPerNet, TransUNet, ConvUNeXt) using WSI-level precision, recall, IoU, and Dice. TransUNet achieved the best performance, closely followed by ConvUNeXt and UperNet. Three-fold cross-validation also confirmed consistent model rankings and label robustness. Mel-DEPTHS provides the fidelity and diversity necessary for clinically meaningful segmentation. It establishes a standardized benchmark and fosters reproducibility in computational pathology.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727860","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":"Multi-source information fusion using CNN-LSTM-Attention for bone layer recognition in robotic orthopedic grinding.","authors":"Kai Yang, Qingxuan Jia, Juxiang Huang, Gang Chen, Chao Feng, Yunfeng Xu","doi":"10.1007/s11517-026-03649-2","DOIUrl":"10.1007/s11517-026-03649-2","url":null,"abstract":"<p><p>Epiphyseal opening requires precise localization for bony bridge resection. Traditional surgery is challenged by unclear bony bridge boundary localization and inaccurate grinding precision, while existing robot-assisted operations predominantly focus on pre-operative localization with limited intraoperative autonomous decision-making capabilities. To address this, we propose a multi-source information fusion framework using a CNN-LSTM-Attention network for real-time bone layer differentiation, specifically identifying idling, cancellous, and cortical bone states, during robotic orthopedic grinding. First, the mapping relationship between acceleration, force and acoustic signals and bone density is analyzed, serving as the basis of bone layer recognition. Second, a three-channel parallel late-feature-fusion CNN-LSTM-Attention network is established. The dataset was constructed from multiple independent grinding trials with trial-wise splitting to prevent data leakage under representative robotic grinding conditions. Over five independent runs, the proposed method achieves a test accuracy of 95.06% ± 0.53%, significantly outperforming comparison models including CNN, CNN-Attention, CNN-LSTM, and non-deep-learning baselines (Random Forest, Extra Trees, SVM, KNN). Ablation studies isolating CNN, LSTM, and Attention contributions are provided, and the learned Squeeze-and-Excitation attention weights are visualized to confirm dynamic cross-modal feature weighting. With approximately 0.32 million parameters, the model introduces an inference latency of 2.8 ms on a desktop CPU and 1.2 ms on an NVIDIA Jetson Orin, well below the 50 ms robot control cycle, confirming real-time feasibility. Additionally, we explore single-signal, dual-signal, and triple-signal fusion settings, demonstrating that tri-modal fusion achieves the best performance.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671133","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":"Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.","authors":"Riccardo Munafò, Simone Saitta, Davide Tondi, Giacomo Ingallina, Paolo Denti, Francesco Maisano, Eustachio Agricola, Emiliano Votta","doi":"10.1007/s11517-024-03275-w","DOIUrl":"10.1007/s11517-024-03275-w","url":null,"abstract":"<p><p>Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used. The Teacher model, an ensemble of three convolutional neural networks, is trained on end-systole and end-diastole frames and is used to generate MV pseudo-segmentations on intermediate frames of the cardiac cycle. The pseudo-annotated frames augment the Student model's training set, improving segmentation accuracy and temporal consistency. The Student outperforms individual Teachers, achieving a Dice score of 0.82, an average surface distance of 0.37 mm, and a 95% Hausdorff distance of 1.72 mm for MV leaflets. The Student model demonstrates reliable frame-by-frame MV segmentation, accurately capturing leaflet morphology and dynamics throughout the cardiac cycle, with a significant reduction in inference time compared to the ensemble. This approach greatly reduces manual annotation workload and ensures reliable, repeatable, and time-efficient MV analysis. Our method holds strong potential to enhance the precision and efficiency of MV diagnostics and treatment planning in clinical settings.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2933-2948"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967338","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}