Computer methods and programs in biomedicine最新文献

{"title":"Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study","authors":"Fen-Fen Li ,&nbsp;Gao-Xiang Li ,&nbsp;Xin-Xin Yu ,&nbsp;Zu-Hui Zhang ,&nbsp;Ya-Na Fu ,&nbsp;Shuang-Qing Wu ,&nbsp;Ying Wang ,&nbsp;Chun Xiao ,&nbsp;Yu-Feng Ye ,&nbsp;Min Hu ,&nbsp;Qi Dai","doi":"10.1016/j.cmpb.2025.108814","DOIUrl":"10.1016/j.cmpb.2025.108814","url":null,"abstract":"<div><h3>Objective</h3><div>Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based image quality control system to classify slit-lamp images, improving diagnostic accuracy and efficiency, particularly in telemedicine applications.</div></div><div><h3>Design</h3><div>Cross-sectional study.</div></div><div><h3>Methods</h3><div>Our Zhejiang Eye Hospital dataset comprised 2982 slit-lamp images as the internal dataset. Two external datasets were included: 13,554 images from the Aier Guangming Eye Hospital (AGEH) and 9853 images from the First People’s Hospital of Aksu District in Xinjiang (FPH of Aksu). We developed a Hybrid Prior-Net (HP-Net), a novel network that combines a ResNet-based classification branch with a prior knowledge branch leveraging Hough circle transform and frequency domain blur detection. The two branches’ features are channel-wise concatenated at the fully connected layer, enhancing representational power and improving the network’s ability to classify eligible, misaligned, blurred, and underexposed corneal images. Model performance was evaluated using metrics such as accuracy, precision, recall, specificity, and F1-score, and compared against the performance of other deep learning models.</div></div><div><h3>Results</h3><div>The HP-Net outperformed all other models, achieving an accuracy of 99.03 %, precision of 98.21 %, recall of 95.18 %, specificity of 99.36 %, and an F1-score of 96.54 % in image classification. The results demonstrated that HP-Net was also highly effective in filtering slit-lamp images from the other two datasets, AGEH and FPH of Aksu with accuracies of 97.23 % and 96.97 %, respectively. These results underscore the superior feature extraction and classification capabilities of HP-Net across all evaluated metrics.</div></div><div><h3>Conclusions</h3><div>Our AI-based image quality control system offers a robust and efficient solution for classifying corneal images, with significant implications for telemedicine applications. By incorporating slightly blurred but diagnostically usable images into training datasets, the system enhances the reliability and adaptability of AI tools for medical imaging quality control, paving the way for more accurate and efficient diagnostic workflows.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108814"},"PeriodicalIF":4.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CUAMT: A MRI semi-supervised medical image segmentation framework based on contextual information and mixed uncertainty","authors":"Hanguang Xiao,&nbsp;Yangjian Wang,&nbsp;Shidong Xiong,&nbsp;Yanjun Ren,&nbsp;Hongmin Zhang","doi":"10.1016/j.cmpb.2025.108755","DOIUrl":"10.1016/j.cmpb.2025.108755","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labeling workload. However, existing consistency semi-supervised segmentation models mainly focus on investigating more complex consistency strategies and lack efficient utilization of volumetric contextual information, which leads to vague or uncertain understanding of the boundary between the object and the background by the model, resulting in ambiguous or even erroneous boundary segmentation results.</div></div><div><h3>Methods:</h3><div>For this reason, this study proposes a hybrid uncertainty network CUAMT based on contextual information. In this model, a contextual information extraction module CIE is proposed, which learns the connection between image contexts by extracting semantic features at different scales, and guides the model to enhance learning contextual information. In addition, a hybrid uncertainty module HUM is proposed, which guides the model to focus on segmentation boundary information by combining the global and local uncertainty information of two different networks to improve the segmentation performance of the networks at the boundary.</div></div><div><h3>Results:</h3><div>In the left atrial segmentation and brain tumor segmentation dataset, validation experiments were conducted on the proposed model. The experiments show that our model achieves 89.84%, 79.89%, and 8.73 on the Dice metric, Jaccard metric, and 95HD metric, respectively, which significantly outperforms several current SOTA semi-supervised methods. This study confirms that the CIE and HUM strategies are effective.</div></div><div><h3>Conclusion:</h3><div>A semi-supervised segmentation framework is proposed for medical image segmentation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108755"},"PeriodicalIF":4.9,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis","authors":"Deng Hui ,&nbsp;Li Pan","doi":"10.1016/j.cmpb.2025.108776","DOIUrl":"10.1016/j.cmpb.2025.108776","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of the gut microbiome in regulating various physiological processes, including sleep. This study investigates the relationship between gut microbiome composition and sleep quality in patients with endometriosis using machine learning (ML) techniques named artificial neural network (ANN) and support vector regression (SVR) with several hybrid approaches as ML-based ANN and SVR coupled with optimization using partial swarm optimization (PSO) and an improved PSO. We analyzed data from 200 endometriosis patients, encompassing a diverse range of age, Body mass index (BMI), symptom severity, and lifestyle factors. Key gut microbiota, including Bacteroides, Prevotella, Ruminococcus, Lactobacillus, Faecalibacterium, and Akkermansia, were quantified. Additionally, lifestyle variables such as diet quality, physical activity level, daily caloric intake, fiber intake, sugar intake, alcohol consumption, smothking status are applied for predictions of sleep quality.</div></div><div><h3>Methods</h3><div>Advanced machine learning models, including Support Vector Machines (SVM), Neural Networks (NN) were employed to analyze the data. Two hybrid machine learning method named SVM- improved <span><span>particle swarm optimization</span><svg><path></path></svg></span> (IPSO) and NN-IPSO as hybrid SVR and NN combined with an IPSO is proposed for prediction of sleep quality. In the enhanced PSO, a local search position of particle is developed for better calibration of the parameters in NN and SVM applied in hybrid models. In local search of improved PSO, the best particle is applied with a random adjusting process applied for new particles.</div></div><div><h3>Results and Conclusion</h3><div>These several ML methods showed that revealed significant associations between specific gut microbiota and sleep quality in endometriosis patients. The hybrid methods are more accurate than traditional machine learning methods-based NN and SVR that these methods exhibit a strong predictive tendency by using the local search. Exploring the underlying mechanisms through which the gut microbiome influences sleep could provide deeper insights into potential therapeutic targets.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108776"},"PeriodicalIF":4.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated strength-interval curve generation using actors","authors":"Raymond J. Spiteri,&nbsp;Joyce Reimer,&nbsp;Kyle Klenk","doi":"10.1016/j.cmpb.2025.108784","DOIUrl":"10.1016/j.cmpb.2025.108784","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Strength-interval (SI) curves are used by physiologists to quantify the response of excitable tissue as a function of the strength and timing of an electrical stimulus. In the context of cardiac electrophysiology, SI curves characterize the refractoriness of cardiac tissue as a function of inter-stimulus interval length. Although conventionally collected experimentally, this type of information can now more conveniently be obtained through computational simulation. Nevertheless, the computational generation of SI curves can be labor-intensive and time-consuming due to its iterative nature, the number and size of computations required, and the amount of manual researcher intervention involved. The objective of this study is to use the Actor Model of concurrent computation to automate the process of SI curve generation, relieving much of the burden from the researcher while maximizing the use of available computational resources.</div></div><div><h3>Methods:</h3><div>The C++ Actor Framework is used to create an automated tool for controlling the <em>openCARP</em> simulation platform. An SI curve is generated for the bidomain model of electrophysiology through the use of sophisticated parallelization techniques, e.g., dynamic information passing between parallel simulations, facilitated by the use of actors. Computational resource management is optimized by the dynamic monitoring, assessment, and reallocation based on each actor’s current simulation state in relation to all other actors.</div></div><div><h3>Results:</h3><div>A bidomain SI curve with 31 data points that takes 27.5 h to compute conventionally using 80 CPU cores is now generated in 15.4 h. This is over 40% faster than using conventional parallel programming techniques with MPI. Furthermore, it requires no researcher intervention, which can add significantly to the time to solution.</div></div><div><h3>Conclusion:</h3><div>Novel parallelization techniques enabled via the Actor Model significantly improve the efficiency of computational SI curve generation, both from the viewpoints of computation and labor intensiveness. This improvement in efficiency has implications for future studies involving cardiac refractory tissue, along with other types of excitable tissue, including the rapid generation of both general and patient-specific SI curves and the use of these curves for design and <em>in silico</em> testing of new therapeutic tools such as personalized pacemakers.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108784"},"PeriodicalIF":4.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-task neural network for full waveform ultrasonic bone imaging","authors":"Peiwen Li ,&nbsp;Tianyu Liu ,&nbsp;Heyu Ma ,&nbsp;Dan Li ,&nbsp;Chengcheng Liu ,&nbsp;Dean Ta","doi":"10.1016/j.cmpb.2025.108807","DOIUrl":"10.1016/j.cmpb.2025.108807","url":null,"abstract":"<div><h3>Background and objective</h3><div>It is a challenging task to use ultrasound for bone imaging, as the bone tissue has a complex structure with high acoustic impedance and speed-of-sound (SOS). Recently, full waveform inversion (FWI) has shown promising imaging for musculoskeletal tissues. However, the FWI showed a limited ability and tended to produce artifacts in bone imaging because the inversion process would be more easily trapped in local minimum for bone tissue with a large discrepancy in SOS distribution between bony and soft tissues. In addition, the application of FWI required a high computational burden and relatively long iterations. The objective of this study was to achieve high-resolution ultrasonic imaging of bone using a deep learning-based FWI approach.</div></div><div><h3>Method</h3><div>In this paper, we proposed a novel network named CEDD-Unet. The CEDD-Unet adopts a Dual-Decoder architecture, with the first decoder tasked with reconstructing the SOS model, and the second decoder tasked with finding the main boundaries between bony and soft tissues. To effectively capture multi-scale spatial-temporal features from ultrasound radio frequency (RF) signals, we integrated a Convolutional LSTM (ConvLSTM) module. Additionally, an Efficient Multi-scale Attention (EMA) module was incorporated into the encoder to enhance feature representation and improve reconstruction accuracy.</div></div><div><h3>Results</h3><div>Using the ultrasonic imaging modality with a ring array transducer, the performance of CEDD-Unet was tested on the SOS model datasets from human bones (noted as Dataset1) and mouse bones (noted as Dataset2), and compared with three classic reconstruction architectures (Unet, Unet++, and Att-Unet), four state-of-the-art architecture (InversionNet, DD-Net, UPFWI, and DEFE-Unet). Experiments showed that CEDD-Unet outperforms all competing methods, achieving the lowest MAE of 23.30 on Dataset1 and 25.29 on Dataset2, the highest SSIM of 0.9702 on Dataset1 and 0.9550 on Dataset2, and the highest PSNR of 30.60 dB on Dataset1 and 32.87 dB on Dataset2. Our method demonstrated superior reconstruction quality, with clearer bone boundaries, reduced artifacts, and improved consistency with ground truth. Moreover, CEDD-Unet surpasses traditional FWI by producing sharper skeletal SOS reconstructions, reducing computational cost, and eliminating the reliance for an initial model. Ablation studies further confirm the effectiveness of each network component.</div></div><div><h3>Conclusion</h3><div>The results suggest that CEDD-Unet is a promising deep learning-based FWI method for high-resolution bone imaging, with the potential to reconstruct accurate and sharp-edged skeletal SOS models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108807"},"PeriodicalIF":4.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prescription data and demographics: An explainable machine learning exploration of colorectal cancer risk factors based on data from Danish national registries","authors":"Abdolrahman Peimankar ,&nbsp;Olav Sivertsen Garvik ,&nbsp;Bente Mertz Nørgård ,&nbsp;Jens Søndergaard ,&nbsp;Dorte Ejg Jarbøl ,&nbsp;Sonja Wehberg ,&nbsp;Søren Paludan Sheikh ,&nbsp;Ali Ebrahimi ,&nbsp;Uffe Kock Wiil ,&nbsp;Maria Iachina","doi":"10.1016/j.cmpb.2025.108774","DOIUrl":"10.1016/j.cmpb.2025.108774","url":null,"abstract":"<div><h3>Objectives:</h3><div>Despite substantial advancements in both treatment and prevention, colorectal cancer continues to be a leading cause of global morbidity and mortality. This study investigated the potential of using demographics and prescribed drug information to predict risk of colorectal cancer using a machine learning approach.</div></div><div><h3>Methods:</h3><div>Five different machine learning algorithms, including Logistic Regression, XGBoost, Random Forests, kNN, and Voting Classifier, were initially developed and evaluated for their predictive capabilities across various time horizons (3, 6, 12, and 36 months). To enhance transparency and interpretability, explainable techniques were employed to understand the model’s predictions and identify the relative contributions of factors like age, sex, social status, and prescribed medications, promoting trust and clinical insights. While all developed models, including simpler ones such as Logistic Regression, demonstrated comparable performance, the Voting Classifier, as an ensemble model, was selected for further investigation due to its inherent diversity and generalizability. This ensemble model combines predictions from multiple base models, reducing the risk of overfitting and improving the robustness of the final prediction.</div></div><div><h3>Results:</h3><div>The model demonstrated consistent performance across these time horizons, achieving a precision consistently above 0.99, indicating high ability in identifying patients at risk. However, the recall remained relatively low (around 0.6), highlighting the model’s limitations in comprehensively identifying all at risk patients, despite its high precision. This suggests additional investigations in future studies to further enhance the performance of the proposed model.</div></div><div><h3>Conclusion:</h3><div>Machine learning models can identify individuals at higher risk for developing colorectal cancer, enabling earlier interventions and personalized risk management strategies. However, further studies are needed before implementation in clinical practice.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108774"},"PeriodicalIF":4.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price","authors":"Chua Ming ,&nbsp;Geraldine JW Lee ,&nbsp;Yao Neng Teo ,&nbsp;Yao Hao Teo ,&nbsp;Xinyan Zhou ,&nbsp;Elizabeth SY Ho ,&nbsp;Emma MS Toh ,&nbsp;Marcus Eng Hock Ong ,&nbsp;Benjamin YQ Tan ,&nbsp;Andrew FW Ho","doi":"10.1016/j.cmpb.2025.108808","DOIUrl":"10.1016/j.cmpb.2025.108808","url":null,"abstract":"<div><h3>Background</h3><div>Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medical applications like disease pattern prediction and forecasting ED volume. Past studies have also found stock market price positively correlated with ED volume.</div></div><div><h3>Objective</h3><div>To determine whether incorporating Internet search data and stock market price to calendar and meteorological data can improve deep learning prediction of ED patient volumes, and whether hybrid deep learning architectures are better in prediction.</div></div><div><h3>Methods</h3><div>Permutations of various input variables namely calendar, meteorological, Google Trends online search data, Standard and Poor’s (S&amp;P) 500 index, and Straits Times Index (STI) data were incorporated into deep learning models long short-term memory (LSTM), one-dimensional convolutional neural network (1D CNN), stacked 1D CNN-LSTM, and five CNN-LSTM hybrid modules to predict daily Singapore General Hospital ED patient volume from 2010–2012.</div></div><div><h3>Results</h3><div>Incorporating STI to calendar and meteorological data improved performance of CNN-LSTM hybrid models. Addition of queried absolute Google Trends search terms to calendar and meteorological data improved performance of two out of five hybrid models. The best LSTM model across all predictor permutations had mean absolute percentage error of 4.8672 %.</div></div><div><h3>Conclusion</h3><div>LSTM provides strong predictive ability for daily ED patient volume. Local stock market index has potential to predict ED visits. Amongst predictors evaluated, calendar and meteorological data was sufficient for a relatively accurate prediction.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108808"},"PeriodicalIF":4.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining","authors":"Qing Zhang ,&nbsp;Xiaoxiao Wu ,&nbsp;Xiang Li ,&nbsp;Wei Ma ,&nbsp;Tongquan Wu ,&nbsp;Liuyue Li ,&nbsp;Fan Hu ,&nbsp;Yicheng Xie ,&nbsp;Xinglong Wu","doi":"10.1016/j.cmpb.2025.108809","DOIUrl":"10.1016/j.cmpb.2025.108809","url":null,"abstract":"<div><h3>Background</h3><div>Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, conventional supervised or semi-supervised methodologies are contingent on expert labels and incur substantial labeling costs, In contrast self-supervised pre-training strategies leverage unlabeled data during the pre-training phase and utilise a limited amount of labeled data in the fine-tuning phase, thereby greatly reducing labor costs. Furthermore, the fine-tuning does not need to learn the feature representations from scratch, enhancing the efficiency and transferability of the model.</div></div><div><h3>Methods</h3><div>The proposed methodology is outlined below. The deep learning framework, TransAnno-Net, is based on transfer learning and a Transformer architecture. It has been designed for efficient and accurate cell type annotations in large-scale scRNA-seq datasets of mouse lung organs. Specifically, TransAnno-Net is pre-trained on the scRNA-seq lung data of approximately 100,000 cells to acquire gene-gene similarities via self-supervised learning. It is then migrated to a relatively small number of datasets to fine-tune specific cell type annotation tasks. To address the issue of imbalance in cell types commonly observed in scRNA-seq data, we applied a random oversampling technique is applied to the fine-tuned dataset. This is done to mitigate the impact of distributional imbalance on the annotation outcomes.</div></div><div><h3>Results</h3><div>The experimental findings demonstrate that TransAnno-Net exhibits superior performance with an AUC of 0.979, 0.901, and 0.982, respectively, on three mouse lung datasets, outperforming eight state-of-the-art (SOTA) methods. In addition, TransAnno-Net demonstrates robust performance on cross-organ, cross-platform datasets, and is competitive with the fully supervised learning-based method.</div></div><div><h3>Conclusion</h3><div>The TransAnno-Net method is a highly effective cross-platform and cross-data set single-cell type annotation method for mouse lung tissues and supports cross-organ cell type annotation. This approach is expected to enhance the efficiency of research on the biological mechanisms of complex biological systems and diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108809"},"PeriodicalIF":4.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting physiological hip joint loads with inverse bone remodeling using clinically available QCT images","authors":"Sebastian Bachmann ,&nbsp;Gianluca Iori ,&nbsp;Kay Raum ,&nbsp;Dieter H. Pahr ,&nbsp;Alexander Synek","doi":"10.1016/j.cmpb.2025.108805","DOIUrl":"10.1016/j.cmpb.2025.108805","url":null,"abstract":"<div><h3>Background and objective</h3><div>Assessing joint-level loading conditions <em>in vivo</em> is challenging due to invasive measurement or complex computation. Inverse bone remodeling (IBR) offers a different approach by recovering the loading conditions directly from computed tomography (CT) images of the bone microstructure by finding the magnitudes to a set of load cases that load the bone optimally, i.e., maximally homogeneously. An efficient IBR method was recently proposed based on homogenized finite element (hFE) models. This study compared the hip joint load predictions of hFE-based IBR with clinically feasible CT scans to those obtained with the current gold standard, micro-FE-based IBR.</div></div><div><h3>Methods</h3><div>A set of 20 proximal femora was scanned <em>ex vivo</em>, both with a clinical quantitative CT (QCT) scanner (0.3 mm resolution) and an Xtreme CT II (XCT2) scanner (0.03 mm resolution). Finite element (FE) models with decreasing complexity were automatically created from those images. Micro-FE (µFE) models based on XCT2 images served as a baseline. hFE models based on the QCT images were created as clinically feasible models. Further intermediate models were created to trace sources of errors. IBR was applied to predict the optimal scaling factors of twelve unit load cases distributed over the femoral head.</div></div><div><h3>Results</h3><div>The predicted loads of the newly developed workflow for QCT images within IBR followed a trend seen previously with hFE models created from high-resolution images, such as XCT2. The peak load magnitudes of µFE and hFE-based IBR were well correlated (R²=76.8 %), and the overall distribution of the loads was similar. However, an additional peak load calibration was required to obtain quantitative agreement (CCC=82.8 %).</div></div><div><h3>Conclusions</h3><div>A thorough comparison of µFE-based IBR and hFE-based IBR using QCT data was performed for the first time. A clinically feasible workflow, including a peak calibration, is presented, allowing for fast prediction of physiological peak hip joint loads.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108805"},"PeriodicalIF":4.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems","authors":"Ciro Mennella ,&nbsp;Massimo Esposito ,&nbsp;Giuseppe De Pietro ,&nbsp;Umberto Maniscalco","doi":"10.1016/j.cmpb.2025.108792","DOIUrl":"10.1016/j.cmpb.2025.108792","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>This study introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises while minimizing performance disparities across subjects with varying motion-related characteristics.</div></div><div><h3>Methods:</h3><div>Advanced architectures designed to process multi-channel time series data using two parallel branches that extract features at different scales were developed and tested.</div></div><div><h3>Results:</h3><div>The results indicate that multiscale algorithms consistently outperform traditional approaches, demonstrating enhanced performance, particularly among patient subjects. Specifically, the multiscale tCNN and multiscale CNN-LSTM achieved accuracies of 91% and 90%, respectively, while the multiscale ConvLSTM maintained strong performance at 89%. Notably, the multiscale Transformer emerged as the most effective model, achieving the best average accuracy of 93%.</div></div><div><h3>Conclusions:</h3><div>This research underscores the need to explore advanced methods for enhancing activity recognition systems in healthcare, where accurate exercise monitoring and evaluation are becoming essential for effective and personalized treatment in telemedicine services.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"267 ","pages":"Article 108792"},"PeriodicalIF":4.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}