Medical & Biological Engineering & Computing最新文献

{"title":"A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets.","authors":"J Ramón Navarro-Cerdán, Pedro Pons-Suñer, Laura Arnal, Joaquim Arlandis, Rafael Llobet, Juan-Carlos Perez-Cortes, Francisco Lara-Hernández, Celeste Moya-Valera, Maria Elena Quiroz-Rodriguez, Gemma Rojo-Martinez, Sergio Valdés, Eduard Montanya, Alfonso L Calle-Pascual, Josep Franch-Nadal, Elias Delgado, Luis Castaño, Ana-Bárbara García-García, Felipe Javier Chaves","doi":"10.1007/s11517-025-03355-5","DOIUrl":"https://doi.org/10.1007/s11517-025-03355-5","url":null,"abstract":"<p><p>Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812799","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":"Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification.","authors":"Fatemeh Taheri, Kambiz Rahbar","doi":"10.1007/s11517-025-03346-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03346-6","url":null,"abstract":"<p><p>Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796846","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":"Assessing detector stability and image quality of thermal cameras on smartphones for medical applications: a comparative study.","authors":"Vladan Bernard, Erik Staffa, Jana Pokorná, Adam Šimo","doi":"10.1007/s11517-025-03348-4","DOIUrl":"https://doi.org/10.1007/s11517-025-03348-4","url":null,"abstract":"<p><strong>Introduction: </strong>Infrared thermography (IRT) has gained significant interest in medical applications for its potential in diagnosing various conditions. Smartphone-based IRT modules offer portability and affordability, leading to increased utilization in medical settings. However, differences in performance among these modules raise questions about their reliability for medical use.</p><p><strong>Materials and methods: </strong>This study compared three smartphone-based IRT modules (SmartIRT-Hikmicro, FLIR One Pro, and Seek Thermal CompactPRO-which, according to their datasheets, exhibit comparable quality and parameters. Temperature stability, surface temperature of the body, and spatial uniformity of provided images were assessed using calibrated black body measurements and surface temperature monitoring.</p><p><strong>Results: </strong>The Hikmicro module exhibited the most stable temperature readings, while FLIR One Pro showed the highest temperature increase over time. Seek Thermal CompactPRO demonstrated relatively better spatial uniformity. However, discrepancies in image resolution were noted, with FLIR One and Seek modules modifying image sizes through post-processing algorithms.</p><p><strong>Conclusion: </strong>While SmartIRT modules offer affordability and portability, their performance varies significantly. Temporal stability emerges as a critical factor, with the Hikmicro module demonstrating leadership in this aspect. Careful consideration and validation are necessary when selecting and utilizing SmartIRT modules for medical applications.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781336","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":"Advances in oxygen-releasing matrices for regenerative engineering applications.","authors":"Yihong Zeng, Can Yan, Guobao Chen, Zhongmin Chen, Fuping Wang","doi":"10.1007/s11517-025-03354-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03354-6","url":null,"abstract":"<p><p>In recent years, the effects of hypoxia on tissue repair have received wider attention with the deepening of tissue engineering research. Various oxygen supply strategies have wider applications in the field of tissue repair. Currently, commonly used methods of oxygen supply for defective tissues include hyperbaric oxygen (HBO) and oxygen-releasing materials. Between them, oxygen-releasing materials continuously and efficiently release oxygen from within the defective tissue. Compared with HBO, which may cause oxidative stress in healthy tissues, supplying oxygen via oxygen-releasing materials is safer because of their oxygen-releasing in situ and specific oxygen supply characteristics. However, there still exist some problems in the study of oxygen-releasing materials, such as cytotoxicity and the shortage of oxygen-releasing time. The current reviews on oxygen-releasing materials mostly elaborate on the principles of oxygen-releasing materials and lack a review of their preparation methods and applications. In this paper, different types of oxygen-releasing materials, such as hydrogels, microspheres, and layers, are reviewed concerning their applications, structures, current development status, and challenges.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781330","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":"ACE-Net: A-line coordinates encoding network for vascular structure segmentation in ultrasound images.","authors":"Beatriz Farola Barata, Guiqiu Liao, Gianni Borghesan, Keir McCutcheon, Johan Bennett, Benoit Rosa, Michel de Mathelin, Florent Nageotte, Michalina J Gora, Jos Vander Sloten, Emmanuel Vander Poorten, Diego Dall'Alba","doi":"10.1007/s11517-025-03323-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03323-z","url":null,"abstract":"<p><p>Ultrasound (US) imaging enables the evaluation of vascular structures in real time, and it can provide morphological and pathological information during US-guided procedures. Automatic prediction of vascular structure boundaries can help clinicians in locating and measuring target structures more accurately and efficiently. Most existing US segmentation methods use per-pixel classification or regression, which require post-processing to obtain contour coordinates. In this work, we present ACE-Net, a novel approach that directly predicts the contour coordinates for every scanning line (A-line) in US images. ACE-Net combines two main modules: a boundary regression module that predicts the upper and lower coordinates of the target area for each A-line, and an A-line classification module that determines whether an A-line belongs to the target area or not. We evaluated our method on three clinical US datasets using, among others, dice similarity coefficient (DSC) and inference time as performance metrics. Our method outperformed state-of-the-art segmentation methods in inference time while achieving superior or comparable performance in DSC. ACE-Net is publicly available at https://github.com/bfarolabarata/ace-net .</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765594","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":"An efficient network with state space model under evidential training for fetal echocardiography standard view recognition.","authors":"Changzhao Chen, Yiman Liu, Tongtong Liang, Shibin Lin, Xiaoxiang Han, Xiaohong Liu, Jing Yang, Yuqi Zhang, Xueping Yan","doi":"10.1007/s11517-025-03347-5","DOIUrl":"https://doi.org/10.1007/s11517-025-03347-5","url":null,"abstract":"<p><p>Fetal congenital heart disease (FCHD) represents a serious and prevalent congenital malformation. However, there exist notable regional disparities in the detection rates of fetal heart abnormalities. To enhance the diagnostic capabilities of ultrasound physicians in primary hospitals regarding fetal heart structures, the adoption of artificial intelligence technology to assist in acquiring high-quality, standard fetal echocardiographic images is of paramount importance. Currently, primary hospitals face challenges in recognizing standard views in fetal echocardiography, particularly under resource-constrained conditions. Efficient and accurate identification of fetal heart structures has become an urgent issue to address. Despite existing research efforts dedicated to the recognition of standard views in fetal echocardiography, current methods still suffer from limitations in computational complexity, feature extraction capabilities, and long-distance feature capturing, hindering their widespread application in ultrasound diagnosis at primary hospitals. Specifically, the literature lacks an efficient and robust model that can effectively balance high accuracy in standard view recognition with low computational complexity and fast inference times. The need for a model that can accurately capture long-distance features while maintaining efficiency is particularly acute in the context of primary hospitals, where resources are limited and the demand for accurate fetal heart assessments is high. To address these issues, the present study proposes an efficient network based on a state-space model trained with evidence for standard view recognition in fetal echocardiography. This method integrates a visual state space (VSS) model, which boasts powerful feature extraction capabilities and effective long-distance feature capturing, while significantly reducing computational complexity and facilitating efficient model inference. In the collected dataset, the proposed model achieved an accuracy of 99.32% and an F1-score of 99.29% in identifying eight standard views of fetal echocardiography. Furthermore, the model exhibited the lowest floating point operations per second (FLOPs), parameters, and inference time, while achieving the highest frames per second (FPS). This achievement not only provides a solid technical foundation for intelligent diagnosis of FCHD but also serves as an auxiliary tool for junior or novice sonographers at primary hospitals in acquiring basic views of fetal heart structures.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765595","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":"Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.","authors":"Tony Lin-Wei Chen, Anirudh Buddhiraju, Blake M Bacevich, Henry Hojoon Seo, Michelle Riyo Shimizu, Young-Min Kwon","doi":"10.1007/s11517-024-03258-x","DOIUrl":"10.1007/s11517-024-03258-x","url":null,"abstract":"<p><p>The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty (TKA), compromising care quality and cost efficiency. This study compared the performance of a machine learning (ML) model and ARC in predicting 30-day reoperation after primary TKA using a national-scale dataset. Data of 366,151 TKAs were acquired from the ACS-NSQIP database. A random forest model was derived using ARC build-in parameters from the training dataset via techniques of hyperparameter optimization and cross-validation. The predictive performance of random forest and ARC was evaluated by metrics of discrimination, calibration, and clinical utility using the testing dataset. The ML model demonstrated good discrimination and calibration (AUC: 0.72, slope: 1.18, intercept: - 0.14, Brier score: 0.012), outperforming ARC in all metrics (AUC: 0.51, slope: - 0.01, intercept: 0.01, Brier score: 0.135) including clinical utility measured by decision curve analyses. Age (> 67 years) and BMI (> 34 kg/m²) were the important predictors of reoperation. This study suggests the superiority of ML models in identifying individualized 30-day reoperation risk following TKA. ML models may be an adjunct prediction tool in enhancing patient-specific risk stratification and postoperative care management.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1131-1141"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802873","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 new sensing paradigm for the vibroacoustic detection of pedicle screw loosening.","authors":"Matthias Seibold, Bastian Sigrist, Tobias Götschi, Jonas Widmer, Sandro Hodel, Mazda Farshad, Nassir Navab, Philipp Fürnstahl, Christoph J Laux","doi":"10.1007/s11517-024-03235-4","DOIUrl":"10.1007/s11517-024-03235-4","url":null,"abstract":"<p><p>The current clinical gold standard to assess the condition and detect loosening of pedicle screw implants is radiation-emitting medical imaging. However, solely based on medical imaging, clinicians are not able to reliably identify loose implants in a substantial amount of cases. To complement medical imaging for pedicle screw loosening detection, we propose a new methodology and paradigm for the radiation-free, non-destructive, and easy-to-integrate loosening detection based on vibroacoustic sensing. For the detection of a loose implant, we excite the vertebra of interest with a sine sweep vibration at the spinous process and use a custom highly sensitive piezo vibration sensor attached directly at the screw head to capture the propagated vibration characteristics which are analyzed using a detection pipeline based on spectrogram features and a SE-ResNet-18. To validate the proposed approach, we propose a novel, biomechanically validated simulation technique for pedicle screw loosening, conduct experiments using four human cadaveric lumbar spine specimens, and evaluate our algorithm in a cross-validation experiment. The proposed method reaches a sensitivity of <math><mrow><mn>91.50</mn> <mo>±</mo> <mn>6.58</mn> <mo>%</mo></mrow> </math> and a specificity of <math><mrow><mn>91.10</mn> <mo>±</mo> <mn>2.27</mn> <mo>%</mo></mrow> </math> for pedicle screw loosening detection.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1001-1011"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669765","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":"Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.","authors":"Junjie Li, Xuanle Li, Yingge Chen, Yunling Wang, Binjie Wang, Xuefeng Zhang, Na Zhang","doi":"10.1007/s11517-024-03253-2","DOIUrl":"10.1007/s11517-024-03253-2","url":null,"abstract":"<p><p>Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed. The system extracts radiomic and pathomic features from CT images and WSI, respectively, and sends them into stochastic configuration networks for the final prediction. Compared to traditional radiomics or pathomics, this system has the capability to capture more comprehensive image features, providing a multidimensional insight into tissue characteristics. The experiments and analyses demonstrate the accuracy and effectiveness of the system, with an area under the curve of 81.03%, an accuracy of 73.67%, a sensitivity of 71.54%, a precision of 76.78%, and a F1-score of 72.61%, surpassing both single-modality and dual-modality models. RPMSNet highlights its potential for early diagnosis and personalized treatment in precision medicine.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1117-1129"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787489","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 fully automated measurement of migration percentage on ultrasound images in children with cerebral palsy.","authors":"Reza Yousefvand, Thanh-Tu Pham, Lawrence H Le, John Andersen, Edmond Lou","doi":"10.1007/s11517-024-03259-w","DOIUrl":"10.1007/s11517-024-03259-w","url":null,"abstract":"<p><p>Migration percentage (MP) is the gold standard to assess the severity of hip displacement in children with cerebral palsy, which is measured on anteroposterior hip radiographs. Recently, the ultrasound (US) method has been developed as a safe alternative imaging modality to image and monitor children's hips. However, measuring MP on US images (MP_US) is time-consuming, challenging, and user-dependent. This study aimed to develop machine learning algorithms to automatically measure MP_US and validate the algorithms with MP_Xray. A combination of signal filtering, convolution neural networks (CNNs), and UNets was applied to segment the regions of interest (ROI), detect edges or feature points, and select the desired US frames. A total of 62 hips including both coronal and transverse scans per hip were acquired, out of which 36 with applying augmentation method were utilized for training, 8 for validation, and 18 for testing. The intraclass correlation coefficient (ICC_2,1) and the mean absolute difference (MAD) between the automated MP_US versus manual MP_Xray were 0.86 and 6.5% ± 5.5%, respectively. To report the MP_US, it took an average of 104 s/hip. This preliminary result demonstrated that MP_US was able to extract automatically within 2 min with a clinical acceptance accuracy (10%).</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1177-1188"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830612","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}