Computer methods and programs in biomedicine update最新文献

{"title":"R package to estimate intracluster correlation coefficient for nominal and ordinal data","authors":"Hrishikesh Chakraborty ,&nbsp;Nicole Solomon","doi":"10.1016/j.cmpbup.2025.100200","DOIUrl":"10.1016/j.cmpbup.2025.100200","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: The intracluster correlation coefficient (ICC) is a critical parameter to assess the degree of similarity or correlation between observations within the same cluster or group. It is commonly applied in cluster-randomized trials to estimate average within-cluster correlation. Although methods to estimate ICC exist for binary, continuous, and survival data, a new resampling-based approach has been developed for nominal or ordinal responses with more than two categories. The objective of this paper is to present both the resampling methods estimator and method of moments (MoM) based estimator for categorical ICC estimation. To facilitate the adoption and use of these estimators we developed an R package, <span>iccmult</span>, which calculates the ICC point estimate and confidence interval (CI) for categorical response data under each of these two methods.</div></div><div><h3>Methods</h3><div>: In this paper we incorporated the resampling based estimation method and MoM originally developed to characterize population genetic structure. A simulation study was conducted to compare estimates from MoM to the resampling method under different event rates, varying numbers of clusters, and various cluster sizes. The <span>iccmult</span> package provides two estimates of ICC and its CI, computed using these two methods. Additionally, the package also generates clustered categorical response data.</div></div><div><h3>Results</h3><div>: The <span>iccmult</span> package provides two functions for users. The function <span>rccat()</span> generates clustered categorical data, while the function <span>iccmulti()</span> estimates ICC and its CI. The simulation study revealed that the resampling and MoM methods perform nearly identically in estimating population ICC. However, the MoM method demonstrated greater precision in scenarios with fewer clusters and smaller cluster sizes.</div></div><div><h3>Conclusions</h3><div>: The <span>R</span> package <span>iccmult</span> offers easy-to-use ways to generate clustered categorical data and estimate ICC and its CI for a nominal or ordinal response using different methods. The package is freely available for use with <span>R</span> from the CRAN repository (<span><span>https://cran.r-project.org/package=iccmult</span><svg><path></path></svg></span>). We believe that this package can be a very useful tool for researchers designing cluster randomized trials with a categorical outcome.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development","authors":"Alberto Martín-Pérez ,&nbsp;Beatriz Martinez-Vega ,&nbsp;Manuel Villa ,&nbsp;Raquel Leon ,&nbsp;Alejandro Martinez de Ternero ,&nbsp;Himar Fabelo ,&nbsp;Samuel Ortega ,&nbsp;Eduardo Quevedo ,&nbsp;Gustavo M. Callico ,&nbsp;Eduardo Juarez ,&nbsp;César Sanz","doi":"10.1016/j.cmpbup.2025.100183","DOIUrl":"10.1016/j.cmpbup.2025.100183","url":null,"abstract":"<div><h3>Background and objective</h3><div>Cancer is one of the leading causes of death worldwide, and early and accurate detection is crucial to improve patient outcomes. Differentiating between healthy and diseased brain tissue during surgery is particularly challenging. Hyperspectral imaging, combined with machine and deep learning algorithms, has shown promise for detecting brain cancer <em>in vivo</em>. The present study is distinguished by an analysis and comparison of the performance of various algorithms, with the objective of evaluating their efficacy in unifying hyperspectral databases obtained from different cameras. These databases include data collected from various hospitals using different hyperspectral instruments, which vary in spectral ranges, spatial and spectral resolution, as well as illumination conditions. The primary aim is to assess the performance of models that respond to the limited availability of <em>in vivo</em> human brain hyperspectral data. The classification of healthy tissue, tumors and blood vessels is achieved through the utilisation of different algorithms in two databases: <em>HELICoiD</em> and <em>SLIMBRAIN</em>.</div></div><div><h3>Methods</h3><div>This study evaluated conventional and deep learning methods (<em>KNN, RF, SVM, 1D-DNN, 2D-CNN, Fast 3D-CNN,</em> and a <em>DRNN</em>), and advanced classification frameworks (<em>LIBRA</em> and <em>HELICoiD</em>) using cross-validation on 16 and 26 patients from each database, respectively.</div></div><div><h3>Results</h3><div>For individual datasets,<em>LIBRA</em> achieved the highest sensitivity for tumor classification, with values of 38 %, 72 %, and 80 % on the <em>SLIMBRAIN, HELICoiD</em> (20 bands), and <em>HELICoiD</em> (128 bands) datasets, respectively. The <em>HELICoiD</em> framework yielded the best <em>F1 Scores</em> for tumor tissue, with values of 11 %, 45 %, and 53 % for the same datasets. For the <em>Unified dataset, LIBRA</em> obtained the best results identifying the tumor, with a 40 % of sensitivity and a 30 % of <em>F1 Score</em>.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature selection based on Mahalanobis distance for early Parkinson disease classification","authors":"Mustafa Noaman Kadhim ,&nbsp;Dhiah Al-Shammary ,&nbsp;Ahmed M. Mahdi ,&nbsp;Ayman Ibaida","doi":"10.1016/j.cmpbup.2025.100177","DOIUrl":"10.1016/j.cmpbup.2025.100177","url":null,"abstract":"<div><div>Standard classifiers struggle with high-dimensional datasets due to increased computational complexity, difficulty in visualization and interpretation, and challenges in handling redundant or irrelevant features. This paper proposes a novel feature selection method based on the Mahalanobis distance for Parkinson's disease (PD) classification. The proposed feature selection identifies relevant features by measuring their distance from the dataset's mean vector, considering the covariance structure. Features with larger Mahalanobis distances are deemed more relevant as they exhibit greater discriminative power relative to the dataset's distribution, aiding in effective feature subset selection. Significant improvements in classification performance were observed across all models. On the \"Parkinson Disease Classification Dataset\", the feature set was reduced from 22 to 11 features, resulting in accuracy improvements ranging from 10.17 % to 20.34 %, with the K-Nearest Neighbors (KNN) classifier achieving the highest accuracy of 98.31 %. Similarly, on the \"Parkinson Dataset with Replicated Acoustic Features\", the feature set was reduced from 45 to 18 features, achieving accuracy improvements ranging from 1.38 % to 13.88 %, with the Random Forest (RF) classifier achieving the best accuracy of 95.83 %. By identifying convergence features and eliminating divergence features, the proposed method effectively reduces dimensionality while maintaining or improving classifier performance. Additionally, the proposed feature selection method significantly reduces execution time, making it highly suitable for real-time applications in medical diagnostics, where timely and accurate disease identification is critical for improving patient outcomes.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing laboratory data for poliovirus eradication: contributions of the Africa regional polio laboratory data management team, 2022 – 2024","authors":"Brook Tesfaye,&nbsp;Reggis Katsande,&nbsp;Doungmo Wakem Yannick Arthur,&nbsp;Julius E Chia,&nbsp;Chefor Ymele Demeveng Derrick,&nbsp;Ikeonu Obianuju Caroline,&nbsp;Kabore Sakma,&nbsp;Mahmud Zubairu,&nbsp;Busisiwe Ngobe,&nbsp;Abdulahi Walla Hamisu,&nbsp;Ticha Johnson Muluh,&nbsp;Kebba Touray,&nbsp;Modjirom Ndoutabe,&nbsp;Jamal A Ahmed,&nbsp;Anfumbom Kfutwah","doi":"10.1016/j.cmpbup.2025.100214","DOIUrl":"10.1016/j.cmpbup.2025.100214","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Polio laboratory data is crucial in providing timely and accurate information on poliovirus outbreaks and therefore an important component of the overall poliovirus eradication strategies. This paper discusses the contributions of the Africa Regional Polio Laboratory Data Management Team (RPLDMT) in optimizing data-driven polio eradication efforts in the African region from 2022 to 2024.</div></div><div><h3>Methods</h3><div>We explored key data management activities performed by the RPLDMT from 2022 to 2024 and assessed their contribution on enhancing polio eradication efforts in the African region.</div></div><div><h3>Results</h3><div>The RPLDMT has significantly advanced polio eradication efforts in Africa through multiple initiatives. Notably, the team has supported the Africa Regional Emergency Operations Center (EOC) by providing 218 daily line lists of polioviruses identified, improving real-time case tracking and decision-making. The integration of Open Data Kit (ODK), an open-source electronic data collection tool, has enhanced poliovirus environmental surveillance, benefitting 23 countries in 2022, 13 in 2023, and 14 as of August 2024. The development of a sophisticated automated data quality assurance script has improved data accuracy and reliability, with 65 weekly line lists of errors provided for data correction. Additionally, the introduction of the biweekly Africa Regional Polio Laboratory Network (ARPLN) bulletin and real-time dashboards has optimized data use, aiding in actionable insights and decision-making. Efforts to transition to the Web-based Information for Action (WebIFA) system and capacity building through training workshops have further strengthened data management and surveillance capabilities across the region.</div></div><div><h3>Conclusion</h3><div>The contributions provided by the RPLDMT has played a key role in boosting the polio eradication efforts with a focus on enhancing human resource skills embracing new technologies and implementing real-time performance monitoring tools to improve data quality and strengthen data-driven decision-making processes essential for speeding up the progress towards eradicating polio in the region.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical analysis of HPV transmission dynamics and cervical cancer progression: The role of screening, prophylactic and therapeutic vaccination strategies","authors":"L.J. Mbigili ,&nbsp;N. Nyerere ,&nbsp;A. Iddi ,&nbsp;S. Mpeshe","doi":"10.1016/j.cmpbup.2025.100219","DOIUrl":"10.1016/j.cmpbup.2025.100219","url":null,"abstract":"<div><div>Cervical cancer remains a significant global health threat in the 21st century, posing serious societal, public health, and economic challenges. Despite being largely preventable, it is the most common cancer among women worldwide, responsible for over 250,000 deaths annually. This study develops and analyzes a mathematical model that captures the transmission dynamics of Human Papillomavirus (HPV) infection and its progression to cervical cancer. The model incorporates key intervention strategies, including prophylactic vaccination, regular screening and treatment, as well as therapeutic vaccination. Mathematical analysis confirms that the model is both epidemiologically and mathematically well-posed. Using a Lyapunov function in conjunction with LaSalle’s Invariance Principle, we establish the global asymptotic stability of the disease-free equilibrium (DFE) when the effective reproduction number <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>&lt;</mo><mn>1</mn></mrow></math></span>, and the global stability of the endemic equilibrium when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>&gt;</mo><mn>1</mn></mrow></math></span>. Bifurcation analysis reveals that the model exhibits a forward (degenerate) transcritical bifurcation at <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>=</mo><mn>1</mn></mrow></math></span>, indicating that HPV infection becomes endemic and persists when <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span> exceeds unity. Conversely, when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>≤</mo><mn>1</mn></mrow></math></span>, the force of infection diminishes, rendering the DFE globally stable. A sensitivity analysis was conducted to identify the most influential parameters governing HPV transmission and the progression to cervical cancer. Local sensitivity was assessed using the normalized forward finite difference method, while global sensitivity was evaluated using the Partial Rank Correlation Coefficient (PRCC) technique. Numerical simulations indicate that prophylactic HPV vaccination is the most impactful standalone intervention. However, a synergistic approach combining vaccination with regular screening, therapeutic vaccination, and treatment strategies such as immunotherapy integrated with induced pluripotent stem cells (iPSCs) and conventional chemotherapy offers a more rapid and substantial reduction in HPV infections. Such a multifaceted strategy is likely to accelerate the eradication of cervical cancer and significantly reduce the disease burden in the population.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sustainable neuromorphic framework for disease diagnosis using digital medical imaging","authors":"Rutwik Gulakala,&nbsp;Marcus Stoffel","doi":"10.1016/j.cmpbup.2024.100171","DOIUrl":"10.1016/j.cmpbup.2024.100171","url":null,"abstract":"<div><h3>Background and objective:</h3><div>In the diagnosis of medical images, neural network classifications can support rapid diagnosis together with existing imaging methods. Although current state-of-the-art deep learning methods can contribute to this image recognition, the aim of the present study is to develop a general classification framework with brain-inspired neural networks. Following this intention, spiking neural network models, also known as third-generation models, are included here to capitalize on their sparse characteristics and capacity to significantly decrease energy consumption. Inspired by the recent development of neuromorphic hardware, a sustainable neural network framework is proposed, leading to an energy reduction down to a thousandth compared to the current state-of-the-art second-generation counterpart of artificial neural networks. Making use of sparse signal transmissions as in the human brain, a neuromorphic algorithm for imaging diagnostics is introduced.</div></div><div><h3>Methods:</h3><div>A novel, sustainable, brain-inspired spiking neural network is proposed to perform the multi-class classification of digital medical images. The framework comprises branched and densely connected layers described by a Leaky-Integrate and Fire (LIF) neuron model. Backpropagation of discontinuous spiking activations in the forward pass is achieved by surrogate gradients, in this case, fast sigmoid. The data for the spiking neural network is encoded into binary spikes with a latency encoding strategy. The proposed model is evaluated on a publicly available dataset of digital X-rays of chest and compared with an equivalent classical neural network. The models are trained using enhanced and pre-processed X-ray images and are evaluated based on classification metrics.</div></div><div><h3>Results:</h3><div>The proposed neuromorphic framework had an extremely high classification accuracy of 99.22<span><math><mtext>%</mtext></math></span> on an unseen test set, together with high precision and recall figures. The framework achieves this accuracy, all the while consuming 1000 times less electrical power than classical neural network architectures.</div></div><div><h3>Conclusion:</h3><div>Though there is a loss of information due to encoding, the proposed neuromorphic framework has achieved accuracy close to its second-generation counterpart. Therefore, the benefit of the proposed framework is the high accuracy of classification while consuming a thousandth of the power, enabling a sustainable and accessible add-on for the available diagnostic tools, such as medical imaging equipment, to achieve rapid diagnosis.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modeling of the impact of HPV vaccine uptake in reducing cervical cancer using a graph-theoretic approach via Caputo fractional-order derivatives","authors":"Sylas Oswald ,&nbsp;Eunice Mureithi ,&nbsp;Berge Tsanou ,&nbsp;Michael Chapwanya ,&nbsp;Crispin Kahesa ,&nbsp;Kijakazi Mashoto","doi":"10.1016/j.cmpbup.2025.100216","DOIUrl":"10.1016/j.cmpbup.2025.100216","url":null,"abstract":"<div><div>Human papillomavirus (HPV) is a highly prevalent sexually transmitted infection and the primary cause of cervical cancer, which remains a leading cause of cancer-related mortality among women globally. Despite ongoing vaccination efforts, challenges such as latency, persistent infections, and imperfect vaccine coverage complicate disease control. In this study, we develop a novel fractional-order compartmental model using Caputo derivatives to capture the memory and non-local transmission effects inherent in HPV dynamics. We analyze the model’s epidemiological properties by proving positivity, boundedness, and deriving the effective reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>) via a Graph Theoretic approach. Stability of disease-free and endemic equilibria is established through Lyapunov theory, complemented by Hyers–Ulam stability to ensure robustness. Parameter estimation is performed using Markov Chain Monte Carlo (MCMC), and sensitivity analysis utilizes Partial Rank Correlation Coefficients (PRCC) to identify key drivers of transmission. Our results indicate that achieving 56% vaccination coverage with 45.5% efficacy can reduce <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span> below one, supporting herd immunity. Numerical simulations demonstrate that vaccination coverage, timely treatment, and vaccine efficacy critically reduce infection prevalence and disease burden. Furthermore, higher fractional orders accelerate convergence to equilibrium without changing equilibrium values. This work lies in integrating fractional calculus with time-dependent vaccination and treatment controls to realistically model HPV progression and intervention impact. This approach provides a more accurate representation of HPV transmission dynamics, especially the long-term memory effects, thereby offering valuable insights for optimizing public health strategies.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computer-based method for the automatic identification of the dimensional features of human cervical vertebrae","authors":"Nicola Cappetti ,&nbsp;Luca Di Angelo ,&nbsp;Carlotta Fontana ,&nbsp;Antonio Marzola","doi":"10.1016/j.cmpbup.2024.100175","DOIUrl":"10.1016/j.cmpbup.2024.100175","url":null,"abstract":"<div><h3>Background and objective</h3><div>Accurately measuring cervical vertebrae dimensions is crucial for diagnosing conditions, planning surgeries, and studying morphological variations related to gender, age, and ethnicity. However, traditional manual measurement methods, due to their labour-intensive nature, time-consuming process, and susceptibility to operator variability, often fall short in providing the objectivity required for reliable measurements. This study addresses these limitations by introducing a novel computer-based method for automatically identifying the dimensional features of human cervical vertebrae, leveraging 3D geometric models obtained from CT or 3D scanning.</div></div><div><h3>Methods</h3><div>The proposed approach involves defining a local coordinate system and establishing a set of rules and parameters to evaluate the typical dimensional features of the vertebral body, foramen, and spinous process in the sagittal and coronal planes of the high-density point cloud of the cervical vertebra model. This system provides a consistent measurement reference frame, improving the method's reliability and objectivity. Based on this reference system, the method automates the traditional standard protocol, typically performed manually by radiologists, through an algorithmic approach.</div></div><div><h3>Results</h3><div>The performance of the computer-based method was compared with the traditional manual approach using a dataset of nine complete cervical tracts. Manual measurements were conducted following a defined protocol. The manual method demonstrated poor repeatability and reproducibility, with substantial differences between the minimum and maximum values for the measured features in intra- and inter-operator evaluations. In contrast, the measurements obtained with the proposed computer-based method were consistent and repeatable.</div></div><div><h3>Conclusions</h3><div>The proposed computer-based method provides a more reliable and objective approach for measuring the dimensional features of cervical vertebrae. It establishes a procedural standard for deducing the morphological characteristics of cervical vertebrae, with significant implications for clinical applications, such as surgical planning and diagnosis, as well as for forensic anthropology and spinal anatomy research. Further refinement and validation of the algorithmic rules and investigations into the influence of morphological abnormalities are necessary to improve the method's accuracy.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving brain tumor detection using FPGA-accelerated deep learning on Kria KV260 for smart healthcare","authors":"Kusum Lata ,&nbsp;Prashant Singh ,&nbsp;Sandeep Saini ,&nbsp;Linga Reddy Cenkeramaddi","doi":"10.1016/j.cmpbup.2025.100205","DOIUrl":"10.1016/j.cmpbup.2025.100205","url":null,"abstract":"<div><div>Technological advancements in high-performance electronics have fueled the development of cutting-edge medical applications, leading to exponential growth in effective treatment and diagnostic solutions for various medical problems. Incorporating deep learning-based systems with medical imaging technologies has revolutionized the field of disease detection. Ensuring the security and privacy of patient’s health records is crucial to developing sophisticated medical imaging diagnostic applications. This paper presents a privacy-focused, vision-based approach for effective brain tumor detection using deep learning algorithms such as ResNet-18, ResNet-50, and InceptionV3, deployed on the KV260 board, which is based on Xilinx® Kria™ K26 System on Module (SOM) platform, a Zynq® UltraScale+ MPSoC. We have integrated the AES-128 cryptographic algorithm with the Password-Based Key Derivation Function 2 (PBKDF2) hashing algorithm to maintain patients' privacy in MRI scans. This ensures the protection of patient data on the server and data movement to and from external servers. The designed system is evaluated for performance by examining its technical metric parameters- accuracy, precision, F1 score, and Recall. Security parameters such as entropy, energy, contrast, and correlation are used to evaluate the security strength of the proposed system. Microsoft operating systems compatible web application is also developed while integrating the above-proposed system on the KV 260 FPGA board. This application can be used remotely to upload the MRI scans and get the prediction results quickly and accurately. Performance assessment shows that ResNet18 outperforms testing-related metric parameters and execution time on the KV260 FPGA board while keeping patient data confidential, making it an ideal edge-device implementation for real-time clinical use.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity of patient-specific physiological and pathological aortic hemodynamics to the choice of outlet boundary condition in numerical models","authors":"Tianai Wang ,&nbsp;Christine Quast ,&nbsp;Florian Bönner ,&nbsp;Tobias Zeus ,&nbsp;Malte Kelm ,&nbsp;Teresa Lemainque ,&nbsp;Ulrich Steinseifer ,&nbsp;Michael Neidlin","doi":"10.1016/j.cmpbup.2025.100194","DOIUrl":"10.1016/j.cmpbup.2025.100194","url":null,"abstract":"<div><h3>Purpose</h3><div>Outlet boundary conditions (OBC) play a pivotal role in all simulations of vascular flow. However, previous investigations of OBC impact on numerical aortic flow simulations were not yet comprehensive for the entirety of hemodynamic characteristics. They mainly investigated near-wall properties and velocity in physiological flow. Therefore, the aim of this work was to expand the sensitivity assessment to hemodynamic markers in the bulk flow to the choice of OBC for a physiological and pathological aortic flow field.</div></div><div><h3>Material and methods</h3><div>Image-based computational models of subject-specific aortic geometries were created. Temporally and spatially resolved inlet velocity profiles derived from 4D Flow MRI were implemented. Three types of OBCs were compared: zero pressure, loss coefficients and three-element Windkessel. Their influence on velocity, near-wall properties and bulk flow quantities were analyzed.</div></div><div><h3>Results</h3><div>Velocity and near-wall parameters in the ascending aorta are largely insensitive to the OBC choice. However, bulk flow parameters, in particular the helicity field, are highly sensitive throughout the entire aortic domain with differences of up to 600 % between models. The relative sensitivity to OBC drops for pathological flows, as the influence of more complex inlet profiles increases.</div></div><div><h3>Conclusion</h3><div>While the sensitivity of velocity and near-wall parameters to OBC choice is insignificant when only the ascending aorta is assessed, our study proposes a more thorough discernment once bulk flow parameters are of interest. Different degrees of boundary condition complexity are required to determine the hemodynamic properties of interest accurately. A support tool is presented to determine the case-dependent minimum requirement for inlet and outlet boundary conditions.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}