Computer methods and programs in biomedicine update最新文献

{"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 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":"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}

{"title":"Picture: A web application for decision support in glioma surgery","authors":"Maisa N.G. van Genderen ,&nbsp;Raymond M. Martens ,&nbsp;Frederik Barkhof ,&nbsp;Philip C. de Witt Hamer ,&nbsp;Roelant S. Eijgelaar","doi":"10.1016/j.cmpbup.2025.100199","DOIUrl":"10.1016/j.cmpbup.2025.100199","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Patients with glioma, the most common primary malignant brain tumor, often undergo surgery, aiming to remove as much tumor as possible while maintaining functional integrity. However, there is large variation in surgical decisions. This study aims to provide a data-driven approach to surgery planning and evaluation, estimating personalized potential extent of resection, based on a large multicenter MRI database.</div></div><div><h3>Methods</h3><div>We developed an interactive web-application (PICTURE tool), that uses segmented MRI scans from prior surgeries to create resection probability maps. The maps depict the chance of tumor tissue resection based on decisions in prior surgeries.</div></div><div><h3>Results</h3><div>The PICTURE tool enables uploading scans of a new patient and comparing these with the resection probability map of previous patients. This map can then be filtered for clinical characteristics to compare with similar patients and can be interactively explored to determine which parts of the tumor are more or less likely to be resected in a particular patient. Additionally, tumor characteristics and expected extent of resection are reported.</div></div><div><h3>Conclusions</h3><div>The PICTURE tool can enable data-driven glioma surgery planning through interactive generation of resection probability maps.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579831","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":"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":"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":"SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder","authors":"Andrea Pollastro,&nbsp;Francesco Isgrò,&nbsp;Roberto Prevete","doi":"10.1016/j.cmpbup.2025.100213","DOIUrl":"10.1016/j.cmpbup.2025.100213","url":null,"abstract":"<div><div>Over the past few decades, electroencephalography monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately 1<!--> <!-->% of the population. These patients face significant risks, underscoring the need for reliable, continuous seizure monitoring in daily life. Most of the techniques discussed in the literature rely on supervised machine learning methods. However, the challenge of accurately labeling variations in epileptic electroencephalography waveforms complicates the use of these approaches. Additionally, the rarity of ictal events introduces a high imbalance within the data, which could lead to poor prediction performance in supervised learning approaches. Instead, a semi-supervised approach allows training the model only on data that does not contain seizures, thus avoiding the issues related to the data imbalance. This work introduces a semi-supervised approach for detecting epileptic seizures from electroencephalography data based on a novel deep learning-based method called SincVAE. This method integrates SincNet, designed to learn an ad-hoc array of bandpass filters, as the first layer of a variational autoencoder, potentially eliminating the preprocessing stage where informative frequency bands are identified and isolated. Experimental evaluations on the Bonn and CHB-MIT datasets indicate that SincVAE improves seizure detection in electroencephalography data, with the capability to identify early seizures during the preictal stage and monitor patients throughout the postictal stage.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828828","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":"Dynamics and optimal control of fractional-order monkeypox epidemic model with social distancing habits and public awareness","authors":"Raqqasyi Rahmatullah Musafir,&nbsp;Agus Suryanto,&nbsp;Isnani Darti,&nbsp;Trisilowati","doi":"10.1016/j.cmpbup.2025.100187","DOIUrl":"10.1016/j.cmpbup.2025.100187","url":null,"abstract":"<div><div>In this article, we propose a fractional-order monkeypox epidemic model incorporating social distancing habits and public awareness. The model includes the addition of a protected compartment and a saturated transmission rate. We implement a power rescaling for the parameters of the proposed model to ensure dimensional consistency. We have investigated the existence, uniqueness, nonnegativity, and boundedness of the solution. The model features monkeypox-free, human-endemic, and endemic equilibrium points, which depend on the order of derivative. The existence and stability of each equilibrium point have been analyzed locally and globally, depending on the basic reproduction number. Moreover, the basic reproduction number of the model also depends on the order of derivative. We carried out a case study using real data showing that the fractional-order model performs better than the first-order model in calibration and forecasting. Numerical simulations confirm the stability properties of each equilibrium point with respect to the specified parameter values. Numerical simulations also demonstrate that the social distancing habits can reduce monkeypox cases in the early stages, but do not significantly alter the basic reproduction number. Meanwhile, public awareness can substantially modify the basic reproduction number, shifting the endemic condition towards a disease-free state, although its impact on case reduction in the early period is not significant. We also implemented optimal control strategies for vector culling and vaccination in the proposed model. We have solved the optimal control problem, and the simulation results show that the combination of both controls yields the minimum cost with better effectiveness compared to the controls implemented separately.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609293","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":"MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects","authors":"Domenico Lofù ,&nbsp;Paolo Sorino ,&nbsp;Tommaso Colafiglio ,&nbsp;Caterina Bonfiglio ,&nbsp;Rossella Donghia ,&nbsp;Gianluigi Giannelli ,&nbsp;Angela Lombardi ,&nbsp;Tommaso Di Noia ,&nbsp;Eugenio Di Sciascio ,&nbsp;Fedelucio Narducci","doi":"10.1016/j.cmpbup.2024.100176","DOIUrl":"10.1016/j.cmpbup.2024.100176","url":null,"abstract":"<div><div>Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we propose MORIX, an Artificial Intelligence-based framework capable of predicting fatal mortality outcomes in subjects with MAFLD. MORIX utilizes data from epidemiological datasets containing carefully selected anthropometric and biochemical information. This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. Experimental results identified the RF as the best model, achieving a high accuracy for both mortality risks predicted. Additionally, an eXplainable Artificial Intelligence (XAI) analysis was conducted to clarify the diagnostic logic of the RF model and to assess the impact of each feature to the prediction. Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. In conclusion, the MORIX framework is easy to apply, and the required parameters are readily available in healthcare datasets, making it a practical tool for medical professionals.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180357","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":"A morphological approach for efficient macular center detection to support pre-diagnosis of diabetic retinopathy","authors":"E. de-la-Cruz-Espinosa ,&nbsp;Rita Q. Fuentes-Aguilar ,&nbsp;E. Morales-Vargas","doi":"10.1016/j.cmpbup.2025.100212","DOIUrl":"10.1016/j.cmpbup.2025.100212","url":null,"abstract":"<div><div>Diabetes is a disease with a worldwide presence and a high mortality rate, causing a significant social and economic impact. One of the more adverse effects of diabetes is visual loss due to diabetic retinopathy. Current methods to identify patients who need to be seen by a specialist to prevent vision impairment include screening and optical coherence tomography examinations; however, the number of devices and ophthalmologists is insufficient to cover the diabetic population. To address this, computational methods have been developed for rapid early-damage detection. This work presents an algorithm for ocular macula identification using simple image processing techniques for a low computational cost. The proposed algorithm achieved an Euclidean distance of 8.162 <span><math><mo>±</mo></math></span> 6.774 px (1.496 <span><math><mo>±</mo></math></span> 1.190% Relative error) in a processing time of 0.458 <span><math><mo>±</mo></math></span> 0.874 s across four databases, demonstrating competitive accuracy (100%) and speed on low-resource hardware.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100212"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851776","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}