Mathematical Biosciences and Engineering最新文献_第8页

MAET-SAM: Magneto-Acousto-Electrical Tomography segmentation network based on the segment anything model. MAET-SAM：基于分段任意模型的磁声电层析成像分割网络。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-25 DOI: 10.3934/mbe.2025022

Shuaiyu Bu, Yuanyuan Li, Guoqiang Liu, Yifan Li

{"title":"MAET-SAM: Magneto-Acousto-Electrical Tomography segmentation network based on the segment anything model.","authors":"Shuaiyu Bu, Yuanyuan Li, Guoqiang Liu, Yifan Li","doi":"10.3934/mbe.2025022","DOIUrl":"https://doi.org/10.3934/mbe.2025022","url":null,"abstract":"Magneto-Acousto-Electrical Tomography (MAET) is a hybrid imaging method that combines advantages of ultrasound imaging and electrical impedance tomography to image the electrical conductivity of biological tissues. In practical applications, different tissue or disease organization display various conductivity traits. However, the conductivity map consists of overlapping signals measured at multiple locations, the reconstruction results are affected by noise, which results in blurred reconstruction boundaries, low contrast, and irregular artifact distributions. To improve the image resolution and reduce noise of MAET, a dataset of conductivity maps reconstructed from MAET was established, dubbed MAET-IMAGE. Based on this dataset, we proposed a MAET tomography segmentation network based on the Segment Anything Model (SAM), termed as MAET-SAM. Specifically, we froze the encoder weights of SAM to extract rich feature information of image and design, an adaptive decoder with no prompts. In the end, an end-to-end segmentation model for specific MAET images with MAET-IMAGE was proposed. Qualitative and quantitative experiments demonstrated that MAET-SAM outperformed traditional segmentation methods and segmentation models with initial weights in terms of MAET image segmentation performance, bringing new breakthroughs and advancements to the field of medical imaging analysis and clinical diagnosis.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"585-603"},"PeriodicalIF":2.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data. 针对新发呼吸道疾病的人工智能驱动健康分析：基于COVID-19数据的也门患者案例研究

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-24 DOI: 10.3934/mbe.2025021

Saleh I Alzahrani, Wael M S Yafooz, Ibrahim A Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh

{"title":"AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.","authors":"Saleh I Alzahrani, Wael M S Yafooz, Ibrahim A Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh","doi":"10.3934/mbe.2025021","DOIUrl":"10.3934/mbe.2025021","url":null,"abstract":"In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation between COVID-19 and other infectious diseases. We explored the use of AI-powered predictive models and classifiers to enhance healthcare preparedness by forecasting respiratory disease trends using COVID-19 data. We developed mathematical models based on autoregressive (AR), moving average (MA), ARMA, and machine and deep learning algorithms to predict daily confirmed deaths. Statistical models were trained on 80% of the data and tested on the remaining 20%, with predicted results compared to actual values. The ARMA model demonstrated promising performance. Additionally, eight machine learning (ML) classifiers and deep learning (DL) models were utilized to identify COVID-19 severity indicators. Among the ML classifiers, the Decision Tree (DT) achieved the highest accuracy at 74.70%, followed closely by Random Forest (RF) at 74.66%. DL models showed comparable accuracy scores, around 70%. In terms of AUC-ROC, the kernel Support Vector Machine (SVM) outperformed others, achieving 71% accuracy, with precision, recall, F-measure, and area under the curve values of 0.7, 0.75, 0.59, and 0.72, respectively. These findings underscore the potential of AI-driven health analysis to optimize resource allocation and enhance forecasting for respiratory diseases.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"554-584"},"PeriodicalIF":2.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty CNNs: A path to enhanced medical image classification performance. 不确定性cnn：一种增强医学图像分类性能的方法。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-20 DOI: 10.3934/mbe.2025020

Vasileios E Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

{"title":"Uncertainty CNNs: A path to enhanced medical image classification performance.","authors":"Vasileios E Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras","doi":"10.3934/mbe.2025020","DOIUrl":"10.3934/mbe.2025020","url":null,"abstract":"The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient modeling techniques and enhanced data storage capabilities. However, methodologies that account for the uncertainty of predictions remain relatively uncommon in medical imaging. Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. In this paper, we introduce a low-complexity uncertainty-based CNN architecture for medical image classification, particularly focused on tumor and heart failure (HF) detection. The model's predictive (aleatoric) uncertainty is quantified through a test-set augmentation technique, which generates multiple surrogates of each test image. This process enables the construction of empirical distributions for each image, which allows for the calculation of mean estimates and credible intervals. Importantly, this methodology not only provides UQ, but also significantly improves the model's classification performance. This paper represents the first effort to demonstrate that test-set augmentation can significantly improve the classification performance of medical images. The proposed DL model was evaluated using three datasets: (a) brain magnetic resonance imaging (MRI), (b) lung computed tomography (CT) scans, and (c) cardiac MRI. The low-complexity design of the model enhances its robustness against overfitting, while it is also easily re-trainable in case out-of-distribution data is encountered, due to the reduced computational resources required by the introduced architecture.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"528-553"},"PeriodicalIF":2.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A within-host model on the interaction dynamics between innate immune cells and Mycobacterium tuberculosis. 固有免疫细胞与结核分枝杆菌相互作用动力学的宿主内模型。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-17 DOI: 10.3934/mbe.2025019

Eduardo Ibargüen-Mondragón, M Victoria Otero-Espinar, Miller Cerón Gómez

引用次数: 0

Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon. 喀麦隆某卫生区伤寒动态的数学模型和两种治疗模式。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-14 DOI: 10.3934/mbe.2025018

Thierry Jimy Tsafack, Cletus Kwa Kum, Arsène Jaurès Ouemba Tassé, Berge Tsanou

{"title":"Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon.","authors":"Thierry Jimy Tsafack, Cletus Kwa Kum, Arsène Jaurès Ouemba Tassé, Berge Tsanou","doi":"10.3934/mbe.2025018","DOIUrl":"10.3934/mbe.2025018","url":null,"abstract":"In this paper, we propose a novel mathematical model for indirectly transmitted typhoid fever disease that incorporates the use of modern and traditional medicines as modes of treatment. Theoretically, we provide two Lyapunov functions to prove the global asymptotic stability of the disease-free equilibrium (DFE) and the endemic equilibrium (EE) when the basic reproduction number $ (mathcal{R}_0) $ is less than one and greater than one, respectively. The model is calibrated using the number of cumulative cases reported in the Penka-Michel health district in Cameroon. The parameter estimates thus obtained give a value of $ mathcal{R}_0 $ = 1.2058 > 1, which indicates that the disease is endemic in the region. The forecast of the outbreak up to November 2026 suggests that the number of cases will be 21,270, which calls for urgent attention on this endemic disease. A sensitivity analysis with respect to the basic reproduction number is conducted, and the main parameters that impact the widespread of the disease are determined. The analysis highlights that the environmental transmission rate $ beta $ and the decay rate $ mu_b $ of the bacteria in the environment are the most influential parameters for $ mathcal{R}_0 $. This underscores the urgent need for potable water and adequate sanitation within this area to reduce the spread of the disease. Numerically, we illustrate the usefulness of recourse to any mode of treatment to lessen the number of infected cases and the necessity of switching from modern treatment to the traditional treatment, a useful adjuvant therapy. Conversely, we show that the relapse phenomenon increases the burden of the disease. Hence adopting a synergistic therapy approach will significantly mitigate typhoid disease cases and overcome the cycle of poverty within the afflicted communities.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 2","pages":"477-510"},"PeriodicalIF":2.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational modeling of peritoneal dialysis: An overview. 腹膜透析的计算模型：综述。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-13 DOI: 10.3934/mbe.2025017

Sangita Swapnasrita, Joost C de Vries, Carl M Öberg, Aurélie Mf Carlier, Karin Gf Gerritsen

引用次数: 0

On the impossibility of increasing the MSY in a multisite Schaefer fishing model. 关于多点Schaefer捕鱼模式中MSY不可能增加的问题。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-11 DOI: 10.3934/mbe.2025016

Pierre Auger, Tri Nguyen-Huu, Doanh Nguyen-Ngoc

引用次数: 0

Deterministic, stochastic and fractional mathematical approaches applied to AMR. 确定性、随机和分数数学方法在AMR中的应用。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-10 DOI: 10.3934/mbe.2025015

Sebastian Builes, Jhoana P Romero-Leiton, Leon A Valencia

引用次数: 0

Stochastic games of parental vaccination decision making and bounded rationality. 父母接种疫苗决策的随机博弈与有限理性。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-02-07 DOI: 10.3934/mbe.2025014

Andras Balogh, Tamer Oraby

{"title":"Stochastic games of parental vaccination decision making and bounded rationality.","authors":"Andras Balogh, Tamer Oraby","doi":"10.3934/mbe.2025014","DOIUrl":"10.3934/mbe.2025014","url":null,"abstract":"Vaccination is an effective strategy to prevent the spread of diseases. However, hesitancy and rejection of vaccines, particularly in childhood immunizations, pose challenges to vaccination efforts. In that case, according to rational decision-making and classical utility theory, parents weigh the costs of vaccination against the costs of not vaccinating their children. Social norms influence these parental decision-making outcomes, deviating their decisions from rationality. Additionally, variability in values of utilities stemming from stochasticity in parents' perceptions over time can lead to further deviations from rationality. In this paper, we employ independent white noises to represent stochastic fluctuations in parental perceptions of utility functions of the decisions over time, as well as in the disease transmission rates. This approach leads to a system of stochastic differential Eqs of a susceptible-infected-recovered (SIR) model coupled with a stochastic replicator Eq. We explore the dynamics of these Eqs and identify new behaviors emerging from stochastic influences. Interestingly, incorporating stochasticity into the utility functions for vaccination and nonvaccination leads to a decision-making model that reflects the bounded rationality of humans. Noise, like social norms, is a two-sided sword that depends on the degree of bounded rationality of each group. We also perform a stochastic optimal control as a discount to the cost of vaccination to counteract bounded rationality.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 2","pages":"355-388"},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An exploration of modeling approaches for capturing seasonal transmission in stochastic epidemic models. 在随机流行病模型中捕捉季节性传播的建模方法的探索。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-24 DOI: 10.3934/mbe.2025013

Mahmudul Bari Hridoy

{"title":"An exploration of modeling approaches for capturing seasonal transmission in stochastic epidemic models.","authors":"Mahmudul Bari Hridoy","doi":"10.3934/mbe.2025013","DOIUrl":"10.3934/mbe.2025013","url":null,"abstract":"Seasonal variations in the incidence of infectious diseases are a well-established phenomenon, driven by factors such as climate changes, social behaviors, and ecological interactions that influence host susceptibility and transmission rates. While seasonality plays a significant role in shaping epidemiological dynamics, it is often overlooked in both empirical and theoretical studies. Incorporating seasonal parameters into mathematical models of infectious diseases is crucial for accurately capturing disease dynamics, enhancing the predictive power of these models, and developing successful control strategies. In this paper, I highlight key modeling approaches for incorporating seasonality into disease transmission, including sinusoidal functions, periodic piecewise linear functions, Fourier series expansions, Gaussian functions, and data-driven methods. These approaches are evaluated in terms of their flexibility, complexity, and ability to capture distinct seasonal patterns observed in real-world epidemics. A comparative analysis showcases the relative strengths and limitations of each method, supported by real-world examples. Additionally, a stochastic Susceptible-Infected-Recovered (SIR) model with seasonal transmission is demonstrated through numerical simulations. Important outcome measures, such as the basic and instantaneous reproduction numbers and the probability of a disease outbreak derived from the branching process approximation of the Markov chain, are also presented to illustrate the impact of seasonality on disease dynamics.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 2","pages":"324-354"},"PeriodicalIF":2.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0