BioMedInformatics

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, grey matter, and cerebrospinal fluid by using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

Jake McNaughton

Samantha Holdsworth

Benjamin Chong

Justin Fernandez

Vickie Shim

Alan Wang

Synthetic MRI Generation from CT Scans for Stroke Patients

从CT扫描生成中风患者的合成MRI

Background: Intracranial neoplasm, often referred to as a brain tumor, is an abnormal growth or mass of tissues in the brain. The complexity of the brain and the associated diagnostic delays cause significant stress for patients. This study aims to enhance the efficiency of MRI analysis for brain tumors using deep transfer learning. Methods: We developed and evaluated the performance of five pre-trained deep learning models—ResNet50, Xception, EfficientNetV2-S, ResNet152V2, and VGG16—using a publicly available MRI scan dataset to classify images as glioma, meningioma, pituitary, or no tumor. Various classification metrics were used for evaluation. Results: Our findings indicate that these models can improve the accuracy of MRI analysis for brain tumor classification, with the Xception model achieving the highest performance with a test F1 score of 0.9817, followed by EfficientNetV2-S with a test F1 score of 0.9629. Conclusions: Implementing pre-trained deep learning models can enhance MRI accuracy for detecting brain tumors.

Amarnath Amarnath

Ali Al Bataineh

Jeremy A. Hansen

Transfer-Learning Approach for Enhanced Brain Tumor Classification in MRI Imaging

迁移学习方法在MRI影像中增强脑肿瘤分类的应用

(1) Background: Differences in health outcomes across populations are due to disparities in access to the social determinants of health (SDoH), such as educational level, household income, and internet access. With several positive outcomes reported with patient portal use, examining the associated social determinants of health is imperative. Objective: This study analyzed the association between social determinants of health—education, health insurance, household income, rurality, and internet access—and patient portal use among adults in the United States before and after the COVID-19 pandemic. (2) Methods: The research used a quantitative, retrospective study design and secondary data from the combined cycles 1 to 4 of the Health Information National Trends Survey 5 (N = 14,103) and 6 (N = 5958). Descriptive statistics and logistic regression were conducted to examine the association between the variables operationalizing SDoH and the use of patient portals. (3) Results: Forty-percent (40%) of respondents reported using a patient portal before the pandemic, and this increased to 61% in 2022. The multivariable logistic regression showed higher odds of patient portal utilization by women compared to men (AOR = 1.56; CI, 1.32–1.83), those with at least a college degree compared to less than high school education (AOR = 2.23; CI, 1.29–3.83), and annual family income of USD 75,000 and above compared to those &lt;USD 20,000 (AOR = 1.59; CI, 1.18–2.15). Those with access to the internet and health insurance also had significantly higher odds of using their patient portals. However, those who identified as Hispanic and non-Hispanic Black and residing in a rural area rather than urban (AOR = 0.72; CI, 0.54–0.95) had significantly lower odds of using their patient portals even after the pandemic. (4) Conclusions: The social determinants of health included in this study showed significant influence on patient portal utilization, which has implications for policymakers and public health stakeholders tasked with promoting patient portal utilization and its benefits.

Elizabeth Ayangunna

Gulzar H. Shah

Hani Samawi

Kristie C. Waterfield

Ana M. Palacios

Association Between Social Determinants of Health and Patient Portal Utilization in the United States

美国社会健康决定因素与患者门户使用之间的关联

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate.

None Aarti

Swathi Gowroju

Mst Ismat Ara Begum

A. S. M. Sanwar Hosen

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

基于深度学习和动态特征包优化的帕金森病最佳特征选择与分类

Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research.

Carolina Magalhaes

Joaquim Mendes

Ricardo Vardasca

Systematic Review of Deep Learning Techniques in Skin Cancer Detection

皮肤癌检测中深度学习技术的系统综述

Background: Heart failure poses a significant global health challenge, with high rates of readmission and mortality. Accurate models to predict these outcomes are essential for effective patient management. This study investigates the impact of data pre-processing techniques on XGBoost model performance in predicting all-cause readmission and mortality among heart failure patients. Methods: A dataset of 168 features from 2008 heart failure patients was used. Pre-processing included handling missing values, categorical encoding, and standardization. Four imputation techniques were compared: Mean, Multivariate Imputation by Chained Equations (MICEs), k-nearest Neighbors (kNNs), and Random Forest (RF). XGBoost models were evaluated using accuracy, recall, F1-score, and Area Under the Curve (AUC). Robustness was assessed through 10-fold cross-validation. Results: The XGBoost model with kNN imputation, one-hot encoding, and standardization outperformed others, with an accuracy of 0.614, recall of 0.551, and F1-score of 0.476. The MICE-based model achieved the highest AUC (0.647) and mean AUC (0.65 ± 0.04) in cross-validation. All pre-processed models outperformed the default XGBoost model (AUC: 0.60). Conclusions: Data pre-processing, especially MICE with one-hot encoding and standardization, improves XGBoost performance in heart failure prediction. However, moderate AUC scores suggest further steps are needed to enhance predictive accuracy.

Qisthi Alhazmi Hidayaturrohman

Eisuke Hanada

Impact of Data Pre-Processing Techniques on XGBoost Model Performance for Predicting All-Cause Readmission and Mortality Among Patients with Heart Failure

数据预处理技术对预测心力衰竭患者全因再入院和死亡率的XGBoost模型性能的影响

Background: Gene regulatory networks (GRNs) are complex gene interactions essential for organismal development and stability, and they are crucial for understanding gene-disease links in drug development. Advances in bioinformatics, driven by genomic data and machine learning, have significantly expanded GRN research, enabling deeper insights into these interactions. Methods: This study proposes and demonstrates the potential of BioGRNsemble, a modular and flexible approach for inferring gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, the BioGRNsemble methodology focuses on providing trimmed-down sub-regulatory networks consisting of transcription and target genes. Results: The methodology was successfully tested on a Drosophila melanogaster Eye gene expression dataset. Our validation analysis using the TFLink online database yielded 3703 verified predicted gene links, out of 534,843 predictions. Conclusion: Although the BioGRNsemble approach presents a promising method for inferring smaller, focused regulatory networks, it encounters challenges related to algorithm sensitivity, prediction bias, validation difficulties, and the potential exclusion of broader regulatory interactions. Improving accuracy and comprehensiveness will require addressing these issues through hyperparameter fine-tuning, the development of alternative scoring mechanisms, and the incorporation of additional validation methods.

Abdul Jawad Mohammed

Amal Khalifa

Drosophila Eye Gene Regulatory Network Inference Using BioGRNsemble: An Ensemble-of-Ensembles Machine Learning Approach

使用BioGRNsemble的果蝇眼基因调控网络推断：一种集成-集成机器学习方法

涉及主题	genome and sequence analysisgene and protein expressiongenetics and population analysiscomparative genomicscomputational functional genomicsprotein dynamics and function
出版信息	出版商: Multidisciplinary Digital Publishing Institute (MDPI)，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2021，原创研究文献占比: ，自引率:， Gold OA占比:
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Synthetic MRI Generation from CT Scans for Stroke Patients

Transfer-Learning Approach for Enhanced Brain Tumor Classification in MRI Imaging

Association Between Social Determinants of Health and Patient Portal Utilization in the United States

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

Systematic Review of Deep Learning Techniques in Skin Cancer Detection

Systematic Review of Deep Learning Techniques in Skin Cancer Detection

Association Between Social Determinants of Health and Patient Portal Utilization in the United States

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

Impact of Data Pre-Processing Techniques on XGBoost Model Performance for Predicting All-Cause Readmission and Mortality Among Patients with Heart Failure

Drosophila Eye Gene Regulatory Network Inference Using BioGRNsemble: An Ensemble-of-Ensembles Machine Learning Approach