Sensitivity Of Metrics Research Articles

Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts.

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease.

Spatial extent-based measures of how far amyloid beta (Aβ) has spread throughout the neocortex may be more sensitive than traditional Aβ-positron emission tomography (PET) measures of Aβ level for detecting early Aβ deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aβ's association with tau proliferation and cognitive decline. Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aβ level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+. EXT enabled earlier detection of Aβ deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aβ+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL. These findings indicate EXT may be more sensitive to Aβ's role in preclinical AD than level and improve targeting of individuals for AD prevention trials. Aβ spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aβ EXT improved detection of Aβ below traditional PET thresholds. Early regional Aβ deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aβ EXT than Aβ level. Neocortical tau onset aligned with reaching widespread neocortical Aβ.

Story recall performance and AT classification via positron emission tomography: A comparison of logical memory and Craft Story 21

BackgroundEarly detection of Alzheimer's disease (AD) is one of the critical components of the global response to the growing dementia crisis. Analysis of serial position performance in story recall tests has yielded sensitive metrics for the prediction of AD at low cost. In this study, we examined whether serial position markers in two story recall tests (the logical memory test, LMT, and the Craft Story 21 test, CST) were sensitive to cross-sectional biomarker-based assessment of in vivo neuropathology. MethodsParticipants were selected from the Wisconsin Registry of Alzheimer's Prevention (n = 288; WRAP) and the Alzheimer's Disease Research Center (n = 156; ADRC), both from the University of Wisconsin–Madison. Average age at PET was 68.9 (6.7) and 67.0 (8.0), respectively. Data included tau and PiB PET, and LMT for WRAP participants and CST for ADRC participants. Two sets of Bayesian analyses (logistic regressions and ANCOVAs) were conducted within each cohort, separately. ResultsResults indicated that the A+T+ classification was best predicted, cross-sectionally, by the recency ratio (Rr), indexing how much of the end of the story was forgotten between initial learning and delayed assessment. Rr outperformed traditional scores and discriminated between A+T+ and A+T−/A-T-, in both cohorts. ConclusionsOverall, this study confirms that serial position analysis of LMT and CST data, and particularly Rr as an index of recency loss, is a valuable tool for the identification of in vivo tau pathology in individuals free of dementia. Diagnostic considerations are discussed.

Diffusion tensor imaging biomarkers and clinical assessments in amyotrophic lateral sclerosis (ALS) patients: an exploratory study.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. Biomarkers are needed to improve diagnosis, gauge progression, and evaluate treatment. Diffusion tensor imaging (DTI) is a promising biomarker for detecting microstructural alterations in the white matter tracts. This study aimed to assess DTI metrics as biomarkers and to examine their relationship with clinical assessments in patients with ALS. Eleven patients with ALS and 21 healthy controls (HCs) underwent 3T MRI with DTI. DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), were compared between key motor and extra-motor tract groups. Group comparisons and correlations between DTI metrics also correlated with clinical scores of disability (ALSFRS-R), muscle strength (dynamometry), and motor unit loss (MUNIX). Widespread differences were found between patients with ALS and HCs in DTI metrics, including decreased FA and increased diffusivity metrics. However, MD and RD are more sensitive metrics for detecting white matter changes in patients with ALS. Significant interhemispheric correlations between the tract DTI metrics were also observed. DTI metrics showed symmetry between the hemispheres and correlated with the clinical assessments. MD, RD, and AD increases significantly correlated with lower ALSFRS-R and MUNIX scores and weaker dynamometry results. DTI reveals microstructural damage along the motor and extra-motor regions in ALS patients. DTI metrics can serve as quantitative neuroimaging biomarkers for diagnosis, prognosis, monitoring of progression, and treatment. Combined analysis of imaging, electrodiagnostic, and functional biomarkers shows potential for characterizing disease pathophysiology and progression.

Connectivity of the Piriform Cortex and its Implications in Temporal Lobe Epilepsy.

The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients. We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity. We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy. Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy.

Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population

BackgroundBrain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. MethodsWe introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. ResultsOur findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. ConclusionThese findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset

Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors.

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.

Exploring the effects of interdependencies on energy systems in smart communities: A multi-domain modeling and quasi-Monte Carlo sensitivity analysis

An efficient smart and connected community (SCC) depends on the interconnectivity of essential infrastructure systems. However, current modeling tools are unable to determine which interconnections are most important to include, particularly as system dynamics become more complex with high-order effects. To bridge this gap, we propose a comprehensive framework that incorporates multi-layers, multi-blocks, and multi-agents to model interdependent infrastructure systems. Interconnections span cyber, physical, and logical aspects, including human interactions. With the equation-based object-oriented language Modelica, we model energy, transportation, communication, and water systems for a hypothetical SCC and assess higher-order interdependency effects during normal operation. Additionally, we develop a quasi-Monte Carlo sensitivity analysis framework and use variance-based sensitivity metrics to assess the impact of interdependencies on energy system operation. Compared to the decoupled baseline system, the energy consumption of logical interdependency cases varied by 23.3%, the cyber interdependency by 2.0%, and the nested global interdependency by 21.5%. The sensitivity analysis further revealed that interrelationships are not linear nor quadratic, but involve higher-order interactions between parameters. Specifically, occupancy and cyber delays had significant first-order effects. Road delays had a significant higher-order effect, which corresponded to a stronger influence on other model parameters. By modeling higher-order cascading dependencies, our proposed framework has the potential to improve the planning, operation, and co-design of SCCs by quantifying the importance of complex system interactions.

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.

A multi-population-based marine predators algorithm to train artificial neural network

Marine predators algorithm (MPA) is one of the recently proposed metaheuristic algorithms. In the MPA, position update mechanisms are implemented, emphasizing global search in the first part of the search process, balanced search in the middle, and local search in the last part. This may adversely affect the local search capability of the algorithm in the first part of the search process and the global search capability in the last part of the search process. To overcome these issues, an algorithm called MultiPopMPA with a multi-population and multi-search strategy is proposed in this study. Thanks to the proposed algorithm, local, balanced, and global search strategies of the original MPA were utilized from the beginning to the end of the search process. Thus, it is aimed to contribute to a more detailed search of the parameter space. In this study, the proposed algorithm has been applied in training artificial neural networks for 21 different classification datasets. The success of the algorithm has been scored on precision, sensitivity, specificity, and F1-score metrics and compared with eight different metaheuristic algorithms, including the original MPA. In terms of the mean rank of success, the proposed MultiPopMPA has been ranked first in precision, sensitivity, and F1-score metrics and ranked second in the specificity metric. In addition, it has been observed that the proposed algorithm outperforms its competitors in most cases in terms of convergence and stability. Finally, Wilcoxon’s signed-rank test results calculated through the MSE metric showed that the proposed algorithm produced statistically significant results in most cases.

Computational Predictions of MGMT Promoter Methylation in Gliomas: A Mathematical Radiogenomics Approach

In the treatment of glioblastomas, non-invasive methods for determining MGMT gene promoter methylation status are crucial due to their implications for chemotherapy responsiveness. This study utilizes a mathematical and computational framework to extract and analyze radiogenomic data from MRI images to predict the methylation status. The first step in our framework involves extracting radiogenomic data from MRI images. This process requires sophisticated image processing techniques to convert MRI scans into a format suitable for machine learning analysis. The features extracted include textural patterns, intensity distributions, and other relevant radiomic characteristics.To identify the most significant features, we employ a Random Forest (RF) algorithm. Mathematically, RF is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes for classification tasks. The importance of each feature is evaluated based on its contribution to the accuracy of the model, quantified by metrics such as Gini impurity or information gain. Employing advanced machine learning models like VGG19, ResNet50, and Sequential FCNet Artificial Neural Networks, alongside traditional classifiers such as Naive Bayes and Logistic Regression, we analyze features identified by a Random Forest algorithm. Our mathematical approach ensures rigorous evaluation of model accuracy through sensitivity, specificity, and other performance metrics, presenting the Sequential FCNet ANN combined with ResNet50 as the superior model. This research contributes to the field of precision healthcare by enhancing the mathematical methods used in the non-invasive diagnosis of glioblastomas.

PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment

This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.

Choledochal cancer region detection in hyperspectral images using U-Net based models

Cholangiocarcinoma (CCA) is a type of cancer that forms in the bile duct that carry digestive fluid from the liver. CCA is the primary form of liver cancer that affects population ranging from age 60 to 69 years. CCA is difficult to diagnose at an early stage. Hyperspectral (HS) imaging is an advanced imaging technique that combines spectroscopy with conventional imaging. HS imaging is an emerging field of study which can be used for early CCA detection. HS imaging involves capturing images across various spectral bands, which forms a three-dimensional data cube often called as hyperspectral data cube. In this study, we have utilized U-Net based models, namely U-Net and DenseUNet were used to perform semantic segmentation on the HS images of CCA tissues. A band selective approach was employed to derive a subset of meaningful bands based on the spectrum plot from the HS image. The HS images are further preprocessed with Principal Component Analysis (PCA). The models were further evaluated by computing the accuracy, AUC (Area under the ROC curve), sensitivity and specificity metrics. The proposed models, namely, U-Net and DenseUNet reported an overall accuracy of 73.47% and 77.09% respectively. The DenseUNet models outperforms the U-Net model on every evaluation metric. The proposed models were also compared with other state-of-the-art (SOTA) models trained on various HS dataset. This study explores the application of HS imaging in carcinoma detection. The findings of this study could be used for further enhancement of the approach.

Tuning the EKF using estimated measurements (EKF-EM) for balanced performances

In this paper, the conventional Extended Kalman Filter (EKF) algorithm has been systematically extended to the estimated measurement (EKF-EM) paradigm. The derivation of the EKF-EM state and state error estimates establishes the relation of the robustness and sensitivity metrics to the measurement projected Kalman gain. Recently, these metrics are being used ad-hoc for obtaining desired EKF performances in various signal processing applications. The EKF-EM is used to develop a process noise adjustment algorithm to tune the EKF for balanced performances. This proposed method is applied and validated in 3 diverse applications:(a) ensure convergent comparable filter performances for an originally divergent EKF in the 3D Lorenz attractor problem, (b) obtain spectral and audio improvement of a practical noise corrupted speech signal and (c) determine a suitable process model out of several options for balanced filter performances in a realistic 2D ballistic target tracking scenario.

Advancements in Image Enhancement and Attention based EfficientDet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection

Introduction: Osteosarcoma is a malignant bone tumor that frequently spreads to the lungs, hence therapy effectiveness depends on early identification. However, noise and subtle characteristics still pose a challenge for reliable Lung Nodules Detection (LND) in medical pictures. In earlier work, SSD-VGG16 was implemented to provide a bounding box with an accuracy score that represented a single osteosarcoma nodule. Increasing model complexity is sometimes necessary to achieve improved accuracy with current approaches, which might worsen their computing inefficiencies.Methods: For accurate osteosarcoma lung nodule identification, this study offers the hybrid Dynamic Virtual Bats Algorithm with Attention based Efficient Object identification (A- EfficientDet). In order to improve the quality and informativeness of clinical pictures, this study suggests including Chebyshev filtering into the pre-processing pipeline. It focuses on CT scans for the purpose of detecting lung nodules associated with osteosarcoma. Additionally, provide the optimized A-EfficientDet model, a hybrid EfficientDet model improved using the DVBA optimization technique for accurate lung nodule identification. Results: The effectiveness of the suggested strategy in attaining accurate osteosarcoma LND is demonstrated by the experimental findings. Chebyshev filtering is incorporated during the pre-processing step, which leads to more accurate detection findings by improving the signal-to-noise ratio (SNR) and lung nodule visibility. Conclusion: Additionally, the improved EfficientDet model demonstrates its suitability for clinical applications in early osteosarcoma detection and treatment monitoring by achieving (SOTA) State-Of-The-Art execution by the metrics of sensitivity, specificity, and F1 score.

Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms

Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of intrusion detection. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study developed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifiers. The model was also tested without a mayfly algorithm. The model's efficacy was determined through a comparison of its accuracy, specificity, precision, sensitivity, and F1 score. The experimental outcomes revealed that the proposed model is more efficient than existing models based on the performance evaluation and the CIC-IDS 2017 dataset employed in this research. Accuracy scores of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. In comparison to existing models, the proposed model yielded better results in terms of accuracy, sensitivity, specificity, and F1-score metrics. The model's sturdiness can be attributed to the use of mayfly techniques, which harness the strength in PSO, GA and FA for selecting optimal feature subsets. The results of this research provide a reliable dimensionality reduction model that may be used in the field of computer networks for intrusion detection and enhancement of security in computer networking environments.

In vitro to in vivo extrapolation from 3D hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrhythmia risk assessment.

Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. In vitro human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing in vitro and in vivo models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established 3D human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted in vitro to in vivo therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for 2 physiologically relevant metrics of proarrhythmic risk (i.e. action potential duration and triangulation assessed by optical mapping) across concentrations spanning 3 orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of 4 compounds with a range of known proarrhythmic risk profiles (i.e. quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human-induced pluripotent stem cell-based 3D cardiac tissue models as new approach methodologies that enable in vitro proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.

Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning

Background The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilized to create real-time navigation systems during laparoscopic and robotic right adrenalectomy. In this study, we aimed to develop deep learning models that can identify critical anatomical structures during minimally invasive right adrenalectomy. Methods In this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system. Semantic segmentation of the liver, the inferior vena cava (IVC), and the right adrenal gland were performed. Fifty random images per patient during the dissection phase were extracted from videos. The experiments on the annotated images were performed on two state-of-the-art segmentation models named SwinUNETR and MedNeXt, which are transformer and convolutional neural network (CNN)-based segmentation architectures, respectively. Two loss function combinations, Dice-Cross Entropy and Dice-Focal Loss were experimented with for both of the models. The dataset was split into training and validation subsets with an 80:20 distribution on a patient basis in a 5-fold cross-validation approach. To introduce a sample variability to the dataset, strong-augmentation techniques were performed using intensity modifications and perspective transformations to represent different surgery environment scenarios. The models were evaluated by Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) which are widely used segmentation metrics. For pixelwise classification performance, accuracy, sensitivity and specificity metrics were calculated on the validation subset. Results Out of 20 videos, 1000 images were extracted, and the anatomical landmarks (liver, IVC, and right adrenal gland) were annotated. Randomly distributed 800 images and 200 images were selected for the training and validation subsets, respectively. Our benchmark results show that the utilization of Dice-Cross Entropy Loss with the transformer-based SwinUNETR model achieved 78.37%, whereas the CNN-based MedNeXt model reached a 77.09% mDSC score. Conversely, MedNeXt reaches a higher mIoU score of 63.71% than SwinUNETR by 62.10% on a three-region prediction task. Conclusion Artificial intelligence-based systems can predict anatomical landmarks with high performance in minimally invasive right adrenalectomy. Such tools can later be used to create real-time navigation systems during surgery in the near future.

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor. A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network's performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network's performance utilized Dice scores, precision, and sensitivity metrics. Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively. The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation.