Search Results (41)

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R² = 0.47), whereas k-NN attained 10.8 years (R² = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption. Full article

Comparative investigations on homogenous cranial series have demonstrated that metopism is linked to a specific configuration of the cranial vault; however, there are no comparative data concerning the endocranial morphology in this condition. This study aimed to compare the endocranial space in metopic and control crania using morphometric analysis and machine learning algorithms. For this purpose, a series of 230 (184 control and 46 metopic) dry crania of contemporary adult Bulgarian males were scanned using an industrial µCT system. The 3D coordinates of 47 landmarks were collected on the endocranial surface. All possible measurements between the landmarks were calculated as Euclidean distances. The resultant 1081 measurements represented the initial dataset, which was reduced to smaller datasets applying different criteria. The derived datasets were used for learning a set of classification models by machine learning algorithms. The morphometric analysis showed that in the metopic crania some segments of the anterior and middle cranial fossae were significantly longer, and the landmark endobregma was significantly closer to the anterior and middle sections of the cranial base. The most accurate model, with a classification accuracy of 85%, was the Naive Bayes one learned on a dataset of 69 attributes assembled after an attribute selection procedure. Full article

Background/Objectives: Gamma knife radiosurgery (GKRS) is widely performed as an adjuvant management of patients with residual or recurrent pituitary adenoma (PA). However, the variability in the tumor volume response to GKRS emphasizes the need for reliable predictors of treatment outcomes. The application of radiomics, an analytical approach for quantitative imaging, remains unexplored in predicting treatment responses for PAs. This study aimed to pioneer the use of radiomic MRI analysis to predict the volumetric response of PA to GKRS. Methods: This retrospective observational cohort study involved 81 patients who underwent GKRS for PA. Pre-treatment 3-Tesla MRI scans were used to extract radiomic features capturing the intensity, shape, and texture of the tumors. Radiomic signatures were generated using the least absolute shrinkage and selection operator (LASSO) for feature selection, in conjunction with several classifiers: random forest, naïve Bayes, kNN, logistic regression, neural network, and SVM. Results: The models demonstrated predictive performance in the test folds, with AUC values ranging from 0.759 to 0.928 and R² values between 0.272 and 0.665. Single-sequence T1w, dual-sequence T1w + CE-T1w, and multi-modality including clinicopathological (CP) parameters (CP + T1w + CE-T1w) achieved rather similar prognostic performance in the test folds, with respective AUCs of 0.928, 0.899, and 0.909. All these radiomics models significantly outperformed a benchmark model involving only CP features (AUC = 0.846). Conclusions: This study represents a radiomic analysis focused on predicting the volume response of PAs to GKRS to facilitate treatment individualization. The developed MRI-based radiomics models exhibited superior classification performance compared with the benchmark model composed solely of standard clinicopathological parameters. Full article

Red jujube is a nutritious food, known as the “king of all fruits”. The quality of Chinese red jujube is closely associated with its place of origin. To classify Chinese red jujube more correctly, based on the combination of adaptive boosting (Adaboost) and common vectors linear discriminant analysis (CLDA), Adaboost-CLDA was proposed to classify the near-infrared (NIR) spectra of red jujube samples. In the study, the NIR-M-R2 spectrometer was employed to scan red jujube from four different origins to acquire their NIR spectra. Savitzky–Golay filtering was used to preprocess the spectra. CLDA can effectively address the “small sample size” problem, and Adaboost-CLDA can achieve an extremely high classification accuracy rate; thus, Adaboost-CLDA was performed for feature extraction from the NIR spectra. Finally, K-nearest neighbor (KNN) and Bayes served as the classifiers for the identification of red jujube samples. Experiments indicated that Adaboost-CLDA achieved the highest identification accuracy in this identification system for red jujube compared with other feature extraction algorithms. This demonstrates that the combination of Adaboost-CLDA and NIR spectroscopy significantly enhances the classification accuracy, providing an effective method for identifying the geographical origin of Chinese red jujube. Full article

Background/Objectives: In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images. The aim of our proof-of-concept study is to investigate whether machine learning-based tumor diagnosis is also possible using CT images. Methods: We investigate the differentiability of histologically confirmed low-grade and high-grade gliomas. Three conventional machine learning algorithms and a neural net are tested. In addition, we analyze which of the common imaging methods (MRI or CT) appears to be best suited for the diagnostic question under investigation when machine learning algorithms are used. For this purpose, we compare our results based on CT images with numerous studies based on MRI scans. Results: Our best-performing model includes six features and is obtained using univariate analysis for feature preselection and a Naive Bayes approach for model construction. Using independent test data, this model yields a mean AUC of 0.903, a mean accuracy of 0.839, a mean sensitivity of 0.807 and a mean specificity of 0.864. Conclusions: Our results demonstrate that low-grade and high-grade gliomas can be differentiated with high accuracy using machine learning algorithms, not only based on the usual MRI scans, but also based on CT images. In the future, such CT-image-based models can help to further accelerate brain tumor diagnostics and to reduce the number of necessary biopsies. Full article

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = −0.11, p_FDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c’ = 0.01, [−0.0181, −0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship. Full article

This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train–test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages. Full article

Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the ‘Mejhoul’ and ‘Boufeggous’ cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried ‘Mejhoul’ reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of ‘Boufeggous’ were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit. Full article

The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA⁽²⁾ errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA⁽²⁾ errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters. Full article

Cyber deception technology plays an important role in monitoring attackers’ activities and detecting new attack types. However, in a deceptive environment, low-risk attack traffic, such as scanning, is included in large quantities and acts as noise. Therefore, even though high-risk traffic is actually present, it may be overlooked, or the analysis algorithm’s accuracy regarding traffic may be reduced, causing significant difficulties in intrusion detection and analysis processes. In this study, we propose a model that can identify and filter the ordinal scale risk of the source IP in deceptive environment-generated traffic. This model aims to quickly classify low-risk attacks, including information gathering and scanning, which are widely and repeatedly performed, as well as high-risk attacks, rather than classifying specific types of attacks. Most existing deceptive technology-based Cyber Threat Intelligence (CTI) generation studies have been limited in their applicability to real-world environments because data labeling, learning, and detection processes using AI algorithms that consume significant amounts of time and computing resources. Here, the Naive Bayes discriminant analysis-based ordinary scale classification model showed higher accuracy for low-risk attack classification, while consuming significantly fewer resources than the models presented in other studies do. The accuracy of the current active deceptive environment traffic analysis research may be enhanced by filtering low-risk traffic via preprocessing. Full article

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body’s interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed. Full article

Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons’ feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills. Full article

Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a novel non-destructive evaluation method for blade composite materials, which employs Frequency Modulated Continuous Wave (FMCW) radar, robotics and machine learning (ML) analytics. We show that using FMCW raster scan data, our ML algorithms (SVM, BP, Decision Tree and Naïve Bayes) can distinguish different types of composite materials with accuracy of over 97.5%. The best performance is achieved by SVM algorithms, with 94.3% accuracy. Furthermore, the proposed method can also achieve solid results for detecting surface defect: interlaminar porosity with 80% accuracy overall. In particular, the SVM classifier shows highest accuracy of 92.5% to 98.9%. We also show the ability to detect air voids of 1mm differences within the composite material WT structure with 94.1% accuracy performance using SVM, and 84.5% using Naïve Bayes. Lastly, we create a digital twin of the physical composite sample to support the integration and qualitative analysis of the FMCW data with respect to composite sample characteristics. The proposed method explores a new sensing modality for non-contact surface and subsurface for composite materials, and offer insights for developing alternative, more cost-effective inspection and maintenance regimes. Full article

As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR). Full article

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%. Full article