To compare dose to the organ at risk (OAR) and target coverage of carbon-ion beam, protons, and photons for patients with head and neck cancer. Treatment plans for carbon-ion pencil beam scanning (C-PBS) (64 Gy (RBE) in 16 fractions), proton pencil beam scanning (P-PBS), and volumetric modulated arc therapy (VMAT) (70 Gy in 35 fractions for P-PBS and VMAT) were generated and compared using different dose constraints per treatment modality. Dose metrics (e.g. D95,V20) were analyzed. Statistical significance was assessed by the Wilcoxon signed-rank test. Also, we investigated howmany normal tissues were irradiated above the constraint after achieving the planning goals (pass rate) in the OARs. C-PBS outperformed P-PBS and VMAT in PTV coverage (p = 0.01 for both); however, P-PBS and VMAT did not differ substantially from each another (p = 0.35). C-PBS was superior in limiting the dose to the OAR. The pass rates for C-PBS, P-PBS, and VMAT were 94%, 81%, and 69%, respectively. C-PBS demonstrated superior performance compared to VMAT and P-PBS in terms of dose conformation to the target volume and normal tissue sparing, and achieved the highest pass rate in meeting dose constraints.

Telehealthcare is an evolving area that typically employs cloud-connected wireless biomedical gadgets for diagnosis, monitoring, and prognosis of diseases. In such environment, data compression, transmission, security and processing effectiveness are key issues. This paper proposes a new method for the automated diagnosis of arrhythmia in an efficient and effective manner. The proposed technique fuses a combination of Level-Crossing Analog-Digital Converters (LCADCs), Enhanced Activity Selection Algorithm (EASA), Adaptive-Rate Filtering (ARF), and ID-CNN. The electrocardiogram (ECG) signal is sampled by using the level-crossing concept. The QRS based segmentation and ARF with lower tap filters are realized. The denoised segments, without any handcrafted features extraction, are classified with one dimensional (1-D) deep convolutional neural network (CNN). Comparison is performed with using statistically extracted features in combination with CNN, existing state-of-the-art classical methods for ECG classification, and recent advanced deep learning models. The goal is to reach an efficient method by attaining a real-time data size reduction, computationally efficient signal preconditioning and a lower latency accurate classification. Five clinically important classes of arrhythmias, collected from the MIT-BIH dataset, are used to examine its applicability. Our experimental results show a 4.2-times diminishing in the count of acquired samples, on average, compared to conventional fix-rate counterparts. Similarly, data dimension reduction results in a more than 7.2-times computational effectiveness of the post denoising stage over the conventional counterparts. Moreover, classification latency is also significantly reduced while still achieving an accuracy rate of 99%.

The purpose of this study is to develop a CT radiomics-based interpretable prognostic diagnostic model for vascularized bone graft hip preservation, with the objective of predicting postoperative hip preservation outcomes. The study recruited 107 patients, collecting preoperative CT scans and preoperative blood biochemistry data. Among these patients, 27 had a good prognosis, while 80 had a poor prognosis. Five machine learning algorithms were employed to develop predictive models evaluating the effectiveness of modified vascularized bone implants in hip preservation. The interpretability of the top-performing models was assessed using SHapley Additive exPlanations (SHAP). Nine radiomic features were extracted from preoperative CT scans to develop a radiomic score. Through univariate and multivariate logistic regression analyses, clinical indicators, including patient age and preoperative platelet-to-lymphocyte ratio (PLR), were retained. Fifteen models were constructed, incorporating clinical, radiomic, and combined approaches across various algorithms. The combined model utilizing the XGBoost algorithm demonstrated superior performance, achieving an AUC of 0.90 (95% CI 0.81-0.98) on the training set and 0.87 (95% CI 0.75-1.00) on the test set. These results showed improvements of around 31% and 28%, respectively, compared to the top performing clinical and radiomic models (p < 0.05). High radiomics scores, a high PLR, and older age were identified as significant predictors of poor prognosis. A robust joint clinical and radiomics model was developed using the XGBoost algorithm for predicting the prognosis of hip-preserving surgery. The predictions of this model were interpreted using SHAP to enhance clinical applications.

This study aims to explore the prognostic value of regionally modulated radiomics for patients with head and neck cancer (HNC) in positron emission tomography/computed tomography (PET/CT) imaging. The dataset included 224 HNC patients who underwent PET/CT imaging at five different centers. The primary tumor was manually contoured by experienced radiologists. For introducing regionally modulated radiomics, we developed four fuzzy masks by applying Gaussian filter, and four peritumor-included masks by applying morphological operations. For each patient, a total of 326 radiomic features were extracted from each of nine masks. Multivariate Cox proportional hazards model with ensemble strategy was adopted to construct classical, fuzzy, and peritumoral based prognostic models, respectively, for predicting progression-free survival. ComBat harmonization was applied to adjust for multicenter variability. A consistent modelling approach was employed to ensure the independence and comparability of these models. The models were evaluated by C-index, log-rank test, and the area under the time-dependent ROC curve (tAUC). The fuzzy radiomics model applied with 5 mm FWHM of Gaussian filter demonstrated superior performance compared to classical radiomics model (Testing C-index, 0.735 vs. 0.685; log-rank test, p < 0.007 vs. p < 0.035). Peritumoral radiomics models showed slightly improved performance compared to classical radiomics model (Testing C-index, 0.727 vs. 0.685; log-rank test, p < 0.014 vs. p < 0.035). The tAUC demonstrated consistent findings with the C-index. The harmonization strategy showed further improved performance for both fuzzy and peritumoral models. These results showed that regionally modulated radiomics analysis was superior for estimating prognosis in this multicenter HNC cohort when compared to classical radiomics. This demonstrated the potentially prognostic values by considering regional variations in radiomics analysis.

Cardiovascular diseases represent a leading cause of mortality worldwide, necessitating accurate and early diagnosis for improved patient outcomes. Current diagnostic approaches for cardiac abnormalities often present challenges in clinical settings due to their complexity, cost, or limited accessibility. This study develops two deep learning architectures that offer fast, accurate, and cost-effective methods for automatic diagnosis of cardiac diseases, focusing specifically on addressing the critical challenge of limited labeled datasets in medical contexts. We propose two methodologies: first, a Multi-Branch Deep Convolutional Neural Network (MBDCN) that emulates human auditory processing by utilizing diverse convolutional filter sizes and power spectrum input for enhanced feature extraction; second, a Long Short-Term Memory-Convolutional Neural (LSCN) model that integrates LSTM blocks with MBDCN to improve time-domain feature extraction. The synergistic integration of multiple parallel convolutional branches with LSTM units enables superior performance in heart sound analysis. Experimental validation demonstrates that LSCN achieves multiclass classification accuracy of 89.65% and binary classification accuracy of 93.93%, significantly outperforming state-of-the-art techniques and traditional feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and wavelet transforms. A comprehensive fivefold cross-validation confirms robustness of our approach across varying data partitions. These findings establish the efficacy of our proposed architectures for automated heart sound analysis, offering clinically viable and computationally efficient solutions for early detection of cardiovascular diseases in diverse healthcare environments.

Early prediction of brain metastases (BM) in epidermal growth factor receptor (EGFR) positive lung adenocarcinoma patients is critical for improving treatment strategies and prognosis. This study aimed to enhance BM risk prediction within two years for lung adenocarcinoma patients by using lung CT images and clinical data both derived from initial diagnosis. This study comprised 173 patients with EGFR positive lung adenocarcinoma who underwent diagnostic CT and was stratified into 93 patients with BM and 80 patients without BM. We extracted a total of 1334 radiomic features from each manually delineated primary pulmonary nodule. Least absolute shrinkage and selection operator (LASSO) method was applied to select the optimal image features. Subsequently, the clinical model, radiomic model and hybrid model were constructed employing logistic regression, random forest (RF), support vector machine (SVM), and light gradient boosting machine (LGBM) algorithms separately. Ultimately, the model was evaluated and interpreted utilizing the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and shapley additive explanations (SHAP). The hybrid model consistently exhibited superior predictive performance. Specifically, the logistic regression-based hybrid model exhibited the highest overall performance metrics, with an AUC of 0.94 (95% CI 0.81-0.99). This study demonstrates that the logistic regression-based hybrid model can effectively predict BM in EGFR positive lung adenocarcinoma patients at their initial diagnosis, aiding physicians in developing more accurate treatment plans.

Ovarian cancer is often diagnosed at advanced stages, with high-grade serous ovarian cancer (HGSOC) accounting for 70-80% of fatalities. Current predictive tools, limited by single-time-point data, fail to capture subtle temporal changes indicative of relapse. To evaluate the performance of OvarXNet, a novel deep learning framework integrating longitudinal PET/CT imaging and clinical data for early prediction of ovarian cancer relapse. This retrospective study included 58 advanced-stage HGSOC patients (mean age, 56 ± 10.4 years) who underwent [F]FDG PET/CT scans from April 2019 to January 2025. Patients with uncontrolled diabetes or recent cancers were excluded. Each patient had a median of three PET/CT scans and associated clinical data. The OvarXNet framework combines 3D convolutional neural networks (CNNs) for volumetric feature extraction and bidirectional gated recurrent units for temporal analysis. Statistical analyses included area under the receiver operating characteristic curve (AUC), precision-recall (PR) metrics, and calibration plots. Fifty-eight patients (mean age 56 ± 10.4 years) contributed 1914 image sets post-augmentation. OvarXNet achieved an AUC of 0.92, outperforming single-time-point CNN (AUC: 0.84) and LSTM-based models (AUC: 0.89). PR analysis confirmed superior model performance (PR-AUC: OvarXNet > 0.90 vs. single-time-point CNN: 0.82). Calibration plots demonstrated robust probability estimates. Attention mechanisms highlighted time points with elevated CA-125 or progression-related clinical notes, enhancing interpretability. OvarXNet significantly improves early relapse prediction in advanced-stage HGSOC by leveraging longitudinal imaging and clinical data. The framework's accuracy and interpretability support its potential for guiding personalized treatment strategies.

The triceps kickback is a popular strength exercise targeting the arm muscles, often performed by women to enhance muscle strength and tone. However, physiological differences in endurance between women and men can make the exercise challenging, particularly as dumbbell weight increases. Higher weights may compromise proper form and reduce effective muscle contraction, yet the relationship between increased weight and muscle contraction remains underexplored. This study investigated the mechanical effects of varying dumbbell weights during rest-pause triceps kickback exercises in 14 women. Motion analysis with passive markers and EMG measurements from the triceps brachii were conducted. A link-segment model simulated in MATLAB Multibody calculated joint moments and muscle forces, while a finite element model of the triceps brachii, developed in COMSOL Multiphysics 6.0, analyzed structural responses to these forces. Results revealed no linear correlation between increasing exercise force and muscle contraction intensity. These findings provide insights into the biomechanics of the triceps kickback and suggest that weight increments should be carefully managed to optimize muscle activation and exercise effectiveness. This study contributes valuable data for designing tailored strength-training programs, especially for women.

Orthovoltage x-rays are useful for the treatment of some superficial cancers and benign conditions. An orthovoltage machine has numerous different applicators (open and closed ended) and energies that require measurements for all different applicator-energy combinations in addition to patient-specific Standoff Factor (SF) measurements, which is arduous and time-consuming. This study aimed to introduce a simple, accurate, and practical method to calculate SF. This factor is usually calculated based on the inverse square law (ISL), which is not an accurate approximation for closed-ended applicators. In this work, we introduced a simple, accurate, and practical method to calculate SF that is valid for both open-ended and closed-ended applicators. Xstrahl 300 therapy unit was used with two sets of Open-ended and Closed-ended applicators with energies up to 300 kVp. The proposed SF empirical formula and ISL were evaluated against the measurements. For open-ended applicators, the maximum Percentage Differences (PD) in calculated SF using the suggested formula and ISL were 0.84% and 1.97% relative to the measurement, respectively. For closed-ended applicators, the maximum PD was 2.53% and -8.12% using the suggested formula and ISL relative to the measurement, respectively. The results demonstrated satisfactory accuracy compared to the measured standoff factor values and superior accuracy when compared to the commonly used ISL method, particularly for closed-ended applicators. The study concluded that SF calculated using the proposed formula was in agreement with measured SF at clinically relevant standoff distances for all energies and applicators combinations. Thus, we recommend using this proposed formula for SF calculations.

Hepatic steatosis, affecting one-third of the global population, is a key challenge in gastroenterology with limited screening focus. It characterizes metabolic dysfunction-associated steatotic liver disease, which is increasingly prevalent and linked to metabolic issues, yet lacks accessible non-invasive early detection tools. This study evaluates an AI model's ability to predict controlled attenuation parameter (CAP) scores, providing qualitative estimates of mild and moderate or greater liver steatosis degrees. The study included 705 participants from a nutrition clinic, with data collected on 27 features such as physical exams, body measurements, and InBody270 results. CAP score was obtained from transient elastography findings. We developed a novel graph neural network (GNN) architecture that conceptualizes the human body as an interconnected graph structure to capture complex physiological relationships between different anatomical regions. The proposed GNN model significantly outperformed traditional machine learning approaches, achieving RMSE of 23.7 dB/m, MAE of 18.9 dB/m, and R of 0.87. Attention-guided feature importance analysis identified waist circumference, trunk fat mass, and neck circumference as the most influential predictors of CAP scores. The graph-based model outperforms traditional machine learning in predicting CAP scores, leveraging body relationships for reliable, non-invasive hepatic steatosis screening across all severities.

Due to high cost, training phantoms are often inaccessible and their manufacturing technologies are quite sophisticated. The purpose of this paper is to develop an inexpensive and reproducible technology for creating ultrasound training phantoms. These phantoms are a 3D printed porous medium composed of 156-µm-thick photopolymer resin fibers and include models of cysts ranging from 4 to 8 mm in diameter, effectively simulating a muscle tissue with anechoic lesions. A custom software generates a virtual phantom model, enabling precise control over its properties. We believe that the results of the acoustic characteristics' measurements for the designed phantoms provide an opportunity to mimic muscle (1547 m/s) and breast (1510 m/s) tissues. Following the creation of the phantom, a series of assessments were conducted to evaluate its efficacy for needle insertion (involving 3 observers) and to identify its mimicked tissue type (with 29 observers participating). The findings revealed that the phantom is capable of enduring up to 300 punctures in a single location without exhibiting significant decline in image quality. A subsequent survey of ultrasound specialists, who possessed a range of professional experiences, indicated that the ultrasound images produced by the phantom predominantly corresponded to those of muscle tissues upon visual examination. The 3D printing process for the phantom 60 mm × 60 mm × 30 mm in size was completed in 3 h and 23 min. The proposed technology allows creating low-cost, long-lasting phantoms for training in ultrasound diagnostics and ultrasound-guided procedures. The phantom designed using widely available photopolymer resin, while the custom software and high-resolution 3D printing ensures reproducibility of the shape and positions of the fibers and inclusions. The phantom mimics muscle tissues with multiple cysts and can be used to develop basic coordination and navigation skills required for ultrasound diagnostics.

Nowadays, monitoring the health of elderly people at home or patients at the hospital on a regular basis is becoming necessary. Unfortunately, peer-to-peer treatment may require a longer time based on the availability of the doctors. In addition, it is practically impossible to go to hospitals for health checkups almost every day of the week. Hence, this research proposes an idea that can automate these processes without decreasing efficiency and reducing manual labor by integrating a healthcare system with the cyber layer to execute the automation processes. Previous text and image recognition studies used different machine learning and deep learning algorithms. However, in this study, an optical character recognition method ‛YOLO V8' is used, which provides a faster detection speed than other methods. The target was to retrofit biomedical devices such as blood pressure monitoring machines, digital thermometers, etc. using image processing techniques. To train the'YOLOv8' model, we have utilized two distinct image datasets that we have developed. The model showed an accuracy of 99.5% in detecting areas of concern on medical devices. Later, for recognition of values of different parameters from those devices a Convolutional Neural Network model is used, which confirms real-time validation employing 1000 images from different medical equipment. An accuracy of 99.7% has been achieved using this method. In the future, other medical devices such as heart rate monitors, pulse oximeters, etc. can be included in this system.

This study investigated the potential of combining baseline F-FDG PET tumor-to-liver ratio radiomics with demographic data, using machine learning, to classify lymphoma subtypes and differentiate between candidates for ABVD and R-CHOP therapy. Additionally, we assessed whether nodal radiomics alone is sufficient for treatment and subtype classification. We conducted a multi-center study involving 241 lymphoma patients, including 125 with Non-Hodgkin lymphoma (NHL) and 116 with Hodgkin lymphoma. Among these, 94 had high-grade NHL, whereas 110 had classical Hodgkin lymphoma. We utilized 107 radiomic features, along with demographic data, such as age, stage, gender, and weight, to develop predictive models for classifying lymphoma subtypes and selecting treatment regimens (ABVD vs. R-CHOP). Data harmonization was performed using ComBat, feature selection was done with SelectKBest, and three machine learning models (Logistic Regression, Random Forest, and XGBoost) were trained with hyperparameter tuning, followed by external validation. For the best model in each classifier on the external test, adding extra-nodal radiomic features improved performance for certain lymphoma subtypes. For NHL vs. HL, accuracy increased from 0.807-0.819, whereas NHL precision rose from 0.837-0.875. High-grade NHL precision improved notably from 0.821-0.962. In treatment classification, extra-nodal features boosted accuracy for R-CHOP from 0.783-0.839 and increased F1-scores for both R-CHOP and ABVD. This study demonstrated the promise of PET radiomics combined with demographic features for lymphoma classification and treatment decision-making. Overall, extra-nodal features enhanced high-grade NHL and treatment classification but had minimal impact on other lymphoma subtypes.

Beam modeling for radiotherapy treatment planning systems (RTPS) can be performed using representative beam data (RBD) or direct measurements. However, RBD typically excludes output factor (OPF) measurements for fields smaller than 3 × 3 cm. The Eclipse treatment planning system addresses this limitation by incorporating measured OPF data for fields as small as 1 × 1 cm. Although existing studies have primarily examined the accuracy of small-field OPFs for plastic scintillator detectors, studies directly comparing the OPF values obtained through RBD modeling with and without OPF measurements for small field sizes are limited. Therefore, this study proposes a novel measurement approach using data averaged from an ion chamber and diode detector for small-field dosimetry to provide critical insights into the integration of OPFs for these small field sizes in RBD-based beam modeling. We systematically evaluated the impact of small-field OPF measurements on beam modeling accuracy by comparing three distinct approaches: (1) RBD-based modeling without small-field OPF data, (2) RBD-based modeling incorporating measured small-field OPF data, and (3) modeling based solely on measured data, with and without the inclusion of 1 × 1 cm field sizes. In addition, we compared OPF values obtained from a W2 plastic scintillator detector with the averaged OPF values from a PinPoint 3D ion chamber and EDGE diode detector across multiple beam energies and flattening filter-free (FFF) configurations. Our analysis included field sizes ranging from 1 × 1 cm to 40 × 40 cm. The results demonstrated that for square fields, OPF calculation differences between RBD modeling with and without measured data were < 1.5%, < 4.5%, and < 4.5% at 1 × 1 cm, and < 0.5%, < 1.5%, and < 1.5% at 2  ×  2  cm, respectively. The RBD group exhibited a trend in which the OPF difference increased with the expansion of the irradiation field size. Notably, the most significant variations between modeling approaches occurred along the upper jaw expansion direction in rectangular fields. This suggests that a thorough evaluation is necessary for modeling results with an OPF ≤  1 × 1 cm. This study highlights the advantages and disadvantages of beam modeling using measured OPF and RBD, providing valuable insights for future facilities that rely solely on RBD for beam modeling.

Cognitive workload refers to the mental effort required to perform a task and plays a vital role in cognitive functioning and daily decision-making. The precise estimation of cognitive workload can increase efficiency and decrease mental errors. EEG signals are non-invasive and trustworthy, containing useful information about mental and cognitive tasks, and are very effective in measuring cognitive workload. This study aims to classify various cognitive workload levels using EEG signals, primarily by channel selection based on the Pearson Correlation Coefficient, to reduce computational complexity and facilitate real-time applications. As time-frequency decomposition techniques can provide simultaneous time and frequency information for more accurate analysis, three techniques were adopted: Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), and a hybrid approach combining both. After decomposition, ten statistical features were extracted, and the Improved Distance Evaluation technique was employed to select the most critical features. Classification was performed on these features using three classifiers: Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree. The findings revealed the important role of frontal EEG channels in assessing cognitive workload. Additionally, the combined use of MODWT and EMD with the SVM classifier yielded the best classification accuracy for both binary and three-class classification scenarios. The results indicate that the optimal choice of channels, combined with time-frequency decomposition methods, can significantly enhance classification accuracy while reducing system complexity in estimating cognitive workload.

The aim of this study is to assess the dosimetric advantages and clinical feasibility of the Half-Field Volumetric Modulated Arc Therapy technique in comparison to conventional Full-Field Arc Therapy and Intensity-Modulated Radiation Therapy for the treatment of prostate cancer. 120 Treatment plans were created for 24 prostate cancer patients using Half-Field, Full-Field, and Intensity Modulated static fields (5-, 7-, and 9-fields). The dosimetric parameters and the homogeneity index were evaluated for the different Planning Target Volumes included pelvic lymph nodes, seminal vesicles, and prostate. Additionally, the dose burden to organs at risk was assessed. The efficiency of the plans was analyzed based on monitor unit usage and the gamma index. Half-Field plans exhibited comparable target coverage to static fields while demonstrating superior homogeneity in comparison to Full-Field plans. This technique resulted in a significant reduction in bladder and rectum doses within the mid- and high-dose ranges, with a V30 for the bladder of 67.8% in Half-Field compared to 75.3% in Full-Field (p < 0.001). The Half-Field technique required a significantly fewer monitor units than the Intensitiy-Modulated technique (600.8 vs. 1172.7 for 5-field, p < 0.001) resulting in a notable reduction in treatment. Half-Field represents an effective combination of the dosimetric precision of static Intensity Modulated fields with the efficiency of Full-Field arc therapy, offering a promising alternative for prostate cancer treatment. The technique ensures reduced organ at risks doses, enhanced treatment homogeneity and lower complexity, making it a viable option for moderately hypofractionated radiotherapy protocols.

An aneurysm, enlargement of an artery or vein, weakens the surrounding vascular wall, making it susceptible to rupture and the possibility of life-threatening bleeding, ultimately resulting in death. The placement of flow-diverting stents is a highly utilized and effective method for treating aneurysms. This study presents a novel approach combining CFD simulations, deep neural networks (DNN), and differential evolution optimization (DEO) to optimize hemodynamic conditions in aneurysms. Initially, CFD simulations were conducted to generate a comprehensive dataset of 2,700 simulations with various stent configurations. This dataset was then used to train a DNN model, enabling accurate predictions of velocity, vorticity, and wall shear stress for any stent configuration. The model demonstrated consistent and reliable performance across different configurations. DEO was applied to identify the optimal stent, resulting in a configuration with seven struts. The optimal strut sizes were 0.3184, 0.9599, 0.7889, 0.9599, 1.0073, 1.0073, and 2.9283, with gap sizes of 0.2238, 0.5897, 0.3379, 0.2996, 0.2052, 0.0371, and 0.3068 between the struts. This configuration achieved superior performance in reducing velocity, vorticity, and maximum wall shear stress. The study demonstrated that increasing the number of struts, with a concentration at the proximal aneurysm neck, enhanced flow diversion and minimized hemodynamic risks, especially in regions vulnerable to rupture. Validation through additional CFD simulations confirmed the effectiveness of the optimized stent, demonstrating the potential of the proposed methodology to improve stent design and hemodynamic outcomes in aneurysm treatment.

Glioma is primarily treated through surgical resection, but accurately identifying tumor boundaries remains challenging. Traditional intraoperative diagnostic techniques, such as frozen section pathological examination and intraoperative magnetic resonance imaging, suffer from issues such as long duration, high cost, and complex operation. A rapid and accurate intraoperative auxiliary diagnostic method for glioma based on the differences in dielectric properties combined with machine learning is proposed in this study. Using an open-ended coaxial probe technique, the dielectric properties of 81 glioma tissue samples and 47 normal brain tissue samples from 14 patients were measured over a frequency range of 1 MHz-4 GHz. After feature selection and dimensionality reduction using the Lasso method, four machine learning models-Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN)-were used to classify the samples. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic curve (AUC value). The experimental results demonstrated that the dielectric properties of glioma tissues are higher than those of normal brain tissues (with an average increase of 22% in conductivity and 18% in relative permittivity). On the test set, the KNN model exhibited the highest classification accuracy (90%), while the ANN model showed the best AUC value (0.95). This study confirms that the rapid identification of glioma can be achieved based on dielectric properties combined with machine learning techniques, providing neurosurgeons with a novel auxiliary diagnostic technology for precise intraoperative margin detection of glioma.

The increased use of CT has raised concerns about patient radiation exposure. DRLs play a crucial role in optimising radiation dose while maintaining diagnostic quality. In Jordan, the absence of officially established national DRLs across a wide range of CT procedures may contributes to dose variability between healthcare facilities.

A prototype Compton camera composed of two high resolution scintillator detectors is presented in this work. The scatterer detector consists of a 21 × 21 gadolinium aluminum gallium garnet (GAGG) crystal array with a crystal size of 0.6 × 0.6 × 2 mm. The absorber detector consists of a 23 × 23 lutetium yttrium orthosilicate (LYSO) crystal array with a crystal size of 1.0 × 1.0 × 20 mm. A simple back-projection image reconstruction method was developed. The energy of the scatterer detector was accurately calibrated using the 55, 202, 307 keV gamma-rays from the LYSO natural background and the 511 keV gamma-ray from a Na point source. The scatterer detector provides a performance with all crystals clearly resolved even at an energy window of 30-120 keV and an average crystal energy resolution of 10.4% at 511 keV. The absorber detector provides a performance with all crystals clearly resolved, an average crystal depth of interaction resolution of ~ 2 mm and an average crystal energy resolution of 19.4% at 511 keV. An average spatial resolution of 2.5 mm was obtained and 9 point sources of 3 mm apart were well resolved at an image plane 7.5 mm from the front of the scatterer detector by using the 511 keV gamma-rays from a Na point sources. Furthermore, iterative reconstruction using the maximum-likelihood expectation maximization (MLEM) algorithm achieved a spatial resolution of ~ 1 mm at a plane 7.5 mm from the front of the scatterer detector. Compared with the simple back-projection method, the MLEM reconstruction significantly enhanced the image contrast and effectively suppressed the background artifacts.

This paper reviews the latest methods for creating control interfaces for intention detection in active transfemoral prosthetic devices. A literature review over the past two decades identified several control algorithms for intention detection. Sources included scientific publications, books, and online resources focusing on knee prostheses. Three main areas of research were identified. The studies were assessed using the Downs and Black checklist, detailing their control techniques and performance assessments. Initially, 213 studies were retrieved; 33 were selected for this review. Fifteen (15) papers examined control strategy frameworks and goal outputs of active prosthetic legs. Two (2) papers discussed conventional control methods for transfemoral prosthetic legs. Four (4) studies explored potential implementations of intention detection, and twelve (12) papers investigated machine learning algorithms for active prosthetic legs. The review suggests using a simpler sensory system paired with innovative control algorithms to translate limited sensor data into a broader set of relevant information. Effective sensory systems and intention detection algorithms are crucial for active transfemoral prosthetic limbs. This review presents the feasibility of control interfaces that enable intention detection for active prosthetic legs, offering multiple references and classifying different works in the field.