Search Results (323)

Background: Canine cranial cruciate ligament (CCL) rupture is a common orthopedic condition leading to stifle joint dysfunction, discomfort, and reduced mobility. Diagnosis typically involves radiography, computed tomography (CT), and magnetic resonance imaging (MRI). In this study, we conducted a retrospective analysis to evaluate the use of infrared thermography in assessing local temperature and thermal patterns in dogs with acute-onset lameness due to CCL rupture compared to those with intact ligaments. Methods: The study involved 12 dogs with cranial cruciate ligament rupture and nine dogs with intact ligaments. The stifle area of all dogs was clipped and scanned using a FLIR E50 thermographic camera. Two regions of interest (ROI), designated El1 and Bx1, were analyzed with FLIR Tools software 5.X by comparing the average of the maximum and of the mean temperature values between the two groups. Results: Thermal imaging revealed differences between the two groups of dogs, which were further supported by significantly higher temperatures in the El1 (lateral aspect of the stifle joint) and Bx1 (cranial aspect of the stifle joint) areas in the study group compared to the control group using a comparative analysis—two-sample t-test. In the El1 area, the study group showed a temperature increase of 1.8 °C compared to the control group, while in the Bx1 area, the difference was 1.76 °C. Conclusions: Infrared thermography shows potential to differentiate dogs with acute-onset lameness due to CCL rupture from dogs with intact ligaments, but further studies are needed to assess its accuracy in distinguishing it from other stifle pathologies. Full article

Water treadmill (WT) exercise is used for horses’ rehabilitation and training. Given that each training needs to be individualized for each horse, the goal is to assess whether infrared thermography (IRT) can serve as a non-invasive tool for daily monitoring of individual training and rehabilitation progress in horses undergoing WT exercise. Fifteen Polish Warmblood school horses were subjected to five WT sessions: dry treadmill, fetlock-depth water, fetlock-depth water with artificial river (AR), carpal-depth water, and carpal-depth water with AR. IRT images, collected pre- and post-exercise, were analyzed for the mean temperature (Tmean) and maximal temperature (Tmax) across 14 regions of interest (ROIs) representing the body surface overlying specific superficial muscles. While on a dry treadmill, Tmean and Tmax increased post-exercise in all ROIs; wetting of the hair coat limited surface temperature analysis in ROIs annotated on limbs. Tmax over the m. brachiocephalicus, m. trapezius pars cervicalis, m. triceps brachii, and m. semitendinosus increased during walking in carpal-depth water, which therefore may be suggested as an indirect indicator of increased activity related to forelimb protraction and flexion–extension of the limb joints. Tmax over the m. latissimus dorsi and m. longissimus increased during carpal-depth WT exercise with active AR mode, which may be suggested as an indicator of increased workload including vertical displacement of the trunk. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

Background/Objectives: The glymphatic system plays a crucial role in clearing brain metabolic waste, and its dysfunction has been correlated to various neurological disorders. The Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a non-invasive marker of glymphatic function by measuring diffusivity along perivascular spaces; however, its sensitivity to sleep-related changes in glymphatic activity has not yet been validated. This study aimed to evaluate the feasibility of using the DTI-ALPS index as a quantitative marker of dynamic glymphatic activity during sleep. Methods: Diffusion tensor imaging (DTI) data were obtained from 12 healthy male participants (age = 24.44 ± 2.5 years; Pittsburgh Sleep Quality Index (PSQI) < 5), once while awake and 16 times during sleep, following 24 h sleep deprivation and administration of 10 mg zolpidem. Simultaneous MR-compatible electroencephalography was used to determine whether the subject was asleep or awake. DTI preprocessing included eddy current correction and tensor fitting. The DTI-ALPS index was calculated from nine regions of interest in projection and association areas aligned to standard space. The final analysis included nine participants (age = 24.56 ± 2.74 years; PSQI < 5) who maintained a continuous sleep state for 1 h without awakening. Results: Among nine ROI pairs, three showed significant increases in the DTI-ALPS index during sleep compared to wakefulness (Friedman test; p = 0.027, 0.029, 0.034). These ROIs showed changes at 14, 19, and 25 min after sleep induction, with FDR-corrected p-values of 0.024, 0.018, and 0.018, respectively. Conclusions: This study demonstrated a statistically significant increase in the DTI-ALPS index within 30 min after sleep induction through time-series DTI analysis during wakefulness and sleep, supporting its potential as a biomarker reflecting glymphatic activity. Full article

Background: This study aimed at evaluating apparent diffusion coefficient (ADC) values of nasopharyngeal carcinoma (NPC) in the pre-treatment stages of NPC for establishing comparative quantitative parameters between children and adolescents compared to adults. Methods: A retrospective multicentric imaging study was conducted in three medical centers by collecting patient data over a 5-year timeframe. Patients were included in the study based on the following criteria: histopathologically proven carcinoma of the nasopharynx with all available medical records. The total sample included 20 patients (6 pediatric patients and 14 adults). A quantitative analysis of the ADC maps was performed. Two radiologists manually drew the region of interest (ROI) on ADC maps using the whole tumor on all magnetic resonance imaging (MRI) slices. The mean ADC was extracted for each patient and each radiologist’s evaluation. Differences in ADC values between pediatric and adult patients were evaluated using an independent samples t-test, with normality and variance assumptions tested via the Shapiro–Wilk and Levene’s tests, respectively. p-values less than 0.05 were considered statistically significant. Results: The mean ADC values extracted from the initial pre-treatment diffusion-weighted imaging (DWI) data from magnetic resonance imaging (MRI) in children were 712.22 × 10⁻⁶ mm²/s, compared to adults in whom the mean ADC values were 877.34 × 10⁻⁶ mm²/s. We found a statistically significant difference between the mean ADC values of pediatric patients and adult patients, t (17.44) = −3.15, p = 0.006, with the mean ADC values of pediatric patients (M = 712.22, standard deviation [SD] = 57.03) being lower, on average, than the mean ADC values of adult patients (M = 877.34, SD = 175.25). Conclusions: Our results showed significantly lower ADC values in pediatric patients than in adults, independent of tumor T-stage. Additionally, early-stage tumors, particularly in children, tended to exhibit even lower ADC values, suggesting potential biological distinctions across age groups. Full article

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes. Full article

The growing global demand for sustainable energy solutions has spurred interest in hybrid renewable energy systems, particularly those combining photovoltaic (PV) solar and wind power. This study records the technical and financial feasibility of establishing hybrid solar photovoltaic and wind power stations in Iraq, Al-Rutbah and Al-Nasiriya, with a total power of 60 MW for each, focusing on optimizing energy output and cost-efficiency. The analysis evaluates key technical factors, such as resource availability, system design, and integration challenges, alongside financial considerations, including capital costs, operational expenses, and return on investment (ROI). Using the RETScreen program, the research explores potential locations and configurations for maximizing energy production and minimizing costs, and the evaluation is performed through the calculation Internal Rate of Return (IRR) on equity (%), the Simple Payback (year), the Net Present Value (NPV), and the Annual Life Cycle Savings (ALCSs). The results show that both PV and wind technologies demonstrate significant energy export potential, with PV plants exporting slightly more electricity than their wind counterparts. Al Nasiriya Wind had the highest output, indicating favorable wind conditions or better system performance at that site. The results show that the analysis of the proposed hybrid system has a standardizing effect on emissions, reducing variability and environmental impact regardless of location. The results demonstrate that solar PV is significantly more financially favorable in terms of capital recovery time at both sites, and that financial incentives, especially grants, are essential to improve project attractiveness, particularly for wind power. The analysis underscores the superior financial viability of solar PV projects in both regions. It highlights the critical role of financial support, particularly capital grants, in turning renewable energy investments into economically attractive opportunities. Full article

Objectives: A diagnostic indicator for differentiating peritonsillar abscess (PTA) from peritonsillar cellulitis (PTC) has not been established. Our aim was to define radiological criteria for differentiating PTA from PTC. Methods: We retrospectively extracted low-density areas around the tonsils of PTA and PTC cases from contrast-enhanced CT images between 2021 and 2024. PTA cases were identified as those in which drainage by puncture or incision was observed, while PTC cases were those in which drainage was not observed. A total of 138 cases were finally analyzed (PTA, 111 cases; PTC, 27 cases). The CT attenuation value of a low-density area relative to that of the area surrounding the low-density area was used as the relative CT number, and relative CT numbers were compared between PTA and PTC cases. Using univariate and multivariate analyses, we identified factors that had diagnostic value for differentiating between PTA and PTC. Results: Relative CT numbers for PTA were significantly lower than those for PTC (p < 0.001). The univariate logistic regression analysis showed relative CT number, low-density ROI (region of interest), and surrounding ROI as having predictive value for differentiating PTA from PTC. In multivariate logistic regression analysis, only the relative CT number had predictive value for distinguishing PTA from PTC (odds ratio, 2.28), with a relative CT number of <0.46 being significantly associated with PTA. Conclusions: Low relative CT numbers could potentially be used to identify PTA, and their measurement could provide a diagnostic marker for the accurate diagnosis of abscess formation. Full article

Objective: Endocrine disorders, including diabetes mellitus and thyroid dysfunctions, can significantly impact bone metabolism and structure. This study aimed to assess mandibular trabecular architecture in patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), hyperthyroidism, and hypothyroidism using fractal dimension (FD) and histogram analyses (HA), comparing the findings with a healthy control group. Methods: This retrospective study analyzed panoramic radiographs from 200 individuals, comprising 40 patients in each of the four endocrine disorder groups and 40 healthy controls. Fractal dimension and histogram-based pixel intensity analyses were conducted using ImageJ™ (version 1.53) software. Four standardized regions of interest (ROI) were evaluated on the right mandible, and statistical comparisons were conducted across groups using one-way analysis of variance (ANOVA), t-test, Mann–Whitney U, and Spearman correlation analyses. Results: Age and gender distributions did not differ significantly between groups. FD analysis revealed a significant reduction at ROI1 in the hyperthyroidism group compared to controls (p = 0.018); however, no other significant FD differences were observed among the remaining groups or ROIs. A significant positive correlation was found between FD and histogram values at ROI1 and ROI2 (p < 0.001), while pixel intensity values did not differ significantly across groups in any ROI. Conclusion: Although no significant differences were found in diabetic groups, the decreased FD in hyperthyroid patients suggests that FD analysis may be a useful non-invasive method to detect subtle bone alterations. Further research with larger sample sizes and comprehensive biochemical analyses are needed to confirm these findings. Full article

Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients. Methods: From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index. Results: The study included 126 BC patients with predominantly “mass”-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10–8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33–56.98]). Radiomic and clinical–radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (p < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical–radiomic model. Conclusions: Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone. Full article

Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics. Full article

Background/Objectives: Accurate scientific methods are essential for monitoring the osseointegration of dental implants postoperatively. This study aims to evaluate peri-implant bone changes using the fractal analysis (FA) method during follow-up. Methods: Periapical radiographs were obtained from 77 patients with dental implants, and 33 permanent teeth serving as a control group, retrieved from the radiology archive. Radiographs were taken using the parallel technique at 3, 6, and 12 months post-surgery. All images were digitized and saved in TIFF. Each image was aligned using the TurboReg plugin in ImageJ software. Regions of interest (ROIs) were selected from the mesial and distal aspects of the implants, then prepared for fractal analysis. FA was performed to assess changes in bone structure over time. Results: In the study group, radiographs of 24 patients for 0, 3 and 6 month, radiographs of 34 patients for 0, 6 and 12 month, radiographs of 8 patients for 0 and 12 month, radiographs of 5 patients for 0 and 3 month, radiographs of 5 patients for 0 and 6 month, and 1 patient of 0, 3, 6 and 12 month of radiographs were used in the study. There were no statistically significant differences in FA values over time when analyzed by gender and age in both the study and control groups. However, a statistically significant difference was observed in FA value changes over time and jaws. Conclusions: The study indicates a positive correlation between bone remodeling over time and FA results, likely due to the restoration of masticatory forces in the implant area. Image analysis on two-dimensional dental radiographs can be a useful tool for detecting changes in bone density. Fractal analysis is a cost-effective and practical diagnostic method for monitoring bone changes over time. Full article

Objectives: Differentiation of adrenal incidentalomas (AIs) remains a challenge in the oncological setting. The aim of the study was to explore the diagnostic efficacy of [18F]Fluorodeoxyglucose (FDG) positron emission tomography combined with computed tomography (PET/CT)-based radiomics in identifying adrenal metastases and to compare it with that of conventional PET/CT parameters. Materials: Retrospective analysis was performed on 195 AIs for model construction, nomogram drawing, and internal validation. An additional 30 AIs were collected for external validation of the radiomics model and nomogram. Logistic regression analysis was employed to build models based on clinical and PET/CT routine parameters. The open-source software Python (version 3.7.11) was utilized to process the regions of interest (ROI) delineated by ITK-SNAP, extracting radiomic features. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied for feature selection. Based on the selected features, the optimal model was chosen from ten machine learning algorithms, and the nomogram was constructed. Results: The area under the curve (AUC), sensitivity, specificity, and accuracy of conventional parameters of PET/CT were 0.919, 0.849, 0.892, and 0.844, respectively. XGBoost demonstrated superior diagnostic efficiency among the radiomics models, outperforming those constructed using independent predictors. The AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of XGBoost’s internal and external validation were 0.945, 0.932, 0.930, 0.960, 0.970, 0.890 and 0.910, 0.900, 0.860, 1, 1, 0.750. The accuracy, sensitivity, specificity, PPV, and NPV of the nomogram in external validation were 0.870, 0.952, 0.667, 0.870, and 0.857. Conclusions: The radiomics model and conventional PET/CT parameters both showed high diagnostic performance (AUC p > 0.05) in discriminating adrenal metastases from benign lesions, offering a practical, non-invasive approach for clinical assessment. Full article

Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While fully supervised deep learning methods have demonstrated remarkable performance in this domain, their reliance on large-scale, pixel-level annotated datasets—a significant label scarcity challenge—severely hinders their widespread deployment in clinical settings. Addressing this limitation, this review focuses on non-fully supervised learning paradigms, systematically investigating the application of semi-supervised, weakly supervised, and unsupervised learning techniques for medical image segmentation. We delve into the theoretical foundations, core advantages, typical application scenarios, and representative algorithmic implementations associated with each paradigm. Furthermore, this paper compiles and critically reviews commonly utilized benchmark datasets within the field. Finally, we discuss future research directions and challenges, offering insights for advancing the field and reducing dependence on extensive annotation. Full article

Laser vision sensors for weld seam extraction face critical challenges due to arc light and spatter interference in welding environments. This paper presents a real-time weld seam extraction method. The proposed framework enhances robustness through the sequential processing of historical frame data. First, an initial noise-free laser stripe image of the weld seam is acquired prior to arc ignition, from which the laser stripe region and slope characteristics are extracted. Subsequently, during welding, a dynamic region of interest (ROI) is generated for the current frame based on the preceding frame, effectively suppressing spatter and arc interference. Within the ROI, adaptive Otsu thresholding segmentation and morphological filtering are applied to isolate the laser stripe. An optimized RANSAC algorithm, incorporating slope constraints derived from historical frames, is then employed to achieve robust laser stripe fitting. The geometric center coordinates of the weld seam are derived through the rigorous analysis of the optimized laser stripe profile. Experimental results from various types of weld seam extraction validated the accuracy and real-time performance of the proposed method. Full article