Search Results (11,145)

Cyclin-Dependent Kinase-Like 5 (CDKL5) Deficient Disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy characterized by early-onset refractory epilepsy, severe neurodevelopmental impairment, and lifelong disability. Although more than thirty anti-seizure medications are available, most CDD patients remain pharmaco-resistant. Gene-based therapies are emerging, but therapeutic development is hindered by marked clinical heterogeneity, small patient populations, and the lack of robust, translatable brain-based biomarkers for clinical trials. Genetically engineered Cdkl5 mouse models recapitulate many cognitive, behavioral, and molecular features of CDD, yet their utility is limited by the absence of overt seizures, precluding seizure-based outcome measures. Here, we establish high-definition brain network (HDBN) biomarkers using advanced diffusion MRI tractography combined with graph-theoretical analysis to quantify whole-brain network organization in Cdkl5 knockout mice. Diffusion MRI enables non-invasive mapping of axonal connectivity by leveraging anisotropic water diffusion, while high-angular-resolution acquisition overcomes key limitations of conventional diffusion tensor imaging in regions with complex fiber architecture. We demonstrate that Cdkl5 knockout mice exhibit reproducible and region-specific disruptions in brain network organization, prominently affecting the somatosensory and somatomotor cortex, hippocampus, hypothalamus, amygdala, and superior colliculus—regions implicated in cognition, learning and memory, homeostasis, anxiety, and visual–motor function. In contrast, networks within the entorhinal cortex remain largely preserved. These findings identify HDBN metrics as sensitive, non-invasive biomarkers that capture clinically relevant circuit-level abnormalities in CDD. Because diffusion MRI–based network analyses are directly translatable across species, HDBN biomarkers provide a unified framework for therapeutic evaluation in mouse models, large animals, and human clinical trials, enabling longitudinal monitoring of disease progression and treatment response. Full article

Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We evaluated dose behavior in adult chest CT, derived BMI-stratified local DRLs, and developed models to enable AI-assisted, prescan dose prediction. Methods: Consecutive adult chest CT examinations from a single center were analyzed. Dose indices (CTDIvol, DLP) and patient factors (BMI, weight, height, age, sex; scan length and planned technical parameters where available) were extracted. DRLs were defined as the 75th percentile overall and within BMI categories (underweight, normal, overweight, and obese). Group differences were assessed using non-parametric tests; associations were examined using correlation analysis. Supervised learning (e.g., Random Forest, Gradient Boosting) was trained to predict CTDIvol and DLP from routinely available variables. Results: BMI-stratified DRLs increased monotonically with habitus: underweight 444.95 mGy·cm/9.60 mGy; normal 513.00/11.55; overweight 756.08/14.65; obese 931.60/20.25 (DLP/CTDIvol). Differences across BMI groups were significant for DLP (H = 31.53, p < 0.001) and CTDIvol (H = 33.61, p < 0.001). DLP correlated moderately with weight and BMI (r ≈ 0.54–0.56, p < 0.001), with a weaker association for age; height was not a meaningful predictor. No sex-based differences in CTDIvol or DLP were observed. Predictive models estimated CTDIvol and DLP with high performance (R² up to ~0.79 and ~0.77, respectively), enabling comparison of predicted dose against BMI-matched DRLs before acquisition. Conclusions: Size-aware, BMI-stratified DRLs provide clinically interpretable investigation levels that avoid pitfalls of pooled benchmarks. Coupled with robust prediction of individualized dose from routine variables, this framework supports a shift from retrospective audit to prospective, point-of-care dose governance and protocol optimization in chest CT. Full article

Objectives: Management of unstable pelvic fractures during pregnancy presents a major therapeutic challenge, requiring careful multidisciplinary evaluation to balance maternal benefits and fetal radiation risks. Methods: We report the case of a 32-year-old patient who presented with a pelvic fracture due to a road traffic accident at three months of pregnancy. A left sacroiliac osteosynthesis was performed to treat a left sacroiliac diastasis with pelvic osteosynthesis using a trans-iliosacral approach under cone-beam CT (CBCT) guidance using a very-low-dose protocol. Radiation parameters and fetal dose estimates were calculated in advance in collaboration with a medical physicist. Tight beam collimation, a reduced field of view, and minimization of fluoroscopic checks were applied to keep fetal exposure as low as reasonably achievable. This article aims to demonstrate the feasibility of managing a complex pelvic fracture using interventional radiology and to review the literature on management options and gestational age-dependent fetal risks. Results: The estimated cumulative fetal dose from initial imaging, open surgery, and CBCT-guided osteosynthesis remained below 70 mGy using a pregnant phantom (Duke Organ Dose–Dosewatch–General Electric system), which is below thresholds associated with deterministic effects. The procedure achieved optimal screw positioning with less than 40 s of fluoroscopy. Maternal postoperative recovery was favorable, and follow-up revealed normal fetal development. Conclusions: This case demonstrates that CBCT-guided percutaneous iliosacral screw fixation can be safely performed during pregnancy with meticulous planning, dose-reduction strategies, and multidisciplinary collaboration, maintaining fetal radiation exposure below accepted safety thresholds. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Background: The risk stratification of unruptured intracranial aneurysms (UIAs) relies largely on static clinical and morphological parameters, which may not fully capture aneurysm-specific wall behavior. ECG-gated four-dimensional computed tomography angiography (4D-CTA) enables the time-resolved assessment of aneurysm wall motion, but reliable interpretation requires the differentiation of biological motion from measurement uncertainty. Methods: In this prospective exploratory pilot study, ECG-gated 4D-CTA was used to evaluate the longitudinal aneurysm size change, global volumetric pulsation (GVP), spatial wall pulsation (SWP), intrinsic wall deformability and variability. Size change and pulsation were defined using predefined resolution- and noise-based thresholds. Spatial wall motion was assessed using phase-resolved three-dimensional displacement maps. Harmonic modeling isolated periodic pulsation, and residual variability exceeding empirically derived uncertainty limits was conservatively interpreted as deformability. Associations with aneurysm growth and ELAPSS scores were analyzed using exploratory statistics. Results: Eleven UIAs in ten patients were followed for 4.3 ± 1.1 years. A longitudinal size change occurred in six aneurysms (54.5%). Baseline GVP was present in eight aneurysms (73%) and SWP in nine (82%). GVP was not associated with a size change (p = 1.00). All aneurysms with a size change exhibited baseline SWP, whereas no size change was observed in aneurysms without SWP; however, this association did not reach statistical significance in this small exploratory cohort (p = 0.18). Conservative variability metrics were not associated with growth but correlated with baseline shape irregularity, particularly the undulation index (Spearman’s ρ up to ~0.90). Conclusions: In this small exploratory pilot cohort, spatial wall pulsation showed a descriptive directional pattern with longitudinal aneurysm size changes, whereas global volumetric pulsation did not. These findings are preliminary, should be interpreted cautiously, and require confirmation in larger, adequately powered longitudinal studies before clinical application. Full article

Background: Acute type A aortic dissection (ATAAD) is a life-threatening condition that may be complicated by malperfusion, particularly in patients with prior aortic interventions such as Thoracic Endovascular Aortic Repair (TEVAR). Management becomes increasingly complex when the dissection involves supra-aortic branches and compromises previously placed stents. Methods: We report the case of a 58-year-old male presenting with ATAAD and left lower limb paralysis, with a history of prior TEVAR. Imaging demonstrated an entry tear in the ascending aorta with extension into the distal left main and supra-aortic branches, resulting in a dissection flap obstructing the proximal end of the TEVAR stent. The patient underwent emergency surgical intervention including a Bentall procedure, coronary artery bypass grafting (CABG), and deployment of a small Ascyrus Medical Dissection Stent (AMDS) distally within the TEVAR stent. Pre-operatively, the patient had severe lower limb ischemia due to near-complete obstruction of distal flow. Results: Following surgical intervention, there was restoration of true lumen perfusion with resolution of malperfusion. The patient was successfully weaned from cardiopulmonary bypass, extubated on post-operative day 4, and discharged on day 7 with stable hemodynamics and intact bilateral lower limb perfusion. Post-operative computed tomography (CT) demonstrated a well-seated AMDS with no evidence of ongoing false lumen perfusion. At 30-day follow-up, there was no clinical or biochemical evidence of organ malperfusion. Conclusions: The use of an AMDS deployed within a pre-existing TEVAR stent may represent an effective strategy for managing complex ATAAD with malperfusion, particularly in cases requiring combined surgical interventions. Full article

Background: Grounded in Self-Determination Theory (SDT) and the Dualistic Model (DM) of Passion, this study examined the motivational mechanisms underlying psychological well-being among Tunisian physical education teachers. The objectives were twofold: to examine validity evidence of the Arabic version of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) and to test an integrative structural model linking harmonious passion, need satisfaction and frustration, well-being, vitality, happiness, and perceived stress. Methods: A representative sample of physical education teachers (1238) completed standardized instruments to assess passion, basic psychological needs, and well-being. To conduct exploratory and confirmatory factor analyses, the group was randomly divided into two independent subgroups. Reliability and validity were assessed using additional psychometric indicators, and a structural equation model was used to test the hypothesized relationships. Results: The results support the multidimensional structure and psychometric validity of the scale in the Tunisian context. Harmonious passion appears to be a positive factor in the satisfaction of psychological needs and a negative factor in cases of frustration. The satisfaction of these needs is closely linked to a high level of well-being, whereas their dissatisfaction is associated with adverse consequences. Well-being is also associated with increased vitality, greater happiness, and reduced stress, reflecting adaptive psychological functioning. Conclusions: Harmonious passion and basic psychological need satisfaction emerge as central resources for sustaining teacher well-being, vitality, and resilience against stress in educational contexts. Full article

Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this systematic review, PubMed and Scopus were searched for articles published from 2022 to March 2025. Authors independently identified eligible studies based on predefined inclusion criteria and extracted data. The study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist. Results: Thirty-one studies met the inclusion criteria from 310 records, with eight addressing both segmentation and classification. Most segmentation studies used publicly available multiparametric MRI datasets. Performance varied by architecture and tumor region, with whole-tumor segmentation achieving the highest Dice Similarity Coefficient (DSC). Classical U-Nets reported DSC values ranging 80–87%, while models with residual or attention mechanisms exceeded 90%. Classification focused on tumor type and glioma grading, using features learned from multiparametric MRI. Reported accuracy ranged from 91.3% to 99.4%, with sensitivity and specificity often above 95%. However, variability across tumor subregions, limited external validation, reliance on public datasets, and heterogeneous preprocessing raise concerns about robustness and real-world generalizability. Evidence on the use of explainability methods for both tasks remains limited. Conclusions: DL models for glioma segmentation and classification demonstrate promising performance. However, standardized validation protocols, multi-center datasets, and the integration of explainable artificial intelligence techniques are needed to improve transparency, robustness, and clinical applicability. Full article

Explainable Artificial Intelligence (XAI) has become a critical requirement in medical image analysis, where transparency and interpretability are essential for clinical trust and decision support. Melanoma is recognized as one of the most deadly types of skin cancer, with its occurrence exhibiting an increasing pattern in recent times. However, detecting this cancer in its initial stages greatly increases patients’ chances of long-term survival. Various computer-based techniques have recently been proposed to diagnose skin lesions at their early stages. Even though the machine learning community has achieved a certain degree of success, there is still an unresolved research challenge regarding high error margins and the limited interpretability of automated systems. This study focuses on addressing both segmentation and classification tasks, with particular emphasis on two key concepts: (1) improving image quality to maximize distinguishability between foreground and background regions, thereby enhancing visual interpretability and segmentation accuracy and (2) eliminating redundant and cluttered feature information to generate the most discriminative and compact feature representations. The input images are initially processed using a novel metaheuristic contrast-stretching method to estimate image-specific key parameters, thereby enhancing lesion boundary clarity in a clinically interpretable manner. Following this, the improved images are fed into selected pre-trained deep models, including DenseNet-201, Inception-ResNet v2, and NASNet-Mobile. The extracted features from all pre-trained models are fused to produce resultant vectors, which are then refined using a bio-inspired feature selection method, termed entropy-controlled whale optimization, to retain only the most informative attributes. The selected discriminative feature set is subsequently classified using multiple classifiers. The results indicate that the proposed framework achieves superior performance compared to existing methods in terms of accuracy, sensitivity, specificity, and F1-score. Additionally, it facilitates a more explainable, transparent, and structured diagnostic pipeline appropriate for medical applications. Full article

Low-dose contrast-enhanced computed tomography (CT) is widely used to reduce contrast-induced toxicity, but reduced iodine concentration and inconsistent acquisition conditions often produce uneven contrast attenuation and spatial misalignment between scans. In this context, we define dose-up translation as the computational process of synthetically enhancing low-dose contrast images to approximate the visual and diagnostic quality of full-dose acquisitions. These factors limit the effective use of routinely acquired imaging data for dose-up translation, particularly in veterinary abdominal CT where respiratory motion and postural variability further degrade anatomical correspondence. We present a weakly aligned enhancement framework designed to operate under spatial misalignment and limited paired data. Registration-based pseudo-references are constructed using a hybrid strategy that combines deformable anatomical alignment with feature-level correspondence. Dose-up translation is performed using structure-preserving translation with multi-scale consistency and edge-aware regularization to maintain anatomical boundaries. To address limited low-dose datasets, a two-stage knowledge transfer strategy transfers anatomical and contrast priors from abundant pre-contrast data. Quantitative evaluation demonstrated region-level contrast-to-noise ratio improvements of up to 31.5% (e.g., from 5.55 to 8.38 in the caudal vena cava (CVC), P < 0.05) compared with baseline enhancement methods across 1171 test slices. Experiments demonstrate consistent improvements in structural fidelity, distributional realism, and region-level vascular conspicuity compared with paired, unpaired, and synthetic-pairing baselines. These findings suggest that the dose-up translation of low-enhanced CT is better formulated as a weakly aligned domain adaptation problem rather than a strictly paired reconstruction task, enabling practical image translation under realistic clinical acquisition variability. Full article

Background: The steady rise of prostate cancer in Saudi Arabia signals a critical public health shift that requires immediate investment in early detection and prevention to mitigate a future clinical crisis. Accurate diagnosis using multiparametric MRI and PI-RADS scoring remains challenging, as interpretations are highly experience-dependent and subspecialized radiologists are limited. Methods: To address this gap, this study introduces a novel Dual-Stream Attention Network designed to automate the classification of low-risk (PIRADS 2-3) versus high-risk (PIRADS 4-5) lesions from T2-weighted MRI. Leveraging a ResNet50 backbone, the architecture employs parallel streams for Local and Global Feature Processing, each enhanced by a Channel-Spatial Attention module to highlight diagnostically relevant regions. These features are integrated through a Cross-Stream Fusion mechanism and a gate-controlled Adaptive Feature Fusion module to optimize multi-scale information. The model was developed and validated on a regional dataset of 3850 images from Jazan Specialist Hospital and Prince Mohammed bin Naser Hospital. This research provides a standardized, high-precision diagnostic path tailored to the Saudi Arabian population, conducted under institutional review board approval (No. 25138). Results: The proposed dual-stream attention network achieved an accuracy of 97.8% on the validation set and 96.4% on the test set, demonstrating high performance and generalization capabilities in classifying prostate lesions from Saudi patient populations. Conclusions: The proposed dual-stream architecture with novel attention and fusion mechanisms demonstrates high effectiveness for prostate cancer classification from T2-weighted MRI in Saudi clinical settings. This represents the first deep learning model specifically trained and validated on Saudi Arabian prostate MRI data, with the potential to address the shortage of specialized expertise and improve diagnostic efficiency in the Kingdom. Full article

Diabetic retinopathy (DR) is a leading cause of vision impairment and permanent blindness worldwide, requiring accurate and automated systems for multi-grade severity classification. However, standard Convolutional Neural Networks (CNNs) often struggle to capture fine, high-frequency microvascular patterns critical for diagnosis. This study proposes a Robust Intelligent CNN Model (RICNN) that integrates Gabor-based feature extraction with deep learning to improve DR classification. Specifically, Gabor filters are applied during preprocessing to extract orientation- and frequency-sensitive texture features, which are transformed into feature maps and concatenated with CNN feature representations at the fully connected layer (feature-level fusion). The model also incorporates the Synthetic Minority Oversampling Technique (SMOTE) for data balancing and the Adam optimizer for efficient convergence. This integration enhances sensitivity to microvascular structures such as microaneurysms and hemorrhages. The proposed RICNN was evaluated on the Messidor dataset (1200 images) across four severity levels: Mild, Moderate, Severe, and Proliferative DR. The model achieved an accuracy of 89%, a precision of 88.75%, a recall of 89%, and an F1-score of 89%, with AUCs of 97% for Severe DR and 99% for Proliferative DR. Comparative analysis confirms that the proposed texture-aware Gabor enhancement significantly outperforms LBP and Color Histogram approaches, indicating its potential for reliable clinical decision support. Full article

Background: Myocardial infarction with nonobstructive coronary arteries (MINOCA) is a heterogenous clinical syndrome in which aetiologies range from “true” ischemic mechanisms to non-ischemic mimics (e.g., myocarditis and Takotsubo syndrome). Cardiac magnetic resonance (CMR) plays a central role in the diagnostic pathway. Recent advances in parametric mapping (native T1, T2, and extracellular volume ECV) and evolving AI/radiomic methods promise to further improve diagnostic accuracy and prognostic stratification. This review aims to evaluate the current CMR evidence in MINOCA, while highlighting parametric mapping advances and future directions in the sphere of AI and radiomics. Methods: A systematic literature search of PubMed and the Directory of Open Access Journals (DOAJ) was performed. We included original prospective and retrospective CMR studies of MINOCA and MINOCA-like presentations in adults. Data were extracted into a master dataset and synthetised thematically into five subsections: (1) diagnostic yield, (2) reclassification rate), (3) timing of CMR, (4) prognosis, and (5) future directions. Results: Twenty-two studies met the inclusion criteria. CMR diagnostic yield varied by protocol and timing but was consistently substantial. CMR consistently reclassified initial MINOCA diagnoses (ischemia or alternative non-ischemic diagnoses). Parametric mapping provided incremental diagnostic and prognostic information. Across studies, early imaging (ideally within the first 1–2 weeks) increased diagnostic yield, while delayed CMR reduced detectability of transient lesions. Early AI and radiomics work show promise for LGE-based classification and for predicting post-contrast findings from non-contrast data, but current models require larger, multicentre training and robust external validation. Conclusions: CMR increases diagnostic yield and reclassification rates in MINOCA, particularly when performed early and with standardised T1/T2/ECV mapping. Mapping not only improves detection of inflammatory and diffuse injuries but also contributes to prognostic stratification. High-resolution LGE, OCT, and AI/radiomics are promising future refinements but need prospective validation in large, early, mapping-inclusive cohorts. Full article

Accurate liver segmentation in laparoscopic surgery is critical but remains challenging due to low contrast, occlusion, and irregular organ boundaries. While deep learning has advanced medical image segmentation, existing models often trade off between accuracy, computational efficiency, and boundary precision. We propose IMAU-Net, a hybrid architecture integrating a pre-trained InceptionV3 encoder with a novel bottleneck combining Multi-Core Pooling (MCP) and enhanced Atrous Spatial Pyramid Pooling (ASPP). The MCP module captures fine-to-medium spatial details through parallel multi-kernel pooling, while ASPP extracts multi-scale contextual information via dilated convolutions. Evaluated on the M2CAI dataset with 5-fold cross-validation, IMAU-Net achieves a mean Dice coefficient of 0.9179 ± 0.012 and IoU of 0.8483 ± 0.015. Furthermore, external validation on the independent CholecSeg8K dataset (250 test samples) demonstrates generalizability across different laparoscopic procedures, achieving a Dice coefficient of 0.8745 ± 0.0312 and AUC of 0.9542, with a performance degradation of only 4.3% despite domain shift between liver surgery and cholecystectomy. Comparative analysis with state of the art methods demonstrates superior performance, with computational efficiency suitable for real-time applications (45 FPS, 42.3 M parameters). The proposed architecture provides an optimal balance between accuracy and efficiency for intraoperative guidance systems. While evaluated on retrospective laparoscopic image datasets rather than real-time intraoperative workflows, the model demonstrates potential for integration into surgical guidance systems pending prospective validation. Full article

Orbital apex syndrome (OAS) is characterized by optic neuropathy and ophthalmoplegia and is generally associated with poor visual prognosis. The aim of this study was to describe patients with acute OAS who demonstrated substantial recovery of visual function and ocular motility. We retrospectively reviewed the medical records of patients treated for OAS at a tertiary medical center between 2019 and 2024 whose condition ultimately proved reversible. Data on demographics, clinical findings, imaging, management, and follow-up were collected. Six patients (three female, three male; age range 14–87 years) were included and followed for a median follow-up of 7 months (range 2–31). All presented with reduced vision and ophthalmoplegia of varying severity. Underlying etiologies included inflammatory disease (n = 2), lymphoma, infection, blunt trauma, and post-surgical OAS of undetermined etiology (n = 1 each). Treatment was directed at the underlying cause. Visual acuity ranged from 20/30 to hand motion (HM) at presentation and 20/15 to 20/60 at the final visit. Improvement in vision and ocular motility occurred after a median time to clinical improvement of 2.37 months (range 0.25–5 months). Near-complete recovery of ocular motility was observed in all patients, with only one retaining mild abduction limitation. These findings highlight a subset of OAS cases with favorable outcomes and emphasize the importance of early diagnosis and etiology-directed management. Full article