Radiographers play a pivotal role in healthcare but face significant occupational health challenges, including work-related musculoskeletal disorders (WRMSDs), burnout, fatigue, and sleep disturbances. These challenges, influenced by high workloads and inadequate ergonomics, compromise their well-being and job performance. This study aims to assess the prevalence of these health outcomes and evaluate the role of workplace factors in mitigating or exacerbating these issues. A systematic review and meta-analysis was conducted per PRISMA 2020 guidelines. Literature searches across PubMed, Scopus, Web of Science, and Embase included quantitative studies published from 2014 to 2024. Data on prevalence, outcomes, and measurement tools were extracted, and a random-effects model was used to estimate pooled prevalence. Risk of bias was assessed with the ROBINS-E tool. From 1080 records, 14 studies met inclusion criteria. WRMSDs were reported by 85 % (95 % CI: 74-95) of radiographers, with the neck (73 %) and lower back (67 %) being the most affected regions. Burnout prevalence included emotional exhaustion at 29.8 % (95 % CI: 19.8-39.8). Chronic fatigue and sleep disturbances were strongly linked to extended shifts and poor ergonomics. Protective factors included professional recognition and workplace ergonomic interventions. Radiographers are disproportionately affected by physical and psychological health risks. Addressing these through enhanced workplace ergonomics, stress management, and workload balancing is imperative to improving their quality of life and job performance. Implementing ergonomic training, fostering professional recognition, and reducing workloads are crucial for sustainable radiography practices. Policy reforms and workplace interventions can significantly improve radiographers' health, ensuring better job satisfaction and retention.

Diffusion-weighted MR Imaging is a rapidly emerging technique, that allows in-vivo mapping processes of the water diffusion in tissues. It has the potential capabilities for clinical application in breast imaging. The aim of this study was to find out the optimal b-value for calculation of ADC value for differential diagnosis of breast lesions. A total of 124 subjects (mean age 46 years) with 141 lesions were included. The protocol consists of axial T2 sequence for lesion localization and measurement and DW sequence with three sets of b-values of 0, 300, 600, and 1000 s/mm². The mean ADC values of the breast lesions for b-values (0, 300, 600, and 1000) were 1.75 ± 0.18 × 10⁻³mm²/sec, 1.66 ± 0.12 × 10⁻³mm²/sec and 1.57 ± 0.15 × 10⁻³mm²/sec for the benign lesions and 1.26 ± 0.048 × 10⁻³mm²/sec, 1.14 ± 0.11 × 10⁻³mm²/sec and 0.93 ± 0.14 × 10⁻³mm²/sec for malignant lesions respectively. Statistical significant differences were noted on the ADC value of benign and malignant lesions among the three sets of b values (p = 0.001). ADC values of malignant lesion was significantly lower compared to benign lesions. The AUC (0.998) was substantially large for b-value of 0,600 s/mm² with a threshold ADC cut off value of 1.28 × 10⁻³mm²/sec with 98.4% sensitivity, 93.2% specificity and 98.5% positive predictive value(PPV). In conclusion, diffusion weighted imaging has the ability for differential diagnosis of breast lesions with the optimal b value of 0,600 s/ mm². DWI is a reliable tool for characterising breast lesions and may increase the overall specificity of breast MRI.

Background Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the inferior alveolar nerve and third molar. Usually, canal tacking is performed manually, which takes a lot of time. This study aimed to develop an artificial intelligence (AI) model to automate classification of the mandibular canal in relation to the third molar. Methods This retrospective study was conducted using 434 CBCT images. 3D slicer software was used to annotate and classify the data into lingual, buccal, and inferior categories. Two convolution neural network models, AlexNet and ResNet50, were developed to classify this relationship. The study included 262 images for training and 172 images for testing, with the model performance evaluated by sensitivity, precision, and F1 score. Results The performance of the two models was evaluated using a 3 × 3 confusion matrix, with the data categorized into 3 clases: lingual, buccal, and inferior. The mandibular canal and third molar have a close anatomical relationship, highlighting the need for precise imaging in dental and surgical settings. To accurately classify the mandibular canal in relation to the third molar, both AlexNet and ResNet50 demonstrated high accuracy, with F1 scores ranging from 0.64 to 0.92 for different classes, with accuracy of 81% and 83%, respectively, for accurately classifying the mandibular canal in relation to the third molar. Conclusion The present study developed and evaluated AI models to accurately classify and establish the relationship between the mandibular canal and third molars using CBCT images with a higher accuracy rate.

T2DM is associated with neurodegenerative changes that can be detected through advanced magnetic resonance imaging. Brain atrophy in T2DM is linked to cognitive decline, yet the extent and pattern of structural brain changes remain underexplored. This study aims to assess volumetric differences in brain structures between diabetic and non-diabetic individuals using voxel-based morphometry (VBM). A prospective observational study was conducted with 86 participants (43 T2DM case and 43 healthy controls) who underwent high-resolution 3D MRI scans using 3D SPGR and T1-weighted Fast Spin Echo sequences. Image preprocessing and volumetric analysis were performed using Statistical Parametric Mapping (SPM-12) and the Computational Anatomy Toolbox (CAT-12). Brain volumes were analyzed for 24 regions. Statistical analyses were conducted using independent t-tests and linear regression, with p < 0.05 considered significant. T2DM subjects exhibited significant gray matter (GM) volume reductions compared to controls, particularly in the hippocampus and middle frontal gyrus, as detected in both 3DSPGR and T1 FSE sequences. However, regional differences emerged between imaging modalities: while T1 FSE imaging revealed significant bilateral hippocampal atrophy, 3DSPGR data showed no such difference. Notably, both sequences demonstrated significantly increased volumes in the anterior and posterior temporal lobes in T2DM participants, suggesting possible region-specific hypertrophy. T2DM is associated with significant brain atrophy, particularly in brain regions associated with cognition. Based on these findings, MRI-based volumetric analysis has the potential to detect and monitor T2DM-related neurodegeneration early, emphasizing the need for routine neuroimaging in diabetic populations. Research on longitudinal assessments will be necessary in the future to gain a deeper understanding of the progression of brain atrophy in diabetics.

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related deaths worldwide. The clinical utility of artificial intelligence (AI) and radiomics models based on contrast-enhanced computed tomography (CECT) for HCC detection remains underexplored. This systematic review and meta-analysis evaluated the diagnostic performance of machine learning and radiomics-based models. A comprehensive literature search (2015-2025) across four major databases identified 29 eligible studies according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias was assessed using the QUADAS-2 tool. Of these, 17 studies provided independent performance metrics and were included in the meta-analysis. Pooled estimates of sensitivity, specificity, accuracy, and the F1 score were calculated using a random effects model. Models evaluated on independent test datasets, the pooled sensitivity and specificity were 0.562 and 0.570, respectively. A subgroup of radiomics-specific models (n=3) demonstrated comparatively higher sensitivity (0.660) and specificity (0.670), with minimal heterogeneity in sensitivity (I² = 0%, τ² ≈ 0), suggesting strong internal consistency. However, due to the limited number of studies, these results should be interpreted with caution. Combined models showed lower sensitivity (0.368) and specificity (0.297), with high heterogeneity (I² = 98.3%), likely due to the absence of independent test sets, which may have led to limited generalisability. Radiomics-based models evaluated on independent datasets exhibit more consistent diagnostic accuracy for HCC detection using CECT. However, methodological variability underscores the need for standardised reporting, robust external validation, and incorporation of explainable AI techniques to enhance clinical adoption.

Purpose: Medical technology has gone a long way in diagnosis and characterization of breast tumors. Diffusion-weighted MR imaging is the state of the art for breast screening and diagnosing. The aim of this meta-analysis is to evaluate the diagnostic performances of diffusion-weighted MR imaging in characterization of breast lesions with different b value in 1.5 T MRI. Method: An extensive search on Scopus, Embase, and PubMed databases were performed on studies published between January 2000 and 2020. The systematic seek initially yielded 2467 studies, out of which 27 research were covered on this meta-evaluation. The included studies for meta-analysis utilized different b value and noted that the ADC value was highly influenced by the b value, for differential diagnosis of breast tumors. Results: The current meta-analysis has shown the ADC values was lower for malignant breast lesions as compared with benign lesions. The recommended mean threshold ADC was 1.25 ± 0.17 × 10^–3 mm²/s range from 0.93 to 1.60 × 10^–3 mm²/s for differential diagnosis of breast tumors. Sub-group analysis on the bases of b value showed statistically significant differences in the ADC value of benign and malignant breast tumors. Conclusion: In conclusion, we noted that b value has a significant effect in calculating the ADC value of the breast lesions as well as ADC threshold value but lacks standardization. The ADC value measurement has a potential for differentiation between benign and malignant breast lesions.