Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDI_vol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDI_vol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDI_vol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes.

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.

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.

Background: Artificial intelligence (AI) techniques are increasingly applied to magnetic resonance imaging (MRI) for detecting temporomandibular joint (TMJ) anomalies; however, their overall diagnostic accuracy and generalizability remain uncertain. Objectives: To systematically review and meta-analyse the diagnostic performance of AI models for TMJ anomaly detection on MRI and to identify factors influencing model performance. Methods: A comprehensive search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published between January 2015 and September 2025. Two reviewers independently screened and extracted data. Eligible studies developed and tested AI, machine learning, or deep learning models on human TMJ MRI and reported quantitative performance metrics. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model, while pooled accuracy was derived using logit transformation. Heterogeneity ( I ² ) was explored through subgroup analyses by model architecture and validation strategy. Results: Fourteen studies were included in the systematic review, of which six met the criteria for meta-analysis. Across these six studies, 18 models were analyzed for accuracy, 29 for sensitivity, and 24 for specificity. The pooled diagnostic accuracy was 0.487 (95% CI 0.403–0.571), with pooled sensitivity and specificity of 0.399 (95% CI 0.348–0.450) and 0.399 (95% CI 0.343–0.456), respectively, all showing substantial heterogeneity ( I ² > 90%). Subgroup analyses indicated that advanced architectures such as ResNet-18, Inception v3, and EfficientNet-b4 achieved higher and more consistent diagnostic performance. Conclusions: Advanced deep learning architectures such as ResNet-18, Inception v3, and EfficientNet-b4 demonstrated superior diagnostic performance for detecting temporomandibular joint anomalies on MRI. These findings highlight the potential of AI-assisted MRI interpretation to improve diagnostic consistency, efficiency, and early detection of TMJ pathology. However, substantial heterogeneity and limited external validation currently limit clinical translation. Standardized multicenter studies and transparent model validation are essential to ensure reliable integration of AI tools into clinical TMJ imaging workflows.