BACKGROUND: Globally prostate cancer is one of the most prevalent cancer among men and a leading cause of morbidity and mortality, especially in a developing country, which is mainly due to lack of knowledge and awareness regarding the screening of prostate cancer. The main objective of this review and meta-analysis is to evaluate the knowledge, awareness and practice of adult men about prostate cancer. METHOD: An extensive literature search was performed on studies published between January 2000 to 2021. The systematic review initially yielded 137 studies, out of which 7 studies were covered on this meta-evaluation. RESULT: We noted that the pooled estimate of knowledge and awareness were respectively 65% [CI: 29%, 100%], and 74% [CI: 66%, 82%] about prostate cancer. However, there were limited practices noted in screening of prostate cancer. CONCLUSION: In order to increase the awareness and screening practice rate for prostate cancer, an improved health education is highly recommended.

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.

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.

Artificial intelligence (AI)-driven image reconstruction has shown potential for enhancing image quality in CT angiography while reducing radiation exposure. However, reproducibility and methodological transparency are essential for real-world adoption. To compare image quality and radiation dose of cerebral CT angiography reconstructed with conventional iterative reconstruction (IR) and AI-based Precise Imaging (PI) at 100 kVp using a reduced Dose Right Index (DRI-15). In this prospective single-center study, 68 patients underwent 100 kVp cerebral CT angiography with a 50% reduced Dose Right Index (DRI-15 vs. DRI-36). Identical raw datasets were reconstructed using IR (iDose⁴) and PI. Quantitative analysis included attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), while two blinded radiologists independently performed qualitative assessment using a 5-point Likert scale. Radiation dose metrics (CTDIvol, DLP, effective dose) were recorded. Standardized acquisition protocols, fixed contrast injection, and consistent ROI placement ensured reproducibility. PI reconstructions yielded significantly lower noise and higher SNR and CNR than IR (all p < 0.001; r = 0.61-0.71), with no difference in vascular attenuation (p = 0.118). Qualitative scores were superior for PI across all criteria (p < 0.01), while inter-rater agreement was substantial for both methods, slightly higher with IR. The median effective dose was 0.785 mSv (IQR 0.71-0.90). AI-based PI reconstruction enhances image quality without increasing radiation dose or altering vascular attenuation, consistently outperforming IR and supporting its use in low-dose cerebral CT angiography, especially in dose-sensitive patients.

Background Cerebral computed tomography angiography (CTA) is widely used to assess neurovascular disorders, but venous contamination often obscures arteries. Optimizing bolus-tracking thresholds is crucial, yet patient factors influencing contrast dynamics and the value of radiation dose indices in head CTA remain unclear. Objectives To optimize bolus-tracking thresholds in cerebral CTA by examining patient-related influences on enhancement and radiation metrics. Methods 126 adults undergoing cerebral CTA were evaluated in this prospective study. Demographics, physiologic parameters, peak enhancement time (PET), peak enhancement attenuation (PEA), and dose indices (CTDIvol, SSDE) were recorded. Linear regression identified predictors of enhancement. Two blinded radiologists graded venous contamination. ROC analysis, including age subgroups, determined the optimal HU threshold. Results Median age was 55.5 years; 70% were male. PET rose with age (+0.086 s/year, p < 0.001) and was shorter in females (–2.39 s, p = 0.003). PEA increased with threshold (+1.03 HU/unit, p < 0.001). Arterial enhancement was higher in females (+40.7 HU, p < 0.001) and patients ≥60 years (+70 HU, p < 0.001). Venous enhancement correlated with PET (p = 0.023) and systolic pressure (p = 0.002). ROC analysis showed an optimal threshold of 105 ± 5 HU (AUC = 0.634; sensitivity 88.4%, specificity 77.1%). CTDIvol, but not SSDE, correlated with weight (p = 0.015). Conclusion Intrinsic (age, gender) and extrinsic (threshold) factors shape CTA enhancement. A 105 ± 5 HU threshold reduces venous contamination, especially in younger patients. CTDIvol remains the preferred dose index. Findings support individualized, resource-efficient CTA protocols aligned with UN SDGs 3, 9, and 12.

Background Diabetic peripheral neuropathy (DPN) is associated with progressive skeletal muscle degeneration, particularly in distal lower limb muscles. Advanced imaging modalities such as CT and MRI can detect these changes; however, their routine clinical use is limited. This study aimed to evaluate the utility of quantitative musculoskeletal ultrasound for assessing lower limb muscle morphology in individuals with type 2 diabetes with and without DPN. Methods In this cross-sectional study, 215 participants were included and categorized into three groups: T2DM with DPN (n = 129), T2DM without DPN (n = 43), and healthy controls (n = 43). DPN was assessed using the 10-g monofilament test and vibration perception threshold (VPT). Ultrasound measurements of the muscle thickness and cross-sectional area (CSA) were obtained for proximal and distal lower limb muscles using a standardized protocol. Group comparisons were performed using the Kruskal–Wallis test with DSCF post-hoc analysis. Associations were assessed using Spearman’s correlation. Results Quantitative ultrasound revealed that participants with DPN demonstrated significantly reduced muscle thickness and CSA across most lower limb muscles compared with both T2DM without DPN and controls (p < 0.001), with the largest effect sizes observed in distal muscles, including tibialis anterior, extensor hallucis longus, extensor digitorum brevis, and abductor hallucis brevis. Ultrasound-derived muscle measurements showed significant correlations with neuropathy severity score and age. Conclusions Quantitative musculoskeletal ultrasound can be used as a feasible and sensitive imaging modality for detecting diabetes-related muscle changes in the lower limb muscles enabling early detection and monitoring of neuromuscular involvement in type 2 diabetes.