The examination of Alzheimer’s disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

Background: Computed Tomography (CT) is crucial in medical diagnosis, particularly for head examinations. Diagnostic Reference Levels (DRLs) are pivotal in balancing diagnostic efficacy with radiation safety. International organizations such as the International Atomic Energy Agency (IAEA) and the International Commission on Radiological Protection (ICRP) provide guidelines for establishing DRLs, emphasizing their importance in optimizing radiation doses. Aim: This study aimed to establish and standardize DRLs for head CT imaging in institutional and regional settings, emphasizing the need for tailored reference levels specific to each region’s practices and standards. Method: Data collection included patient demographics, imaging parameters, and radiation dose indices, namely volume-weighted CT dose index (CTDIvol) and dose-length product (DLP). Statistical analyses were conducted to determine the median and 3rd quartile values for establishing DRLs. The results were compared with national and international benchmarks to assess variations in radiation doses across regions. Results: Demographic profiles detailed gender distributions and ages across hospitals. Imaging parameters exhibited variability in tube voltage, milliampere-seconds (mAs), pitch, scan length, and field of view (FOV). For CTDIvol, the 3rd quartile value was 65.8 mGy (range: 24.8–85.9 mGy), and for DLP, it was 1230.95 mGy·cm (range: 382.3–1189.0 mGy·cm). These values were slightly higher than the national DRLs for Saudi Arabia in 2021 and other international benchmarks, underscoring the need for further optimization and alignment of protocols. Conclusion: Optimizing and standardizing DRLs for head CT imaging is crucial for effectively managing radiation doses while ensuring diagnostic accuracy. Comparison with national and international benchmarks highlighted the importance of tailoring reference levels to regional practices and standards, ensuring patient safety without compromising diagnostic efficacy.

Objective: To compare the diagnostic accuracy of radiology trainees, CT technicians, and radiologists in interpreting chest CT scans for the detection of COVID-19. Methods: Retrospective data were analyzed from 269 reading assessments of chest CT scans for COVID-19. These were conducted by 160 radiologists, 41 radiology residents, and 68 CT technicians from diverse countries and performed online using DetectedX software between March 30, 2020, and January 1, 2022. The readers examined and assessed realistic, de-identified, digital chest CT images—15 positive and 15 negative for COVID-19. Information about the readers' clinical roles (radiologist, radiology resident, or CT technician) was collected alongside measures of their performance in distinguishing between positive and negative cases (sensitivity, specificity, and ROC AUC). The data were evaluated with Kruskal–Wallis tests to identify any differences in performance between the roles. Post hoc comparisons using Bonferroni-corrected Dunn's tests were then used to identify any significant pairwise differences. Results: Statistically significant differences were observed in specificity (p < 0.001) and ROC AUC (p < 0.001) when comparing the three groups. In the pairwise comparisons, differences in specificity were only found between CT technicians and radiologists (p = 0.001), while ROC AUC was significantly different between CT technicians and radiologists (p = 0.001) and between CT technicians and radiology trainees (p = 0.016). No statistically significant differences were observed in sensitivity (p = 0.309) between the groups. Conclusions: The study identifies both diagnostic potential and areas for improvement for radiology residents and CT technicians. Notably, the performance of residents mirrored that of radiologists, underscoring their crucial role in early case management. However, while CT technicians demonstrated comparable sensitivity, their specificity and ROC AUC were lower, highlighting an opportunity for targeted training to enhance their ability to differentiate COVID-19 from other pathologies.