Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model’s accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.

Background: Hypoxia plays a critical role in lung cancer progression and treatment resistance by contributing to aggressive tumor behavior and poor therapeutic response. Molecular imaging, particularly positron emission tomography (PET), has become an essential tool for noninvasive hypoxia detection, providing valuable insights into tumor biology and aiding in personalized treatment strategies. Objective: This narrative review explores recent advancements in PET imaging for detecting hypoxia in lung cancer, with a focus on the development, characteristics, and clinical applications of various radiotracers. Findings: Numerous PET-based hypoxia radiotracers have been investigated, each with distinct pharmacokinetics and imaging capabilities. Established tracers such as ¹⁸F-Fluoromisonidazole (¹⁸F-FMISO) remain widely used, while newer alternatives like ¹⁸F-Fluoroazomycin Arabinoside (¹⁸F-FAZA) and ¹⁸F-Flortanidazole (¹⁸F-HX4) demonstrate improved clearance and image contrast. Additionally, ⁶⁴Cu-ATSM has gained attention for its rapid tumor uptake and hypoxia selectivity. The integration of PET with hybrid imaging modalities, such as PET/CT and PET/MRI, enhances the spatial resolution and functional interpretation, making hypoxia imaging a promising approach for guiding radiotherapy, chemotherapy, and targeted therapies. Conclusions: PET imaging of hypoxia offers significant potential in lung cancer diagnosis, treatment planning, and therapeutic response assessment. However, challenges remain, including tracer specificity, quantification variability, and standardization of imaging protocols. Future research should focus on developing next-generation radiotracers with enhanced specificity, optimizing imaging methodologies, and leveraging multimodal approaches to improve clinical utility and patient outcomes.

Background: Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is critical for accurate diagnosis, but segmentation issues can pose significant challenges, leading to diagnostic difficulties. Machine learning (ML) algorithms have demonstrated superior performance in addressing segmentation problems. Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. Data were collected from 1100 patients who underwent MPI studies at Al-Jahra Hospital between 2015 and 2024. To train the U-Net model, 100 studies were segmented by nuclear medicine (NM) experts to create a ground truth (gold-standard coordinates). The dataset was divided into a training set (n = 100 images) and a validation set (n = 900 images). The performance of the U-Net model was evaluated using multiple cross-validation metrics, including accuracy, precision, intersection over union (IOU), recall, and F1 score. Result: A dataset of 4560 images and corresponding masks was generated. Both holdout and k-fold (k = 5) validation strategies were applied, utilizing cross-entropy and Dice score as evaluation metrics. The best results were achieved with the holdout split and cross-entropy loss function, yielding a test accuracy of 98.9%, a test IOU of 89.6%, and a test Dice coefficient of 94%. The k-fold validation scenario provided a more balanced true positive and false positive rate. The U-Net segmentation results were comparable to those produced by expert nuclear medicine technologists, with no significant difference (p = 0.1). Conclusions: The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data.

Background: Globally, community pharmacists worldwide have prescribed more over-the-counter systemic antibiotics, posing significant issues for international organizations tackling antibiotic-resistant bacteria, a major global threat, due to the accessibility in pharmacies. Objectives: This study aimed to examine the Jordanian pharmacists’ antibiotic selection and over-the-counter antibiotic prescriptions. Methods: A total of 244 community pharmacists participated in an online standardized survey, which examined five essential domains including sources and trust of pharmacy antibiotic prescription information, category and frequency of permitted antimicrobials, prescription-free antimicrobials, interactions, antimicrobial prescription issues for pharmacists, and pharmacy staff’s knowledge of non-prescription antimicrobial questions and answers. Results: This study found that 1—pharmacists are confident in prescribing antibiotics and they use various tools to improve their skills. 2—Antibiotics were the most sought antimicrobials without a prescription, followed by antifungals and antivirals. 3—Throat, urinary tract, chest, and otitis media are the most common infections that require antibiotics. Pharmacists prescribe penicillin for 75% of throat infections, Fluoroquinolones for 48.2% of urinary tract infections, and cephalosporins for 35.9%. Macrolides are the most prevalent otitis media treatment (43.2%). 4—Some people obtain antimicrobial prescriptions without a valid reason or diagnosis. 5—Many pharmacists (171/244, 70%) agree or strongly believe that antibiotic prescription information is difficult to obtain. 6—Many pharmacists (183/200, 91.5%) aimed to educate patients on the risks and correct use of antimicrobials without prescriptions. Conclusions: These results show that Jordanian community pharmacists follow clinical antibiotic prescribing guidelines and conduct antimicrobial stewardship, yet they demand antimicrobials without prescriptions and lack decision support tools. Antibiotic classes address most diseases, and pharmacists emphasize antimicrobial misuse.

Background: Patients with breast cancer undergoing biological therapy and/or chemotherapy perform multiple radionuclide angiography (RNA) or multigated acquisition (MUGA) scans to assess cardiotoxicity. The association between RNA imaging parameters and left ventricular (LV) ejection fraction (LVEF) remains unclear. Objectives: This study aimed to extract and evaluate the association of several novel imaging biomarkers to detect changes in LVEF in patients with breast cancer undergoing chemotherapy. Methods: We developed and optimized a novel set of MATLAB routines called the “RNA Toolbox” to extract parameters from RNA images. The code was optimized using various statistical tests (e.g., ANOVA, Bland-Altman, and intraclass correlation tests). We quantitatively analyzed the images to determine the association between these parameters using regression models and receiver operating characteristic (ROC) curves. Results: The code was reproducible and showed good agreement with validated clinical software for the parameters extracted from both packages. The regression model and ROC results were statistically significant in predicting LVEF (R² = 0.40, P < 0.001) (AUC = 0.78). Some time-based, shape-based, and count-based parameters were significantly associated with post-chemotherapy LVEF (β = 0.09, P < 0.001), LVEF of phase image (β = 4, P = 0.030), approximate entropy (ApEn) (β = 11.6, P = 0.001), ApEn (diastolic and systolic) (β = 39, P = 0.002) and LV systole size (β = 0.03, P = 0.010). Conclusions: Despite the limited sample size, we observed evidence of associations between several parameters and LVEF. We believe that these parameters will be more beneficial than the current methods for patients undergoing cardiotoxic chemotherapy. Moreover, this approach can aid physicians in evaluating subclinical cardiac changes during chemotherapy, and in understanding the potential benefits of cardioprotective drugs.