Nader A. Rahman Mohamed

Retinal diseases represent a major cause of visual impairment worldwide, with conditions such as age-related macular degeneration, diabetic macular edema, retinal vein occlusion, and retinal artery occlusion affecting large patient populations. Delayed diagnosis increases the risk of permanent vision loss. Optical coherence tomography supports early detection through high resolution visualization of retinal structures, yet interpretation requires expert clinicians and increases workload in large screening programs. Automated analysis systems support efficient clinical decision making. This study evaluates twenty different deep learning models, including traditional convolutional neural network and nineteen pretrained models for automated classification of retinal diseases using OCT images. The dataset includes 3,678 clinically verified scans collected from Prince Sattam bin Abdulaziz University Hospital and covers ten retinal disease categories. Two experienced retinal specialists performed independent labeling and verification. Transfer learning supported adaptation of pretrained models to the clinical dataset. Performance evaluation used accuracy, precision, recall (sensitivity), and F1 score. Results show clear variation across architectures. DarkNet53 achieved the highest performance with 95.93 percent accuracy and an F1 score of 0.955. ResNet50 and DarkNet19 followed with accuracy above 94 percent. DenseNet201 and VGG models also demonstrated strong classification capability. Lightweight models such as MobileNetV2 and ShuffleNet achieved moderate performance with reduced computational cost. Misclassification occurred mainly between clinically similar conditions, including Age-Related Macular Degeneration with Macular Neovascularization and with suspected Macular Neovascularization, as well as retinal vein occlusion and diabetic macular edema. Statistical analysis using ANOVA and Friedman ranking confirmed significant differences between models and identified DarkNet53 as the most consistent architecture. The findings support the use of deep learning models for automated OCT interpretation and highlight suitable architectures for clinical retinal disease screening systems.

Diabetic Retinopathy (DR) is a serious complication of diabetes and a major cause of vision loss. The damage it causes to the retina is permanent, which makes early detection and accurate grading essential to prevent blindness. This study evaluates the performance of two deep learning models using two different optimizers for automatic screening and severity grading of DR from retinal fundus images. The models compared are a Convolutional Neural Network (CNN) and a Residual Network (ResNet-50), trained with Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM). Two public datasets, APTOS and EyePACS, were used. Images were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and data augmentation for class balance. Performance was evaluated using accuracy, area under the curve (AUC), F1-score, recall, and precision. ResNet-50 with the ADAM optimizer achieved the best results, with an accuracy of 80.71% and an AUC of 0.9539 on the APTOS dataset. These results show that ResNet-50 with ADAM is an effective model for automated DR screening and can support early detection and prevention of diabetes-related vision loss.

This study investigates the AI-assisted analyses of radiation disinfection effects on the mechanical properties of recycled date kernel powder–epoxy composites for medical applications, utilizing Euclidean distances and the k-nearest neighbor (KNN) algorithm. Tensile and compression tests were conducted on twenty specimens following ASTM standards, with the data analyzed using a t-test to evaluate the impact of the UVC disinfection process on the material’s mechanical properties. The application of AI through the KNN algorithm successfully identified the three most representative curves out of five for both tensile and compression tests. This targeted curve selection minimized variability and focused on the most relevant data, enhancing the reliability of the analysis. Following the application of UVC and AI, tensile tests showed a 20–30% increase in ultimate stress. Similarly, compression tests revealed a 25% increase in transition stress, an 18–22% improvement in ultimate stress, and approximately a 12% rise in fracture stress. This research underscores the potential of combining AI, sustainable materials, and UVC technology to develop advanced composites for medical applications. The proposed methodology offers a robust framework for evaluating material performance while promoting the creation of eco-friendly, high-performance materials that meet the stringent standards of medical use.

This study addresses the high cost of traditional dental impression materials by introducing a novel composite material reinforced with wheat bran powder, aiming to reduce expenses while maintaining suitable mechanical performance. Tensile and compression test specimens were prepared according to the ASTM D412 and ASTM D575 standards, respectively, to evaluate the mechanical properties of the pure elastomer and the wheat-bran-reinforced composite. Comparative t-tests were conducted to analyze the tensile and compression strengths of both materials, focusing on their cost-effectiveness and suitability for dental applications. The results demonstrate that the wheat-bran-reinforced composite exhibits compression strength (105 MPa) comparable to that of the pure elastomer while offering controlled deformation and enhanced stiffness under compression. Although the composite shows reduced tensile strength (7 MPa vs. 11 MPa), its performance remains adequate for applications requiring moderate tensile properties. Notably, the new material reduces costs by approximately 50%, making it an economical and sustainable alternative for dental impression materials. This innovation aligns with sustainable practices by incorporating natural fibers and offers dentists a cost-effective solution without compromising on performance.

According to the World Health Organization, the Global Mental Health Report estimated that between 251 and 310 million individuals worldwide experienced depression during the first year of the COVID-19 pandemic. Most methods for detecting depression rely on clinical diagnoses and surveys. However, the American Psychiatric Association reports that over 50% of patients do not receive appropriate treatment. This study aims to utilize machine learning and pupil diameter features to identify depression and evaluate the accuracy of these classifiers in comparison to our previous deep learning model. While limited research has explored the use of pupillary diameter as a classification tool for distinguishing between individuals with and without depression, several studies have focused on EEG signals for this purpose. The study involved 58 participants, with 29 classified as depressed and 29 as healthy. The classification was based on statistical features extracted from the Hilbert-Huang Transform. Results showed a significant improvement in average accuracy compared to the authors’ prior work, with the current study achieving 77.72% accuracy, compared to 64.78% in their previous research. Machine learning methods, particularly Bagging, outperformed deep learning models such as AlexNet when classifying data from the left and right eyes individually (90.91% vs. 78.57% for the left eye; 90.91% vs. 71.43% for the right eye). However, when combining data from both eyes, deep learning using AlexNet demonstrated superior performance (98.28% accuracy compared to 93.75% using Bagging with statistical features from both eyes). Despite the higher accuracy of deep learning, machine learning is recommended for its faster execution times.

Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was employed to distinguish between individuals diagnosed with depression and those who are considered mentally healthy. Pupillometric recordings were collected from a total of 58 individuals, comprising 29 healthy individuals and 29 individuals diagnosed with depression. Pupil size was recorded every 4 ms. The performance of three pretrained convolutional neural networks (GoogLeNet, SqueezeNet, and AlexNet) was evaluated for depression classification using the pupil size data. The highest accuracy of 98.28% was obtained with AlexNet. This finding highlights the potential of utilizing pupil diameter as a reliable indicator for objectively measuring depression.

Objectives: The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. Methods: To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. Results: The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. Conclusions: The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality of life for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

This study explores the impact of disinfection techniques on the mechanical properties of poly(lactic acid) (PLA), a crucial material in the production of medical implants, tissue engineering, orthopedic devices and drug delivery systems, owing to its biocompatibility and ease of manufacturing. The focus is on evaluating the effectiveness of ultraviolet (UV) type C (254 nm wavelength) and the combined use of type C and B (310 nm wavelength) disinfection methods. Fifteen tensile test specimens (ASTM D638) and fifteen compression test specimens (ASTM D695) were utilized to assess PLA’s mechanical properties, including yield strength, ultimate strength, and fracture strength. The investigation involved subjecting the specimens to the specified disinfection methods and evaluating these properties both before and after the disinfection process. In the tensile test, a statistically significant difference (p = 0) in yield displacement was observed among the three groups. Additionally, a notable difference (p = 0.047) in fracture displacement was identified between the untreated group and the UVC and UVB combination group. No discernible impact on yield or fracture forces was noted. In the compression test, there was a significant difference (p = 0.04) in yield displacement and a clear difference (p = 0.05) in fracture force between the untreated group and the UVC and UVB combination group. The hybrid combination of UVC and UVB disinfection techniques did not affect yield force in both tensile and compression tests. However, it demonstrated a clear impact on displacement, suggesting its potential as a promising disinfection technique in the medical field.

Magnetic polymer composites have recently attracted considerable interest, primarily because of their promising applications, especially in the biomedical industry. The aim of this study is to investigate the impact of ultraviolet C (UVC) irradiation as a disinfection method on the mechanical characteristics of composite polymer magnets. Tensile and compression tests were conducted following the standards set by ASTM D3039 and ASTM D3410, respectively. In addition, energy dispersive spectroscopy (EDS) was used to determine the effect of the disinfection method on the amount of carbon, oxygen, and iron within the surface of the composite polymer magnet material. The UVC’s irradiation impact was statistically assessed by a t-test. The results of the tensile tests demonstrated a significant increase in the transition force, measuring 0.41 kN and 0.58 kN before and after UVC exposure, respectively. Similarly, the outcomes of the compression tests showed a notable increase in yield force, registering 4.9 kN and 6 kN before and after UVC treatment. This suggests that the composite magnetic material has gained a higher capacity to withstand compressive loads than tensile loads. Finally, the EDS analysis revealed the carbon mass percentage was 71.69% prior to UVC radiation exposure, with it increasing to 78.56%, following exposure. This suggests that the composite material exhibited improved hardness. These findings highlight that UVC irradiation has a beneficial impact on both the mechanical and chemical properties of the composite magnet material, which support its use as a disinfection method in clinical settings.