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/Objectives: Accurate segmentation of brain stroke lesions from MRI images is a critical task in medical image analysis that is essential for timely diagnosis and treatment planning. Methods: This paper presents a novel approach for segmenting brain stroke lesions using a deep learning model based on the U-Net neural network architecture. We enhanced the traditional U-Net by integrating a transformer-based backbone, specifically the Mix Vision Transformer (MiT), and compared its performance against other commonly used backbones such as ResNet and EfficientNet. Additionally, we implemented a 2.5D method, which leverages 2D networks to process three-dimensional data slices, effectively balancing the rich spatial context of 3D methods and the simplicity of 2D methods. The 2.5D approach captures inter-slice dependencies, leading to improved lesion delineation without the computational complexity of full 3D models. Utilizing the 2015 ISLES dataset, which includes MRI images and corresponding lesion masks for 20 patients, we conducted our experiments with 4-fold cross-validation to ensure robustness and reliability. To evaluate the effectiveness of our method, we conducted comparative experiments with several state-of-the-art (SOTA) segmentation models, including CNN-based UNet, nnU-Net, TransUNet, and SwinUNet. Results: Our proposed model outperformed all competing methods in terms of Dice Coefficient and Intersection over Union (IoU), demonstrating its robustness and superiority. Our extensive experiments demonstrate that the proposed U-Net with the MiT Backbone, combined with 2.5D data preparation, achieves superior performance metrics, specifically achieving DICE and IoU scores of 0.8153 ± 0.0101 and 0.7835 ± 0.0079, respectively, outperforming other backbone configurations. Conclusions: These results indicate that the integration of transformer-based backbones and 2.5D techniques offers a significant advancement in the accurate segmentation of brain stroke lesions, paving the way for more reliable and efficient diagnostic tools in clinical settings.

Background: Age-related Macular Degeneration (AMD) is a leading cause of irreversible vision loss, particularly in the elderly. Optical Coherence Tomography (OCT), a noninvasive imaging modality, is widely used for retinal disease detection. However, the limited availability of labeled OCT datasets poses a significant challenge, making semi-supervised learning a promising approach. This study introduces a novel Iterative Teacher-Student (ITS) framework, which refines pseudo-labeling strategies to improve AMD detection accuracy, particularly in low-data scenarios. Methods: Initially, an optimal supervised model based on EfficientNet was developed to classify AMD using a dataset from Noor Eye Hospital, consisting of 16,822 OCT images. The dataset size was then progressively reduced to 70%, 50%, 20%, and 5% to evaluate model performance under data scarcity. Unlike conventional semi-supervised learning approaches, our ITS framework iteratively refines pseudo-labels, ensuring more reliable knowledge transfer from teacher to student models. Results: The optimized supervised model achieved 87.14% accuracy in AMD classification. As dataset size decreased to 20% and 5%, accuracy declined to 77.05% and 54.78%, respectively. Implementing the ITS framework improved accuracy to 88.56% at 20% and 64.15% at 5%, outperforming traditional semi-supervised methods. Conclusions: This study highlights the potential of semi-supervised learning, particularly our iterative teacher-student approach, to enhance AMD detection when labeled OCT data are scarce. The proposed framework introduces a novel iterative refinement strategy, which can serve as a foundation for future research in retinal disease diagnosis with limited labeled datasets.

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior–posterior (AP) radiography image. We introduce an encoder–decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss, especially in elderly, that can be diagnosed from OCT images. OCT, a noninvasive imaging modality, is not only widely applied in detecting retinal diseases but meaningful correlations have also been found between OCT images and neurological disorders like Alzheimer disease. The insufficient labeled dataset is the key challenge in using OCT images for disease detection, highlighting the importance of employing semi-supervised methods to address this issue. In this paper, the first step is to investigate the optimal structure of a supervised model for detecting AMD with the model being based on EfficientNet. In subsequent steps, the dataset size is reduced to 70%, 50%, 20%, and 5% of the total dataset to identify the best model based on an iterative teacher-student approach for detecting AMD disorders. In this study, the available OCT dataset gathered at Noor Eye Hospital consisting of 16,822 retinal OCT images are utilized. The optimized supervised model achieved 87.14% accuracy in distinguishing different AMD stages. As the dataset size is reduced to the most severe conditions (i.e., 20% and 5%), an expected decrease in accuracy to 77.05% and 54.78% has observed. Introduced semi-supervised learning based on iterative teacher-student model improved the accuracy to 88.56% at 20% and 64.15% at 5% volume of dataset, achieving high confidence levels, thereby enhancing the performance of the supervised model. This study introduces a framework that can be used in future studies to detect diseases with an insufficient OCT dataset, aiming to improve model accuracy.