Browsing by Author "Abdel Wahed, Manal"

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    Deep Learning-Based Automated Detection and Grading of Papilledema From OCT Images: A Promising Approach for Improved Clinical Diagnosis and Management
    (John Wiley and Sons Inc, 2024-06) Salaheldin, Ahmed M; Abdel Wahed, Manal; Talaat, Manar; Saleh, Neven
    Papilledema is a prevalent neuro-ophthalmic condition characterized by optic disk swelling. It is known to pose a significant risk of vision loss in its advanced stages. To address the pressing need for accurate detection and grading of papilledema, this study introduces a novel approach utilizing optical coherence tomography (OCT) scans. A cascaded model that combines four transfer learning models—SqueezeNet, AlexNet, GoogleNet, and ResNet-50—for both the detection and grading phases was proposed. Additionally, a specialized convolutional neural network (CNN) model is meticulously designed to cater specifically to the complexities of papilledema analysis. Unlike the fundus camera-based models, this study integrates deep learning models for the diagnosis of papilledema from OCT scans. A new dataset of OCT scans was collected to ensure a comprehensive evaluation of the models. It encompasses a wide range of papilledema, pseudopapilledema, and normal cases. This dataset serves as a valuable resource for training and testing of the proposed models. In addition, two validation strategies have been adopted to ensure the model's generalizability and robustness. Furthermore, it enhances the model's accuracy and reliability. The results are highly promising; remarkable accuracy rates have been achieved. Specifically, the SqueezeNet, AlexNet, GoogleNet, ResNet-50, and customized CNN models achieved accuracy levels of 98.44%, 98.50%, 98.28%, 98.30%, and 96.26%, respectively, for the handout validation strategy. These findings not only demonstrate the efficacy of using deep learning in papilledema detection and grading but also establish the superiority of the proposed models when compared with other relevant studies. By addressing the challenges associated with papilledema, the study significantly contributes to the advancement of neuro-ophthalmic diagnostics. The accurate and efficient detection of papilledema from OCT scans holds immense potential for guiding timely interventions and preserving patients' visual health.
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    An evaluation of AI-based methods for papilledema detection in retinal fundus images
    (Elsevier BV, 2024-02) Salaheldin, Ahmed M; Abdel Wahed, Manal; Talaat, Manar; Saleh, Neven
    The complexities inherent in diagnosing papilledema, particularly within the realm of neuro-ophthalmology, emphasize the pressing need for sophisticated diagnostic methodologies. This study highlights the application of novel models tailored explicitly for papilledema detection, distinguishing it from pseudo-papilledema and normal cases, through the strategic utilization of deep learning frameworks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Leveraging hierarchical feature extraction from retinal images, the multi-paths CNN model successfully identifies crucial indicators of papilledema, while the cascaded model, integrating ResNet-50 and long short-term memory (LSTM), effectively captures sequential features. Leveraging a meticulously curated dataset comprising 18,258 fundus images, the presented models exhibit exceptional performance, with the multi-paths CNN achieving an accuracy of 99.97 %, and the LSTM model demonstrating an accuracy of 99.81 %. Comparative analysis showcases the unparalleled efficacy of the models, underscoring their potential in clinical diagnostics. Notably, they demonstrate robustness in occlusion sensitivity tests, highlighting their resilience in scenarios involving obscured image components. This pioneering study represents a significant milestone in papilledema detection, with the promise of advancing patient outcomes and streamlining healthcare practices. The proposed deep learning models not only offer precise diagnoses but also hold the potential to automate elements of the diagnostic workflow, alleviating the workload of healthcare professionals and enhancing overall patient care outcomes.