Ahmed Soliman

JBHI Journal 2024 Journal Article

A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography

• Mohamed Elsharkawy
• Ahmed Sharafeldeen
• Fahmi Khalifa
• Ahmed Soliman
• Ahmed Elnakib
• Mohammed Ghazal
• Ashraf Sewelam
• Aristomenis Thanos

We propose an automated, explainable artificial intelligence (xAI) system for age-related macular degeneration (AMD) diagnosis. Mimicking the physician's perceptions, the proposed xAI system is capable of deriving clinically meaningful features from optical coherence tomography (OCT) B-scan images to differentiate between a normal retina, different grades of AMD (early, intermediate, geographic atrophy (GA), inactive wet or active neovascular disease [exudative or wet AMD]), and non-AMD diseases. Particularly, we extract retinal OCT-based clinical imaging markers that are correlated with the progression of AMD, which include: (i) subretinal tissue, sub-retinal pigment epithelial tissue, intraretinal fluid, subretinal fluid, and choroidal hypertransmission detection using a DeepLabV3+ network; (ii) detection of merged retina layers using a novel convolutional neural network model; (iii) drusen detection based on 2D curvature analysis; (iv) estimation of retinal layers' thickness, and first-order and higher-order reflectivity features. Those clinical features are used to grade a retinal OCT in a hierarchical decision tree process. The first step looks for severe disruption of retinal layers' indicative of advanced AMD. These cases are analyzed further to diagnose GA, inactive wet AMD, active wet AMD, and non-AMD diseases. Less severe cases are analyzed using a different pipeline to identify OCT with AMD-specific pathology, which is graded as intermediate-stage or early-stage AMD. The remainder is classified as either being a normal retina or having other non-AMD pathology. The proposed system in the multi-way classification task, evaluated on 1285 OCT images, achieved 90. 82% accuracy. These promising results demonstrated the capability to automatically distinguish between normal eyes and all AMD grades in addition to non-AMD diseases.

JBHI Journal 2024 Journal Article

Guest Editorial Advanced Machine Learning and Artificial Intelligence Tools for Computational Biology: Methodologies and Challenges

• Fahmi Khalifa
• Imran Razzak
• Mohammad Amjad Kamal
• Ahmed Soliman

In recent years, the management and analysis of biological data have experienced exponential growth propelled by the relentless advancement of machine learning (ML) and artificial intelligence (AI) technologies. This is driven mainly by the remarkable ability and potentials of AI-based systems to craft sophisticated, yet effective, algorithms and analytical models tailored for the interpretation of biological information; thus, assist in making accurate predictions and/or decisions [1]. The surge in AI adoption is not unfounded; it's a response to the overwhelming increase in both the volume and acquisition rates of biological data.

YNICL Journal 2020 Journal Article

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

• Heba Kandil
• Ahmed Soliman
• Fatma Taher
• Mohammed Ghazal
• Ashraf Khalil
• Guruprasad Giridharan
• Robert Keynton
• J. Richard Jennings

JBHI Journal 2016 Journal Article

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

• Amir Alansary
• Marwa Ismail
• Ahmed Soliman
• Fahmi Khalifa
• Matthew Nitzken
• Ahmed Elnakib
• Mahmoud Mostapha
• Austin Black

In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.