Prof. Sumaya Al-Maadeed and her research team have just won two Best Poster Awards at the Qatar University Annual Research Forum & Exhibition 2023. The awards were received in Information and Communications Technologies discipline for Undergraduate Students and Faculty and Postdoc categories. Congratulation!

Category: Undergraduate Studeents

Project’s title: “Enhancing Ultrasound Intima-Media Complex (IMC) Segmentation: Leveraging four Deep Learning Models and Self-ONN integration”.

Project’s team: Hanadi Hassen, Omar Elharrouss, Najmath Ottakath, Somaya Al Maadeed, Mohammed E.H. Chowdhury, Ahmed Bouridane, Susu Zughaier

Project’s abstract: Carotid intima-media thickness (CIMT) is a commonly used indicator for atherosclerosis, typically evaluated through carotid ultrasound images. The utilization of advanced deep learning techniques for the analysis, segmentation, and CIMT measurement in these images remains relatively unexplored. This research aims to assess the effectiveness of four recent deep learning models, including a convolutional neural network (CNN), a self-organizing operational neural network (self-ONN), a transformer-based network, and a pixel difference convolution-based network. The evaluation is conducted on segmenting the intima-media complex (IMC) using the CUBS dataset, which comprises ultrasound images from both sides of the neck of 1088 participants. The findings reveal that the self-ONN model surpasses the traditional CNN-based approach, while the pixel difference and transformer-based models exhibit the most robust performance in segmentation.

Category: Faculty and Postdoc

Project’s title: “Automated device for detection and grading of carotid artery plaque deposit”.

Project’s team: Fatma Al-Mannai, Maryam Al-Kuwari, Ala El-Bardini, Najmath Ottakath, Somaya Al-Maadeed

Project’s abstract: The escalating global incidence of lifestyle-related risk factors, including conditions such as diabetes and elevated cholesterol levels has heightened the prevalence of cardiovascular diseases and stroke. Notably, the accumulation of plaque within the carotid artery is a primary precursor to stenosis, a condition closely associated with the onset of strokes. In response to this pressing health concern, we present the development of a state-of-the-art system engineered system to acquire ultrasound images and precisely delineate the carotid artery intima-media using artificial intelligence. Furthermore, our innovative system provides a graded assessment of stenosis risk within the carotid artery, offering timely notifications to both patients and medical practitioners. Operating on the Raspberry Pi platform, our solution incorporates a user-friendly display and communication interface, capable of promptly alerting patients and doctors to any impending stenosis-related risks.